Many implantable medical devices process data and/or communicate with external circuitry, such as consumer devices. The external circuitry may be used to provide data to the patient or to a medical caregiver, such as for reporting diagnostics, activating care, adjusting care, and/or other purposes. Processing data and/or communicating data with external circuitry may result in battery depletion and/or other problems with the implanted medical device.
In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific examples in which the disclosure may be practiced. It is to be understood that other examples may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense. It is to be understood that features of the various examples described herein may be combined, in part or whole, with each other, unless specifically noted otherwise.
At least some examples of the present disclosure are directed to methods involving identifying a care usage pattern of an implantable medical device (IMD) for a patient associated with the IMD and setting a data event parameter for the IMD based on the care usage pattern.
At least some examples of the present disclosure are directed to devices, systems, and/or methods for controlling a data event of an IMD system, including an IMD implanted within a patient, by setting a data event parameter based on an identified care usage pattern of the IMD. The care usage pattern may comprise at least one cycle of expected care, predicted care (e.g., based on past observation of the patient) and/or currently occurring care provided to the patient by the IMD, herein generally referred to as “care cycles”. In some examples, the care usage pattern may comprise a plurality of different care cycles which are expected or predicted to occur based on different sets of factors. Example factors include an activity pattern of the patient or representative patients, a day of week (e.g., weekday verses a weekend), a time of year, a time of day, among other example factors. In some examples, the IMD may update the care usage pattern, and the associated care cycles, over time based on sensed data.
In at least some examples, the data event parameter comprises a polling interval for data communications and/or a time window for performing data processing operations by the IMD. By setting, and optionally adjusting, the data event parameter based on the care usage pattern, the IMD may optimize energy consumption performance, battery delivery capacity, and/or communication latency. In at least some examples, a polling interval, as used herein, comprises and/or refers to a period of time between successive instances of activation of telemetry circuitry of the IMD to detect a communication signal received from external circuitry. In at least some examples, the time window for performing data processing operations comprises and/or refers to a time of day the IMD initiates and/or performs the data processing operation and/or a length of time the data processing operation may occur.
In some examples, the IMD may adjust a polling interval with respect to different times of the day to balance energy consumption (or power performance) and/or battery delivery capacity with communication latency. In some examples, the care usage pattern may be used to predict when the IMD is performing care and when the patient may communicate with the IMD via external circuitry, and based on the predictions, set configurable and adjustable polling intervals to balance energy consumption and/or power performance with communication latency.
In some examples, the IMD may set and/or adjust at least one time window for performing time-insensitive data processing operations to optimize battery delivery capacity. For example, the care usage pattern may be used to predict when the IMD may exhibit a battery parameter that is below or above a threshold level, such as a battery power delivery capacity of 2.5 volts (V). The battery parameter may be impacted by the IMD providing care, as further described herein. As such, the prediction of the value of the battery parameter may comprise a prediction of care being provided by the IMD. Based on the prediction(s), the IMD may set and/or adjust at least one time window to occur for performing time-insensitive data processing operations, such as setting the time window during a time of the day that the value of the battery parameter is predicted to be within (e.g., above or below) the threshold level, such as a power threshold.
In some examples, the devices, systems, and methods of the present disclosure are configured and used for sleep disordered breathing (SDB) care, such as obstructive sleep apnea (OSA) care, which may comprise monitoring, diagnosis, and/or stimulation therapy. However, in other examples, the system is used for other types of care and/or therapy, including, but not limited to, other types of neurostimulation or cardiac care or therapy. In some examples, such other implementations include therapies, such as but not limited to, central sleep apnea, multiple-type sleep apnea, cardiac disorders, pain management, seizures, deep brain stimulation, respiratory disorders, and various combinations thereof.
It will be further understood that in some instances, a data model may be used to identify some of the internally sensed inputs and/or some of the ways in which the internally sensed inputs may be used to set the data event parameter. Non-data model techniques may be used with (or without) the data model techniques to determine the desired internally sensed inputs.
Accordingly, it will be further understood that aspects of the various example methods involving non-data models and those involving data models may be selectively mixed and matched with each other as desired to achieve the desired and/or effective manner of identifying the care usage pattern of the IMD and/or setting a data event parameter based on the care usage pattern.
These examples, and additional examples, are described in association with at least
The data event parameter may be associated with a data event performed by the IMD, such as a data communication and/or data processing. In some examples, setting the data event parameter may comprise setting a polling interval for data communication by the IMD and/or setting a time window for performing data processing by the IMD. In at least some examples, as used herein, a data event comprises and/or refers to an activity implemented by or using an IMD, such as a listening event, data communication, and/or data processing. In some examples, a data event may occur over a period of time. In some examples, a data event may be a singular event. In some examples, a data event may be a series of multiple events.
Various examples herein refer to different types of “time”, such as time of day, length of time, and time window. In some examples, as used herein, a time of day comprises and/or refers to a time as indicated by a clock (e.g., 4 pm), such as a time to start or stop the data event or data event parameter. In some examples, as used herein, a length of time comprises and/or refers to a duration, such as a duration for setting the polling interval for performing data processing operations (e.g., one hour, two hours, twenty minutes). In some instances, a length of time is herein interchangeably referred to as a period of time or a duration. In some examples, a time window may comprise and/or refer to both the time of day and the length of time (e.g., from 7 am to 8 am).
In some examples, the IMD may communicate data with various external circuitry for monitoring care and patient control. In some instances, the communication may be accomplished using specialized communication schemes, such as Medical Implant Communication Service (MICS). In some examples, the IMD may communicate with a consumer device, such as a smartphone, using standard communication protocols. Example communication protocols include Bluetooth, Bluetooth Low Energy (BLE), ZigBee, Z-wave, Long-Term Evolution (LTE), among other types of standard communication protocols. Robust and low latency communication over several meters may be power intensive due to the energy for the data communication as well as the energy for the IMD to listen for an external device to initiate a communication session. In some examples, a listening event may comprise an event during which the IMD powers the telemetry circuitry, listens for a communication, and then acts upon the communication or shuts the telemetry circuitry down. In some examples, the interval between listening events is herein referred to as the polling interval. Adjusting the polling interval with respect to particular times of the day may be used to balance communication latency (e.g., the speed at which the IMD responds to a request for data communication from an external device) and energy consumption and/or power performance.
However, examples are not limited to setting a polling interval based on the care usage pattern. In some examples, the data event parameter setting may be a time window for performing data processing. The data to be processed may be queued for a period of time, such as hours and/or days, and then processed during a period of time during which lower power demand occurs. For example, the IMD may immediately (or within a threshold period of time) process time-sensitive data operations and perform time-insensitive data operations in batches during the time period of lower power demand in order to balance and/or improve energy consumption and/or power management and/or ensure that battery power capacity is not exceeded during time-sensitive operations of the IMD, as further described herein.
The following provides illustrative and non-limiting examples of setting data event parameters. As an example, at the start of the time window associated with a data event parameter comprising a first polling interval of ten seconds, the IMD may transition to or execute a ten second polling interval (e.g., every ten seconds, the IMD initiates a listening event). At the end of the time window, the IMD may transition to a second polling interval that is greater than the first polling interval (e.g., ten minutes). As another example, at the start of the time window associated with a data event parameter comprising a time window for performing data processing, the IMD may begin processing batched data. At the end of the time window, if the data processing is not complete, the IMD may stop processing until another data processing time window is reached.
In some examples, the data event parameter may be adjusted based on the care usage pattern. In at least some examples, the care usage pattern, as used herein, is a pattern indicative of care expected to, predicted to (e.g., based on observed data), and/or currently being provided by the IMD to the patient, which may be associated with different times of the day. The method 10 may further comprise configuring the IMD to include the care usage pattern, such as storing the care usage pattern on memory of the IMD.
As described above, the care usage pattern may comprise at least one care cycle. In some examples, as further described herein, the care usage pattern may comprise expected care cycles of the IMD, observed care cycles of the IMD and/or a current care cycle of the IMD. In some examples, as used herein, expected care cycles of the IMD comprise and/or refer to predicted care usage time(s) and/or amount of care provided to the patient by the IMD based on data from external data sources. The expected care cycles may be based on literature data, input from a medical caregiver, demographic data associated with the patient and/or a representative plurality of patients, and input from the patient, among other data which may be used to predict when care is to be provided to the patient. In some examples, observed care cycles of the IMD comprise and/or refer to predicted care usage time(s) and/or amount of care provided to the patent by the IMD based on internally obtained data and/or observed care cycles of the IMD. The observed care cycle may be based on data sensed by the IMD and/or otherwise internal to the IMD, such as physiological data and/or care usage data. In some examples, a current care cycle of the IMD comprises and/or refers to presently or a real-time care event of the IMD, which may be based on physiological data sensed by the IMD and/or an implantable sensor in communication with the IMD.
In some examples, the IMD may identify which care cycle to use based on a set of factors, which may be internally sensed and/or input from external sources. Example factors include activity pattern of the patient or representative patients, day of the week, time of year, time of day, among other factors. For example, the activity pattern of the patient may comprise or be indicative of an amount or type of movement (e.g., did the patient exercise or not, at work all day, etc.) which may impact care provided by the IMD. Other example factors may include a sleep pattern, dietary intake, pharmaceutical medications, and weather, among others. However, examples are not so limited, and in some examples, the care usage pattern may comprise a single care cycle. In some examples, the IMD may determine or identify at least a portion of the set of factors based on data sensed by the IMD, as further described herein.
In some examples, the care usage pattern may comprise different combinations of the expected care cycles of the IMD, the observed care cycles of the IMD, and the current care cycle of the IMD. For example, the IMD may be configured to set the data event parameter based on the expected care cycles initially, and may transition to the observed care cycles over time and/or to the current care cycle in response to real-time data which may override the data event parameter setting based on the expected or observed care cycles. In some examples, a care usage pattern may comprise combinations of expected care cycles and observed care cycles, which are each associated with a different set of factors.
As further described herein, the method 10 may include a number of additional steps and/or variations, such as performing the data event based on the data event parameter setting. Performing the data event based on the data event parameter setting may optimize energy consumption by the IMD and/or optimize battery delivery capacity. In some examples, the data event parameter setting may be used to balance or optimize communication latency and energy consumption.
In general terms, the IMD 22 is configured for implantation into a patient, and is configured to provide and/or assist in providing care to the patient. The at least one implantable sensor 25 may assume various forms, and is generally configured for implantation into the patient and to sense at least one of physiological data and care usage data, as further described herein. In some examples, the at least one implantable sensor 25 includes a sensor component in the form of or akin to a motion-based transducer. The motion-based transducer sensor component of the at least one implantable sensor 25 may be or includes an acceleration sensor such as an accelerometer (e.g., a multi-axis accelerometer such as a three-axis or six-axis accelerometer), a gyroscope, etc. In some examples, the at least one implantable sensor 25 includes more than one sensor, such as an acceleration sensor and non-acceleration sensor circuitry. The at least one implantable sensor 25 may be carried by the IMD 22, may be connected to the IMD 22, or may be a standalone component not physically connected to the IMD 22, as further described herein.
The care usage engine 27 is programmed to perform one or more operations as described below and based on a care usage pattern. In some examples, the care usage engine 27 identifies a care usage pattern (e.g., selects a care cycle) and sets a data event parameter based on the identified care usage pattern. In some examples, the care usage engine 27 may identify the care usage pattern based on a set of factors, as described above. In some examples, the care usage engine 27 identifies the care usage pattern based on data sensed via the at least one implantable sensor 25 (e.g., based on an output of the at least one implantable sensor 25 which is input to the care usage engine 27). In some examples, the care usage engine 27 receives the care usage pattern (e.g., expected care cycles) and/or data from the at least one implantable sensor 25 and is programmed (or is connected to a separate engine that is programmed) to set the data event parameter based, at least in part, on the input care usage pattern and/or data from the at least one implantable sensor 25.
In some examples, the care usage engine 27 is programmed (or is connected to a separate engine that is programmed) to affect (or not effect) one or more features or the like relating to operation of the IMD system 20 in response to setting the data event parameter. The care usage engine 27 may reside partially or entirely with the IMD 22, partially or entirely with the external device 26, or partially or entirely with a separate device or component (e.g., the cloud, etc.). Where provided, the external device 26 may wirelessly communicate with the IMD 22, and is operable to facilitate performance of one or more operations as described below. For example, the external device 26 may be used to initially program the IMD 22, and the IMD 22 then processes information and delivers care independent of the external device 26.
In some examples, the external device 26 may be omitted. In some such examples, the IMD 22, the at least one implantable sensor 25 and the care usage engine 27 perform one or more of the operations described below without the need for the external device 26 or human input. The care usage engine 27 may be further programmed to provide information to the patient and/or caregiver, such as processed data or other information of possible interest implicated by information from the at least one implantable sensor 25. In some examples, the care usage engine 27 may provide information indicating the data event parameter to another engine of the IMD 22 that is programmed to execute the data event based on the data event parameter.
The care usage engine 27 (or the logic akin to the care usage engine 27) may be incorporated into a distinct engine or engine programmed to perform certain tasks. For example, the logic of the care usage engine 27 as described below may be part of a care engine and utilized in controlling care provided to the patient, such as but not limited to stimulation therapy delivered to the patient. Logic embodied by the care usage engine 27 may identify or detect a care usage pattern of the IMD 22 in various manners. In some examples, the care usage pattern may be recognized by a function that references expected care cycles and/or data sensed by the at least one implantable sensor 25. As an example, if the data from the at least one implantable sensor 25 includes a pattern of a particular expected care cycles, then the expected care cycle is identified. In some examples, the care usage pattern may be recognized with reference to data from the at least one implantable sensor 25, data from external data sources, and/or a data model (e.g., modeling or artificial intelligence or artificial learning). For example, one or more data sources (including data from the at least one implantable sensor 25) may be employed in a probabilistic decision model to recognize or identify cycles of expected and/or observed care cycles and a currently occurring care cycle.
With these and related examples, the care usage engine 27 is programmed to evaluate the probability of care being provided at different times of the day, and deem or decide when care is expected, observed, and/or is currently occurring (and when not). As an example, care expected or observed to be occurring may be recognized in response to a likelihood of occurrence being greater than a threshold, such as 80 percent or greater. Determining a probability may include weighting different factors and summing the weights to determine the probability. The factors may comprise, but are not limited to, historical data sensed by the at least one implantable sensor 25, as well as patterns identified within the sensed data, and/or other inputs, such as a time of day, day of the week, etc. In some examples, the factors may comprise at least some of the set of factors, as previously described in connection with
While the previously described arrangements comprise a probability associated with care being provided by the IMD, examples are not so limited. In some examples, the probability may additionally or alternatively be associated with predicting when data communications may occur and/or associated with a battery parameter of the IMD.
In some examples, the IMD 22 may comprise an implantable pulse generator (IPG), such as for managing sensing and/or stimulation therapy, as later described in association with at least
In some examples, the power source 64 includes a battery, such as a rechargeable battery or a primary battery. A rechargeable battery, sometimes referred to as a rechargeable cell or secondary cell, includes and/or refers to an electrical battery which can be charged, discharged to a load (e.g., the IMD 51), and recharged, which may be repeated a number of times. A primary battery, sometimes referred to as a primary cell, includes and/or refers to an electrical battery that is discharged to a load and then may be discarded. A primary battery may not be reused once discharged.
In some examples, the stimulation lead 55 includes a lead body 80 with a distally located stimulation electrode 82. At an opposite end of the lead body 80, the stimulation lead 55 includes a proximally located plug-in connector 84 which is configured to be removably connectable to the interface block 66. For example, the interface block 66 may include or provide a stimulation port sized and shaped to receive the plug-in connector 84.
In general terms, the stimulation electrode 82 may optionally be a cuff electrode, and may include some non-conductive structures biased to (or otherwise configurable to) releasably secure the stimulation electrode 82 about a target nerve. Other formats are also acceptable. Moreover, the stimulation electrode 82 may include an array of contact electrodes to deliver a stimulation signal to a target nerve. In some non-limiting examples, the stimulation electrode 82 may comprise at least some of substantially the same features and attributes as described within at least: U.S. Pat. No. 8,340,785, issued Dec. 25, 2012, and entitled “SELF EXPANDING ELECTRODE CUFF”; U.S. Pat. No. 9,227,053, issued Jan. 5, 2016, and entitled “SELF EXPANDING ELECTRODE CUFF”; U.S. Pat. No. 8,934,992, issued Jan. 13, 2015, and entitled “NERVE CUFF”; and/or U.S. Patent Publication No. 2020/0230412, published on Jul. 23, 2020, and entitled “CUFF ELECTRODE”, the entire teachings of each of which are incorporated herein by reference in their entireties.
In some examples, the lead body 80 is a generally flexible elongate member having sufficient resilience to enable advancing and maneuvering the lead body 80 subcutaneously to place the stimulation electrode 82 at a desired location adjacent a nerve, such as an airway-patency-related nerve (e.g., hypoglossal nerve, phrenic nerve, ansa cervicalis nerve, etc.). In some examples, such as in the case of obstructive sleep apnea, the nerves may include (but are not limited to) the nerve and associated muscles responsible for causing movement of the tongue and related musculature to maintain or restore airway patency. In some examples, the nerves may include (but are not limited to) the hypoglossal nerve and the muscles may include (but are not limited to) the genioglossus muscle. In some examples, lead body 80 may have a length sufficient to extend from the IPG assembly 63 implanted in one body location (e.g., pectoral) and to the target stimulation location (e.g., head, neck). Upon generation via the circuitry 62, a stimulation signal is selectively transmitted to the interface block 66 for delivery via the stimulation lead 55 to such nerves.
The at least one implantable sensor 25 may be connected to the IMD 51 in various fashions. For example, the at least one implantable sensor 25 may include a lead body carrying the motion-based transducer sensor element of an acceleration sensor at a distal end, and a plug-in connector at proximal end. The plug-in connector may be connected to the interface block 66, such as the interface block 66 including or providing a sense port sized and shaped to receive the plug-in connector of the at least one implantable sensor 25, and the lead body extended from the IPG assembly 63 to locate the sensor element at a desired anatomical location. Alternatively, the at least one implantable sensor 25 may be physically coupled to the interface block 66, and thus carried by the IPG assembly 63. In some such examples, the at least one implantable sensor 25 may be considered a component of the IMD 51. In some examples, the physical coupling of the at least one implantable sensor 25 relative to the IPG assembly 63 is performed prior to implantation of those components.
In some examples, the at least one implantable sensor 25 (and in particular, at least the motion-based transducer sensor component as described above) may be incorporated into a structure of the interface block 66, into a structure of the housing 60, and/or into a structure of the stimulation lead 55. With these and similar configurations, the sensor component of the at least one implantable sensor 25 is electronically connected to the circuitry 62 within the housing 60 or other enclosure of the IPG assembly 63. More specifically, the at least one implantable sensor 25 may be connected in various orientations as described within U.S. patent application Ser. No. 16/978,275, filed on Sep. 4, 2020, and entitled “SYSTEMS AND METHODS FOR OPERATING AN IMPLANTABLE MEDICAL DEVICE BASED UPON SENSED POSTURE INFORMATION”, the entire teachings of which is incorporated herein by reference in its entirety. Although the above examples describe an IMD 51 having a stimulation lead 55, examples are not so limited and example IMDs may additionally or alternatively include a lead used for sensing, such as a lead used to sense physiological or other data.
In some examples, the at least one implantable sensor 25 may be wirelessly connected to the IMD 51. In some such examples, the interface block 66 need not provide a sense port for the at least one implantable sensor 25 or the sense port may be used for a second sensor. In some examples, the circuitry 62 of the IPG assembly 63 and circuitry of the at least one implantable sensor 25 communicate via a wireless communication pathway according to known wireless protocols, such as Bluetooth, near-field communication (NFC), MICS, 802.11, etc. with each of the circuitry 62 and the at least one implantable sensor 25 including corresponding components for implementing the wireless communication pathway. In some examples, a similar wireless pathway is implemented to communicate with devices external to the patient's body for at least partially controlling the at least one implantable sensor 25 and/or the IPG assembly 63, to communicate with other circuitry (e.g., other sensors or devices) internally within the patient's body, or to communicate with other sensors external to the patient's body.
As further shown in association with at least
The acceleration sensor 110 may comprise an accelerometer (e.g., a single axis or multi-axis accelerometer), a gyroscope, a pressure sensor, etc. The acceleration sensor 110 may provide information along a single axis, or along multiples axes (e.g., three-axis accelerometer, three-axis gyroscope, six-axis accelerometer, nine-axis accelerometer, etc. In some examples, an acceleration sensor 110 that provides information along multiple axes may provide information along multiple linear, rotational, and/or magnetic axes, such as three-rotational axes (e.g., a three-axis accelerometer). In some examples, a six-axis acceleration sensor may provide information along three linear axes and three rotational axes. In some examples, a nine-axis acceleration sensor may provide information along three linear axes, three rotational axes, and three magnetic axes. Regardless of an exact form, the sensor component of the acceleration sensor 110 is capable of sensing, amongst other things, information indicative of body motion of the patient, a posture of the patient, and other vibrations. As a point of reference, while information generated by the acceleration sensor 110 is signaled to and acted upon by the IMD 100 (such as by a care usage engine 27 of an IMD 22 of
The following provides some examples of sensing information indicative of body motion, posture, and vibrations by the acceleration sensor 110, however examples are not so limited and the acceleration sensor 110 may sense body motion, posture, and vibration using a variety of techniques. The acceleration sensor 110 may be used to generate data via sensing of forces in multiple directions or axes. In some examples, the acceleration sensor 110 is a three-axis accelerometer that may sense or measure the static and/or dynamic forces of acceleration on three axes. Static forces include the constant force of gravity. By measuring the amount of static acceleration due to gravity, an accelerometer may be used to identify the angle it is tilted at with respect to the earth. By sensing the amount of dynamic acceleration, the accelerometer may find out how fast and in what direction the IMD is moving, which may be indicative of body movement and, in some examples, indicative of an activity pattern of the patient. Single- and multi-axis models of accelerometers detect magnitude and direction of acceleration (or proper acceleration) as a vector quantity. With these and similar types of sensor constructions, an output from the acceleration sensor 110 may include vector quantities in one, two or three axes.
In some examples, some methods of the present disclosure may include at least some of substantially the same features and attributes as determining or designating a posture of the patient based on data from the acceleration sensor 110 described within U.S. patent application Ser. No. 16/978,275, filed Sep. 4, 2020, and entitled “SYSTEMS AND METHODS FOR OPERATING AN IMPLANTABLE MEDICAL DEVICE BASED UPON SENSED POSTURE INFORMATION”, the entire teachings of which is incorporated herein by reference in its entirety.
As described above, sensing the amount of dynamic acceleration may be used to identify body motion and posture. Example body motions include movement in a vector or a direction (e.g., walking, running, biking), rotational motions (e.g., twisting), and changes in posture (e.g., change from an upright position to a sitting or supine position), among other movements. The motion may be sensed relative to a gravity vector, such as an earth gravity vector and/or a vertical baseline gravity vector. In some examples, the sensed force(s) may be processed to determine a posture of the patient. As used herein, posture refers to or includes a position or bearing of the body. In some instances, the term “posture” may sometimes be referred to as “body position”. Example postures include upright or standing position, supine position or another generally horizontal body position (e.g., prone, lateral decubitis), a generally supine reclined position, sitting position, etc. Further detail on examples of identifying or determining motion and posture are described below in connection with the example care usage engine 27 of an IMD and sub-engines illustrated in association with at least
In some examples, the acceleration sensor 110 may be used to sense physiological data. The physiological data may include physiological parameters, such as cardiac signals and/or respiration information. In some examples, the respiration information may be determined based on rotational movements of a portion of a chest wall of the patient during breathing. For example, the acceleration sensor 110 may be used to determine respiration information based on rotational movements of a chest wall of the patient as described within U.S. patent application Ser. No. 16/977,664, filed on Sep. 2, 2020, and entitled “RESPIRATION DETECTION”, the entire teachings of which is incorporated herein by reference in its entirety.
The other circuitry 112 may set the data event parameter based on the care usage pattern and perform a data event based on the data event parameter, as described by
As shown in association with at least
In some examples, sensor type 130 comprises the modalities of pressure 144, impedance 135, acceleration 143, airflow 136, radio frequency (RF) 138, optical 132, electromyography (EMG) 139, electrocardiography (ECG) 140, ultrasonic 133, acoustic 141, image 137, internal electronics 142 and/or other 134. In some examples, sensor type 130 comprises a combination of at least some of the various sensor modalities 131-144.
It will be understood that, depending upon the attribute being sensed, in some instances a given sensor modality identified within
In some examples, a pressure sensor 144 may sense pressure associated with respiration and may be implemented as an external sensor and/or an implantable sensor. In some instances, such pressures may include an extrapleural pressure, intrapleural pressures, etc. For example, one pressure sensor 144 may comprise an implantable respiratory sensor, such as that disclosed in U.S. Patent Publication No. 2011/0152706, published on Jun. 23, 2011, entitled “METHOD AND APPARATUS FOR SENSING RESPIRATORY PRESSURE IN AN IMPLANTABLE STIMULATION SYSTEM”, the entire teachings of which is incorporated herein by reference in its entirety.
In some examples, a pressure sensor 144 may sense sound and/or pressure waves at a different frequency than occur for respiration (e.g., inspiration, exhalation, etc.). In some instances, this data may be used to track cardiac parameters of patients via a respiratory rate and/or a heart rate. In some instances, such data may be used to approximate electrocardiogram information, such as a QRS complex. In some instances, the detected heart rate is used to identify a relative degree of organized heart rate variability, in which organized heart rate variability may enable detecting apneas or other sleep disordered breathing events, which may enable evaluating efficacy of sleep disordered breathing.
In some examples, pressure sensor 144 comprises piezoelectric element(s) and may be used to detect sleep disordered breathing (SDB) events (e.g., apnea-hypopnea events), to detect onset of inspiration, and/or detection of an inspiratory rate, etc. However, examples are not so limited and may comprise of variety of different types of IMDs.
As shown in
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In some instances, the sensors are positioned about a chest region to measure a trans-thoracic bio-impedance to produce at least a respiratory waveform.
In some instances, at least one sensor involved in measuring bio-impedance may form part of a pulse generator, whether implantable or external. In some instances, at least one sensor involved in measuring bio-impedance may form part of a stimulation element and/or stimulation circuitry. In some instances, at least one sensor forms part of a lead extending between a pulse generator and a stimulation element.
In some examples, impedance sensor 135 is implemented via a pair of elements on opposite sides of an upper airway. Some example implementations of such an arrangement are further described herein.
In some examples, impedance sensor 135 may take the form of electrical components not used in an IMD. For instance, some patients may already have a cardiac care device (e.g., pacemaker, defibrillator, etc.) implanted within their bodies, and therefore have some cardiac leads implanted within their body. Accordingly, the cardiac leads may function together or in cooperation with other resistive/electrical elements to provide impedance sensing.
In some examples, whether internal and/or external, impedance sensor(s) 135 may be used to sense an ECG signal.
In some examples, impedance sensor 135 is used to detect SDB events (e.g., apnea-hypopnea events), to detect onset of inspiration, and/or detection of an inspiratory rate, etc.
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In some examples, acceleration sensor 143 enables acoustic detection of cardiac information, such as heart rate via motion of tissue in the head/neck region, similar to ballistocardiogram and/or seismocardiogram techniques. In some examples, measuring the heart rate includes sensing heart rate variability. In some examples, acceleration sensor 143 may sense respiratory information, such as but not limited to, a respiratory rate. In some examples, whether sensed via an acceleration sensor 143 alone or in conjunction with other sensors, one may track cardiac information and respiratory information simultaneously by exploiting the behavior of the cardiac signal in which a cardiac waveform may vary with respiration.
In some examples, acceleration sensor 143 is used to detect SDB events (e.g., apnea-hypopnea events), to detect onset of inspiration, and/or detection of an inspiratory rate, etc. In some examples, the acceleration sensor 143 may be used to detect SDB events during the sleep period and/or may be used continuously to detect arrhythmias. In some examples, the acceleration sensor 143, detection of cardiac information, and/or detection of SDB events may be implemented as described within U.S. Patent Publication No. 2019/0160282, published on May 30, 2019, entitled “ACCELEROMETER-BASED SENSING FOR SLEEP DISORDED BREATHING (SDB) CARE”, and/or U.S. patent application Ser. No. 16/977,664 filed on Sep. 2, 2020, and entitled “RESPIRATION DETECTION”, the entire teachings of which are each incorporated herein by reference in their entirety.
In some examples, RF sensor 138 shown in
In some examples, one sensor modality may comprise an optical sensor 132 as shown in
As shown in
In some instances, the EMG sensor 139 may comprise a surface EMG sensor while, in some instances, the EMG sensor 139 may comprise an intramuscular sensor. In some instances, at least a portion of the EMG sensor 139 is implantable within the patient's body and therefore remains available for performing electromyography on a long term basis.
In some examples, one sensor modality may comprise ECG sensor 140 which produces an ECG signal. In some instances, the ECG sensor 140 comprises a plurality of electrodes distributable about a chest region of the patient and from which the ECG signal is obtainable. In some instances, a dedicated ECG sensor(s) 140 is not employed, but other sensors such as an array of impedance sensors 135 (e.g., bio-impedance sensors) are employed to obtain an ECG signal. In some instances, a dedicated ECG sensor(s) is not employed but ECG information is derived from a respiratory waveform, which may be obtained via any one or several of the sensor modalities in sensor type 130 in
In some examples, an ECG signal obtained via ECG sensor 140 may be combined with respiratory sensing (via pressure sensor 144 or impedance sensor 135) to determine minute ventilation, as well as a rate and phase of respiration. In some examples, the ECG sensor 140 may be exploited to obtain respiratory information.
In some examples, ECG sensor 140 is used to detect SDB events (e.g., apnea-hypopnea events), to detect onset of inspiration, and/or detection of an inspiratory rate, etc.
As shown in
In some examples, acoustic sensor 141 comprises piezoelectric element(s), which sense acoustic vibration. In some implementations, such acoustic vibratory sensing may be used to detect sounds associated with SDB events (e.g., apnea-hypopnea events), to detect onset of inspiration, and/or detection of an inspiratory rate, etc.
In some examples, data via sensor types 130 in
As may be appreciated, examples are not limited to the implantable sensors and/or combinations as illustrated in associated with at least
In some examples of the present disclosure, an IMD and/or IMD system may include multiple implantable sensors. In some examples, one or more of the implantable sensors may be separate from the respective IMD.
As shown at 201 in
In some examples, as shown at 207 in
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In some examples, as shown at 354 in
In some examples, as shown at 358 in
In some examples, as shown at 360 in
In some examples, as shown at 370 in
In some examples, as shown at 374 in
In some examples, the method comprises identifying the care usage pattern based on the sleep-wake status of the patient. In some examples, the method further comprises determining the sleep-wake status based on at least one of: i) the time of day, ii) day of week, iii) a lack of sensed body motion for a predetermined period of time, iv) demographic data indicative of sleep schedules for representative patients, v) input data indicative of a sleep schedule for the patient and/or vi) physiological data. In some examples, the method comprises determining the sleep-wake status by detecting sleep upon a time of day and a lack of sensed body motion for a predetermined period of time, as shown at 376 in
As described above, in some examples, a sleep-wake status is determined based on respiration data. In some such examples, the method may comprise determining the respiration data based on sensing motion of a chest wall, and determining the sleep-wake status by differentiating, based on an amplitude of the sensed chest wall motion, between active respiration indicative of an awake state and passive respiration indicative of a sleep state.
In some examples, the sleep-wake status is determined by sensing variability in at least one of the respiratory rate, the heart rate, and body motion. In some such examples, performing the determination of the sleep-wake status may be based on the variability in at least one of the respective sensed respiratory rate, the heart rate, and the body motion. For example, the method may further comprise determining the sleep-wake status based on at least one of determining, from the cardiac data, a heart rate variability (HRV) and the heart rate.
In some examples, methods (e.g., 10) as illustrated in association with at least
In some examples, the expected care cycles and/or observed care cycles may be temporarily overridden by a current care cycle. For example, the IMD may provide care at a time that conflicts with the particular expected care cycle and/or observed care cycle. Based on the current care being provided, the data event parameter may be adjusted until care discontinues and/or in response to the care discontinuing. The current care cycle, if occurs repetitively, may be used to adjust the observed care cycles.
The care usage engine 27 may include a movement sub-engine 215 used to determine body motion data 220 and posture data 230. As previously described at least in connection with
The movement sub-engine 215 may determine body motion data 220 of the patient, such as determining whether the patient is active or at rest. In some examples, when a vector magnitude of the acceleration measured via the acceleration sensor meets or exceeds a threshold (optionally for a period of time), the measurement may indicate the presence of non-gravitational components indicative of body movement. In some examples, the threshold is about 1.15G. Conversely, measurements of acceleration of about 1G (corresponding to the presence of the gravitational components only) may be indicative of rest.
The movement sub-engine 215 may determine posture data 230, including the type of posture 232, by determining whether at least an upper body portion (e.g., torso, head/neck) of the patient is in a generally vertical position (e.g., upright position) or lying down. In some examples, a generally vertical position may comprise standing or sitting.
In some examples, the movement sub-engine 215 determines the posture data 230 by rejecting non-posture components from an acceleration sensor via low pass filtering relative to each axis of the multiple axes of the acceleration sensor. In some examples, posture is at least partially determined via detecting a gravity vector from the filtered axes.
In some examples, if the measured angle is greater than a threshold (e.g., 40) degrees, then the measured angle indicates that the patient is lying down. In some such examples, a posture classification implemented by the movement sub-engine 215 includes classifying sub-postures, such as whether the patient is in a supine position, a prone position, or in a lateral decubitus position. In some non-limiting examples, after confirming a likely position of lying down, the movement sub-engine 215 determines if the patient is in a supine position or a prone position. However, examples are not so limited and the patient position or posture may be determined using other techniques, such as use of a dot product of the vectors.
In some examples, the movement sub-engine 215 is programmed to distinguish between a supine sleep position and a generally supine reclined position. As a point of reference, a generally supine reclined position may be one in which the patient is on a recliner, on an adjustable-type bed, laying on a couch, or the like and not attempting to sleep (e.g., watching television) as compared to sleeping in bed. An absolute vertical distance between the head and torso of the patient in the supine sleep position is less than the absolute vertical distance between the head and torso in the generally supine reclined position.
In some examples, the movement sub-engine 215 is programmed to consider or characterize a position of the patient's neck and/or head and/or body position (e.g., as part of a determination of the patient's rotational position while lying down). For example, the movement sub-engine 215 is programmed to estimate a position of the patient's neck based on body position. A determination that the patient's torso is slightly offset may imply different head positions. In some non-limiting examples, the systems and methods of the present disclosure may consider or characterize a position of the patient's neck and/or head via information from a sensor provided with a microstimulator that is implanted in the patient's neck or in conjunction with a sensor integrated into the stimulation lead. In some examples, two (or more) acceleration sensors may be provided, each implanted in a different region of the patient's body (e.g., torso, head, neck) and providing information to the movement sub-engine 215 sufficient to estimate neck and/or head and/or body positions of the patient.
The above explanations provide a few non-limiting examples of some posture determination or designation protocols implemented by the movement sub-engine 215. However, examples are not so limited and a number of other posture determination or designation techniques are also envisioned by the present disclosure, and may be function of the format of the implantable sensor and/or other information provided by one or more additional sensors. Various body postures and sub-postures may be determined or designated as implemented and described within U.S. patent application Ser. No. 16/978,275, filed Sep. 4, 2020, and entitled “SYSTEMS AND METHODS FOR OPERATING AN IMPLANTABLE MEDICAL DEVICE BASED UPON SENSED POSTURE INFORMATION”, the entire teachings of which is incorporated herein by reference in its entirety.
Some systems and methods of the present disclosure may comprise calibrating data sensed to compensate, account, or address the possibility that a position of the at least one implantable sensor (from which posture determinations may be made) within the patient's body is unknown and/or has changed over time. In some examples, the calibration may be based on establishing a horizontal baseline gravity plane, establishing or creating a vertical baseline gravity vector and a horizontal baseline gravity plane, and/or receiving a predetermined vertical baseline gravity vector and one or more predetermined horizontal baseline gravity vectors, based upon respiratory and/or cardiac waveform polarity information provided by or derived from the implantable sensor, among other variations as described within U.S. patent application Ser. No. 16/978,275, filed Sep. 4, 2020, and entitled “SYSTEMS AND METHODS FOR OPERATING AN IMPLANTABLE MEDICAL DEVICE BASED UPON SENSED POSTURE INFORMATION”, the entire teachings of which is incorporated herein by reference in its entirety.
Referring back to
Referring back to
In some examples, the care usage engine 27 may further include other sub-engines, as illustrated by the other inputs sub-engine 285. The other inputs sub-engine 285 may include one or more engines which are used to determine different inputs to the care usage engine 27. The other inputs may include a temporal parameter, such as the time of the day 286, time of the year 287, time zone 288, and/or patterns of activity 289. In some examples, the other inputs may include a subset of the set of factors, as previously described in connection with at least
As described above, in some examples, the data event parameter comprises a polling interval. Setting the polling interval based on a care usage pattern of the IMD may be used to balance communication latency with power performance of the IMD. In some example methods, such as method 400 as shown at 410 in
In some examples, the polling interval is set based on a time of day. In some examples, the polling interval is set based on at least one patient state, such as a sleep-wake state of the patient. In some examples, shown at 413 in
In some examples, as shown at 414 in
In some examples, as shown at 417 in
In some examples, the method 400 of
In some examples, the method 400 may further comprise batching data based on the set polling interval. For example, as shown at 422 in
In some examples, the batched data may be communicated in response to a battery parameter being within a threshold level (e.g., a battery power delivering capacity, such as 2.5V) and based on the data event parameter. In some examples, the battery parameter may include a battery capacity (e.g., how much the battery is being utilized), and if the battery capacity is at or below the threshold level (e.g., battery is not being fully utilized), the batched data may be communicated. In other examples, the battery parameter may include a battery voltage, battery power delivery capacity, available power and/or used power. For example, the method 400 may further comprise identifying that care provided by the IMD is outside a threshold level, and in response, batching the data for subsequent data communication. In some examples, the method 400 may further comprise communicating the batched data to external circuitry in response to the care provided being within the threshold level and based on the set data event parameter.
In some examples, the battery parameter may include or be associated with an amount of power remaining on a battery of the IMD and/or a predicted recharge time, such as with a rechargeable battery. For example, in response to the remaining battery power being below a threshold, it may be predicted that a recharge of the battery is to occur at a particular time (e.g., within an hour) and the batched data may be communicated after the recharge. In some examples, the recharge time may be predicted based on past patterns of recharge, such as the user recharging the battery at specific times and/or days of the week. In response, the data may be batched for subsequent data communication after the recharge. In other examples and/or in addition, in response to the amount of battery power remaining being below another (lower) threshold, an alert message may be communicated to notify the user to recharge the rechargeable battery or to notify that the primary battery is near depleted.
In some examples, the care usage pattern (e.g., expected, observed or current care cycles) may be identified by assessing one or more of: i) a probability of care being provided by the IMD, ii) a probability of communication occurring between the IMD and external circuitry, and iii) a probability of a battery parameter of the IMD being above or below a threshold level at a particular time of the day based on a pattern within external data (e.g., time of day and/or input expected care cycles) and/or internally-obtained data (e.g., physiological data, care usage data). Similarly, in some examples, the data event parameter may be set based on the prediction of when communication is likely to occur with external circuitry, the prediction of when care is likely to be provided by the IMD, and/or the prediction of the battery parameter being within the threshold level (e.g., a power threshold). The prediction(s) may include or be associated with a time of day, and the data event parameter is set based on the prediction. In some examples, the prediction(s) may be overruled or overridden by real-time detection of care being provided and/or battery parameter being outside the threshold level.
In some examples, the care usage pattern may include different cycles of care depending on activity of the user during the day (and/or other period of time), time of the day, day of the week, patterns of motion and/or posture, and various other inputs. As an example, a patient that has an activity level above a threshold level for the previous week, which is indicative of exercise, may have a different expected and/or observed care cycle than when they have an activity level below the threshold level for the previous week. As may be appreciated, motion patterns may include an identified lack of motion. As another example, a patient may have a different expected and/or observed care cycle during a work day than during the weekend and/or when on vacation. Such differences in care cycles may be expected initially and/or updated over time based on the observed care provided by the IMD, and which may change over time. As may be appreciated, the above examples are non-limiting and non-exclusive examples.
The methods illustrated by
In some examples, as shown at 434 in
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The methods illustrated by
As a particular example, a polling interval may initially be set based on expected care cycles. For example, communication between the IMD and external circuitry may be expected for a particular patient (and no care may be expected to be provided to the patient by the IMD) at a first time window. The IMD may have a first polling interval setting for the first time window (e.g., 8 am to 9 am) of 5 seconds. Outside the first time window, the IMD may have a second polling interval setting that is above a threshold value, such as 10 minutes. Overtime, observed care cycles of the IMD may indicate that the first time window can be revised (e.g., shortened) to a second time window of 8 am to 8:30 am. For example, communication between the IMD and external circuitry may be observed to occur between 8 am and 8:30 am, and observed to not occur after 8:30 am to 9 am as expected. The IMD may revise the first polling interval setting for the second time window of 8 am to 8:30 am and the second polling interval for times of the day outside the second time window. At 8:20 am on a particular day, care may be provided to the patient by the IMD, indicating a real time care event is occurring during the second time window and when the IMD is using the first polling interval. In response to the care being provided, the IMD may override the first polling interval (e.g., 5 second polling interval) by preventing communication with the IMD and/or preventing a listening event. In some examples, the IMD may additionally and/or alternatively adjust the first polling interval during the second time window to the second polling interval (e.g., to 10 minutes until the care ends). In some examples, the IMD may prevent communication with external circuitry until after the care ends and the IMD may adjust the polling interval for a respective time window after the care ends to the first polling interval. For example, communication between the IMD and external circuitry may be expected after care is provided. After the care stops, the IMD may adjust the polling interval for a third time window (e.g., 20-30 minutes) to the first polling interval. The third time window may overlap with the second time window (e.g., 8:25 am to 9:05 am) or be outside the second time window (e.g., 8:40 am to 9:10 am), in some examples. After the third time window lapses, the IMD may revert back to the second polling interval and/or other polling intervals based on observed care cycles and/or expected care cycles.
In some examples, the IMD may set multiple polling intervals and/or the polling interval may be set for multiple time windows. For example, as shown at 462 in
In some examples, as shown at 464 in
In some examples, as illustrated at 466 in
As shown at 468 in
In some examples, as shown at 482 in
As a specific example, such as in the method 10 of
However, examples are not limited to time windows for polling intervals being set based on a sleep-wake status of the patient. In some examples, setting the data event parameter comprises setting a first polling interval for at least a first time window associated with a time of day after care is provided by the IMD based on the care usage pattern and setting a second polling interval for at least a second time window, the second polling interval being greater than the first polling interval. The second window may be associated with one or more of care being provided by the IMD, the patient being in a sleep state, and a battery parameter of the IMD being above a threshold level. In some examples, the first polling interval may additionally be set for at least one of a third time window and a fourth time window associated with a sleep-wake status of the patient, wherein the third time window is associated with a time of day prior to sleep onset and the fourth time window is associated with onset of an awake state from the sleep state for the patient.
Examples are not limited to the number of polling intervals and/or time windows as illustrated by
As described above, the care usage pattern may be based on probabilities of communicating with external circuitry at particular times of the day and/or probabilities of care being provided at the particular times of the day.
In some examples, as shown at 491 in
At 532 in
Based on the set polling interval, the IMD may have different polling intervals with respect to different time windows of the day. For example, the IMD may have a relaxed (e.g., less often) polling interval during times that external communication is unlikely and/or when care is likely provided by the IMD. At 534 in
The method 530 may be repeated over time and used to generate additional and/or revised observed care cycles. In some examples, the care usage engine 27 in
Some examples, alternatively and/or additionally to setting the polling interval, comprise setting a data event parameter by setting a time window for performing data processing.
In some examples, as shown at 620 in
As shown at 630 in
In some examples, as shown at 650 in
A battery parameter being within an threshold level, as used herein, may include the battery parameter complying with the threshold level, such as being at or above the threshold level or at or below the threshold level, which indicates the battery is not being fully utilized and/or a data event may be performed. A battery parameter being outside the threshold level, as used herein, may include the battery parameter not complying with the threshold level, such as being below or above the threshold level, which indicates that the battery is being fully utilized and/or the data event should not be performed.
In some examples, as shown at 669 in
In some examples, as shown at 671 in
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In some examples, as shown at 678 in
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In some examples, as shown at 685 in
Examples are not limited to the number of time windows and/or time windows as illustrated by
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The method 700 may be repeated over time and used to generate additional and/or revised observed care cycles. In some examples, the care usage engine 27 of
Some example methods, systems and/or devices may involve programming an IMD (e.g., IMD 22 in
With this in mind, the following example implementations in
In some examples, the data model may comprise at least one of the data model types 830 shown in
In some examples, the artificial neural network 804 may estimate a function(s) that depend on inputs. In some such examples, one or more layers of artificial neurons may receive input data and generate output data. The inputs and outputs may comprise the data sensed by the at least one implantable sensor and/or functions related to such data or other functions. Neural networks may comprise networks such as, but not limited to, learning networks (e.g., deep, deep structured, hierarchical, and the like), convolutional, auto-type networks (e.g., auto-encoder, auto-associator), Diablo networks, and neural network models (e.g., feedforward, recurrent).
In some examples, the SVM 806 may utilize a linear classification. This classification may act to separate the data points into classes based on distance of the data points from a hyperplane. In some examples, the hyperplane is arranged to maximize the distances from the hyperplane to the nearest data points on either side of the hyperplane. This arrangement may group points located on opposite sides of the hyperplane into different classes. However, in some examples, the SVM 806 may comprise a nonlinear classification that separates the data points with a hyperplane in a transformed feature space. The transformed feature space may be determined by one or more kernel functions, including nonlinear kernel functions. In some examples, the SVM 806 is a multiclass SVM that separates data points into more than two classes, which may reduce a multiclass problem into multiple binary classification problems.
In some examples, the deep learning 808 may comprise models such as, but not limited to, convolutional networks (e.g., deep belief, neural), belief networks, Boltzmann machines, deep coding networks, stacked auto-encoders, stacking networks (e.g., deep or tensor deep), hierarchical-deep models, deep kernel machines, and the like. It will be understood that such examples may comprise variants and/or combinations of the above-noted example networks.
In some examples, per type 809, the data model may comprise a clustering method(s), which may comprise hierarchical clustering, k-means clustering, density-based clustering, and the like. In some examples, the hierarchical clustering may be used to construct a hierarchy of clusters of sensed data. In some such examples, the hierarchical clustering utilizes a “bottom up” approach (e.g., agglomerative) wherein each data point starts in its own cluster, and pairs of clusters are merged at progressively higher levels of the hierarchy. However, in some examples, the hierarchical clustering utilizes a top-down approach in which all data points start in one cluster, and then clusters are split at progressively lower levels of the hierarchy.
In some examples, the k-means clustering implementation may comprise placing the sensed data into k clusters, where k is an integer equal or greater than two. Via such clustering, each data point belongs to a cluster having a mean that is closer to the data point than any means of the other clusters. However, in some examples, a machine learning model (MLM) may comprise density-based clustering, which may be used to group together data points that are close to one another, while identifying as outliers any data points that are far away from other data points.
In some examples, as represented per “other” type 810 in
In some examples, as represented per “other” type 810 in
In some examples, a MLM may comprise anomaly detection and/or outlier detection that may be used to identify data that does not conform to an expected pattern or are otherwise distinct from other data in a dataset.
In some examples, machine learning model may comprise learning methods that incorporate a plurality of the machine learning methods.
It will be understood that at least some example methods (and/or devices) of the present disclosure may sense physiological data, and set the data event parameter without use of a constructed data model and/or trained data model, such as but not limited to, a machine learning model. Further, the data model may be constructed on a per-patient basis and/or a representative patient basis.
In some examples a method may comprise implementing construction of a data model at least partially via at least one external resource, in communication with the IMD, according to at least some external data. In some such examples, the external data comprises data indicative of data communication between the IMD and external circuitry (such as detected by external telemetry circuitry) and/or data indicative of care being provided to the user. The external data may be time-stamped by the external resource or by the IMD.
In some examples, the known input sources 850 may comprise data indicative of expected or known patterns of sensed data, such as patterns of motion and posture, as well as care provide, and associations between the same, as described above.
In some examples, the physiological parameters 870 may comprise a respiration signal 887, a respiration rate variability signal, a heart rate variability signal 878, in which may be obtained from seismocardiography sensing (SCG) 879, an EEG parameter 871, ECG parameter 873, and/or an EMG parameter 875. Other inputs sources 850 may comprise ballistocardiography sensing (BCG), and/or accelerocardiograph sensing (ACG). In some examples, the SCG, BCG, ACG sensing may be provided via an implanted acceleration sensor or via other types of implantable sensors. In some examples, the physiological parameters 870 may be indicative of care being or about to be provided by the IMD.
In some examples, the motion 862 may be used to obtain at least one of the physiological parameters 870. For example, motion data sensed by an acceleration sensor may be used to determine respiratory information, as further described herein. In some examples, the respiration information is determined by sensing, via the acceleration sensor, rotational movement associated with a respiratory body portion of the patient with the IMD implanted, with the rotational movement being caused by breathing. In some such examples, the respiratory body portion may comprise a chest wall and/or abdominal wall of the patient, and the motion may include chest motion, such as chest wall motion comprising a rotational movement of the chest wall and/or rotational movement of an abdominal wall or portion of the abdomen indicative to respiratory information, and as described within U.S. patent application Ser. No. 16/977,664, filed on Sep. 2, 2020, and entitled “RESPIRATION DETECTION”, the entire teachings of which is incorporated herein by reference in its entirety.
The known input sources 850 may include various external and internal data sources, such as the implantable sensor of the IMD, implantable sensors of other IMDs, external databases which store data from a plurality of IMDs, such as various data for the respective IMD or for a plurality of IMDs. Accordingly, the data model may be constructed for the particular patient (e.g., per-patient basis) or representative number of patients (e.g., representative patient basis). Additionally, the data model may be updated overtime using feedback data from the particular IMD and/or a plurality of IMDs.
The known inputs 901 may comprise physiological data 902 and care usage data 904. Example physiological data 902 may comprise motion and posture data sensed using an acceleration sensor and care usage data 904 may comprise data indicative of a type, an amount, and a duration of care provided. In some examples, the physiological data 902 and/or and care usage data 904 may be time-stamped and/or time data (e.g., time of day, duration of time, etc.) may additional provided as known inputs.
The known outputs 906 may comprise indicators of a care usage pattern 908. For example, the indicators of the care usage pattern 908 may comprise times of the day and durations of care being provided by the IMD, a battery parameter being within and/or outside a threshold level, and/or data communications associated with and/or between the IMD and external circuitry, which indicate when to set the data event parameter. In some examples, the known outputs 906 (e.g., the indicators 908) may comprise data measured externally from the IMD, such as by the at least one external sensor. The at least one external sensor may comprise external telemetry circuitry configured to sense data communications by the IMD and/or an external sensor used to detect an indicator of a disease burden, such as heart rate sensor, pulse oximeter, blood oxygen desaturation, airflow sensor, among other types of sensors. However, examples are not so limited. For example, with observed care cycles, the known outputs 906 may be obtained by the IMD.
As previously described, constructing the data model may comprise training a data model, such as one of the data models in data model types 830 in
In some examples, just one or some of the inputs 921 may be used, while all of the inputs 921 may be used in some examples.
As shown in association with at least
In some examples relating to at least
In some examples, the care usage engine 1106 may be programmed to set or revise the data event parameter based on communication with another engine that controls an operational feature, such as based on care provided by the IMD. For example, as shown in association with at least
In some examples, and as further illustrated by
In some examples, the SDB engine 1110 may detect a disease burden indicator, such as detecting an indicator of sleep apnea. Non-limiting examples of some features implemented by the SDB engine 1110 in accordance with systems and methods of the present disclosure may comprise at least some of substantially the same features and attributes for detecting a disease burden indicator as described within at least: U.S. Provisional Patent Application No. 63/056,241, filed on Jul. 24, 2020, and entitled “DISEASE BURDEN INDICATION”; and U.S. Provisional Patent Application No. 63/089,118, filed on Oct. 8, 2020, and entitled “IDENTIFYING A PRESENCE-ABSENCE STATE OF A MAGNETIC RESONANCE IMAGING SYSTEM”, the entire teaching each of which are incorporated herein by reference in their entireties.
In some examples, the IMD may comprise an SDB care device having an IPG. In some such examples, care provided by the care engine 1108 may comprise delivering stimulation therapy (e.g., delivering a stimulation signal) when the patient is in a sleep state. In some examples, the care usage engine 1106 may identify the care usage pattern based on the sleep-wake status of the patient. For example, the care usage pattern may include and/or be based on an expected sleep-wake status pattern, an observed sleep-wake status based on a historically observed sleep-wake status pattern, and a current sleep-wake status. As an example, a first polling interval may be set for at least a first time window associated with an awake state of the patient (e.g., a time of the day prior to sleep onset or onset of an awake state from a sleep state). A second polling internal may be set for at least a second time window associated with a sleep state of the patient, where the second polling interval is greater than the first polling interval.
In some examples that include an SDB care device, the observed sleep-wake status and/or the current sleep-wake status by may be determined by sensing physiological data via at least one implantable sensor in communication with the IMD, such as the physiological data described above.
Examples are not limited to SDB care devices and may comprise other neurostimulators, sensing, and/or cardiac care devices. Other example sensing and/or stimulating devices may be directed to sensing and/or simulating for urinary and/or pelvic disorders.
In some examples, for a neurostimulator, and the care provided comprises delivering neurostimulation by the neurostimulator. In some such examples, the care usage pattern may be identified based on an intensity of neurostimulation delivered, a time of day of delivery of the neurostimulation, and/or a time of day of data communication between the IMD and external circuitry.
As an example, with a neurostimulator, a first polling interval may be set for at least a first time window associated with the time of day of delivery of the neurostimulation based on the care usage pattern, the first time window following the time of day of delivery. A second polling interval may be set for at least a second time window, the second polling interval being greater than the first polling interval. In some examples, the care usage pattern may additionally be identified based on the sleep-wake status of the patient. For example, the first polling interval may be set for the first time window and at least a third time interval, where the third time interval is associated with a time of day prior to sleep onset for the patient and/or an onset of an awake state from a sleep state for the patient.
For a pacer or other cardiac care device, the IMD may provide care by delivering cardiac stimulation therapy to the patient. In some such examples, the care usage pattern may be identified based on at least one of an intensity of cardiac stimulation delivered, a time of day of delivery of the cardiac stimulation, and a time of day of data communication between the IMD and external circuitry.
As an example, with the cardiac care device, a first polling interval may be set for at least a first time window associated with the time of day of delivery of the cardiac stimulation based on the care usage pattern, the first time window following the time of day of delivery. A second polling interval may be set for at least a second time window, the second polling interval being greater than the first polling interval. In some examples, the care usage pattern may additionally be identified based on the sleep-wake status of the patient. For example, the first polling interval may be set for the first time window and at least a third time interval, where the third time interval is associated with a time of day prior to sleep onset for the patient and/or an onset of an awake state from a sleep state for the patient.
In some examples the IMD comprises an implanted infusion pump that provides care by delivering fluid (e.g., medicine) to the patient. In some such examples, the care usage pattern may be identified based on at least one of an amount of fluid delivered, a time of day of delivery of the fluid, and a time of day of data communication between the IMD and external circuitry.
As an example, with the implanted infusion pump, a first polling interval may be set for at least a first time window associated with the time of day of delivery of the fluid based on the care usage pattern, the first time window following the time of day of delivery. A second polling interval may be set for at least a second time window, the second polling interval being greater than the first polling interval. In some examples, the care usage pattern may additionally be identified based on the sleep-wake status of the patient. For example, the first polling interval may be set for the first time window and at least a third time interval, where the third time interval is associated with a time of day prior to sleep onset for the patient and/or an onset of an awake state from a sleep state for the patient.
In related and non-limiting examples, a data event may be performed by the IMD based on the data event parameter. For example, data may be communicated to external circuitry, such as external device 26 illustrated by
As further shown in
With regard to the some examples of the present disclosure, in some examples, delivering stimulation to an upper airway patency nerve (e.g., a hypoglossal nerve 1405) via the stimulation electrode 1412 is to cause contraction of upper airway patency-related muscles, which may cause or maintain opening of the upper airway (1408) to prevent and/or treat obstructive sleep apnea. Similarly, in some examples such electrical stimulation may be applied to a phrenic nerve 1406 via the stimulation electrode 1412 to cause contraction of the diaphragm as part of preventing or treating at least central sleep apnea. It will be further understood that some example methods may comprise treating both obstructive sleep apnea and central sleep apnea, such as but not limited to, instances of multiple-type sleep apnea in which both types of sleep apnea may be present at least some of the time. In some such instances, separate stimulation leads 1417 may be provided or a single stimulation lead 1417 may be provided but with a bifurcated distal portion with each separate distal portion extending to a respective one of the hypoglossal nerve 1405 and the phrenic nerve 1406.
In some examples, the device 1411 may treat multiple-type sleep apnea using at least some of substantially the same features and attributes as described within U.S. Patent Publication No. 2020/0147376, published on May 14, 2020, and entitled “MULTIPLE TYPE SLEEP APNEA”, the entire teachings of which is incorporated herein by reference in its entirety. In some examples, the device 1411 may treat and/or stimulate the ansa cervicalis (AC)-related nerve to maintain and/or restore upper airway patency using at least some of substantially the same features and attributes as described within U.S. Provisional Patent Application No. 63/029,446, filed on May 23, 2020, and entitled “SINGLE OR MULTIPLE NERVE STIMULATION TO TREAT SLEEP DISORDERED BREATHING”, the entire teachings of which is incorporated herein by reference in its entirety.
In some such examples, the contraction of the hypoglossal nerve and/or contraction of the phrenic nerve caused by electrical stimulation comprises a suprathreshold stimulation, which is in contrast to a subthreshold stimulation (e.g., mere tone) of such muscles. In one aspect, a suprathreshold intensity level corresponds to a stimulation energy greater than the nerve excitation threshold, such that the suprathreshold stimulation may provide for higher degrees (e.g., maximum, other) of upper-airway clearance (i.e., patency) and sleep apnea therapy efficacy.
In some examples, a target intensity level of stimulation energy is selected, determined, implemented, etc. without regard to intentionally establishing a discomfort threshold of the patient and/or an arousal threshold (such as in response to such stimulation). Stated differently, in at least some examples, a target intensity level of stimulation may be implemented to provide the desired efficacious therapeutic effect in reducing SDB without attempting to adjust or increase the target intensity level according to (or relative to) a discomfort threshold and/or an arousal threshold.
In some examples, the treatment period (during which stimulation may be applied at least part of the time) may comprise a period of time beginning with the patient turning on the therapy device and ending with the patient turning off the device. In some examples, the treatment period may comprise a selectable, predetermined start time (e.g., 10 p.m.) and selectable, predetermined stop time (e.g., 6 a.m.). In some examples, the treatment period may comprise a period of time between an auto-detected initiation of sleep and auto-detected awake-from-sleep time. With this in mind, the treatment period corresponds to a period during which a patient is sleeping such that the stimulation of the upper airway patency-related nerve and/or central sleep apnea-related nerve is generally not perceived by the patient and so that the stimulation coincides with the patient behavior (e.g., sleeping) during which the sleep disordered breathing behavior (e.g., central or obstructive sleep apnea) would be expected to occur.
Information related to the treatment period, in some examples, may be input to the data model and/or otherwise used by the care usage engine to set at least one data event parameter, such as the care usage engine 1106 illustrated by
Some non-limiting examples of such devices and methods to recognize and detect the various features and patterns associated with respiratory effort and flow limitations include, but are not limited to: U.S. Pat. No. 8,938,299, issued Jan. 20, 2015, and entitled “SYSTEM FOR TREATING SLEEP DISORDERED BREATHING”; U.S. Pat. No. 5,944,680, issued Aug. 31, 1999, and entitled “RESPIRATORY EFFORT DETECTION METHOD AND APPARATUS”; and U.S. Pat. No. 5,522,862, issued Jun. 4, 1996, and entitled “METHOD AND APPARATUS FOR TREATING OBSTRUCTIVE SLEEP APNEA”, the entire teachings of each are hereby incorporated by reference herein in their entireties.
Moreover, in some examples various stimulation methods may be applied to treat obstructive sleep apnea, which include but are not limited to: U.S. Pat. No. 10,583,297, issued Mar. 10, 2020, and entitled “METHOD AND SYSTEM FOR APPLYING STIMULATION IN TREATING SLEEP DISORDERED BREATHING”; U.S. Pat. No. 8,938,299, issued Jan. 20, 2015, and entitled “SYSTEM FOR TREATING SLEEP DISORDERED BREATHING”; U.S. Pat. No. 5,944,680, issued Aug. 31, 1999, and entitled “RESPIRATORY EFFORT DETECTION METHOD AND APPARATUS”; and U.S. Patent Publication No. 2018/0117316, published May 3, 2018, and entitled “STIMULATION FOR TREATING SLEEP DISORDERED BREATHING”, the entire teachings of each are hereby incorporated by reference herein in their entireties.
In some examples, the example stimulation electrode(s) 1412 shown in
In some examples, the stimulation electrode 1412 may be delivered transvenously, percutaneously, etc. In some such examples, a transvenous approach may comprise at least some of substantially the same features and attributes as described in U.S. Pat. No. 9,889,299, issued Feb. 13, 2018, entitled “TRANSVENOUS METHOD OF TREATING SLEEP APNEA”, and which is hereby incorporated by reference in its entirety. In some such examples, a percutaneous approach may comprise at least some of substantially the same features and attributes as described in U.S. Pat. No. 9,486,628, issued Nov. 8, 2016, and entitled “PERCUTANEOUS ACCESS FOR SYSTEMS AND METHODS OF TREATING SLEEP APNEA”, the entire teachings of which is incorporated herein by reference in its entirety.
As further shown in the diagram of
However, examples are not so limited and may be directed to other neurostimulation devices and cardiac care devices which may detect cardiac signals and provide atrial chamber stimulation therapy. For example, the IMD may include or be coupled to an implantable leads using to sense left and right atrial and ventricular cardiac signals. The electronics assembly of the IMD processes or monitors the cardiac signals and provides stimulation signals using a pulse generator and the implantable leads.
In some examples, the microstimulator 1419B (and associated elements) and/or IMD 1419A may comprise at least some of substantially the same features and attributes as described and illustrated within: U.S. Patent Publication No. 2020/0254249, published on Aug. 8, 2020, and entitled “MICROSTIMULATION SLEEP DISORDERED BREATHING (SDB) THERAPY DEVICE”; and U.S. Patent Publication No. 2020/0391028, published on Dec. 17, 2020, and entitled “IMPLANT-ACCESS INCISION AND SENSING FOR SLEEP DISORDERED BREATHING (SBD) CARE”, the entire teachings of which are incorporated herein by reference in their entireties.
As implicated by the above description, one or both of the IMD and the external device includes a controller, control unit, or control portion that prompts performance of designated actions.
In general terms, the controller 1602 of the control portion 1600 comprises an electronics assembly 1606 (e.g., at least one processor, microprocessor, integrated circuits and logic, etc.) and associated memories or storage devices. The controller 1602 is electrically couplable to, and in communication with, the memory 1604 to generate control signals to direct operation of at least some the devices, systems, assemblies, circuitry, managers, modules, engines, functions, parameters, sensors, electrodes, and/or methods, as represented throughout the present disclosure. In some examples, these generated control signals include, but are not limited to, employing the care usage engine 1610 of an IMD which may be a software program stored on the memory 1604 (which may be stored on another storage device and loaded onto the memory 1604), and executed by the electronics assembly 1606 to set at least one data event parameter. In addition, and in some examples, these generated control signals include, but are not limited to, employing the care engine 1609 stored in the memory 1604 to at least manage care provided to the patient, for example cardiac therapy or therapy for sleep disordered breathing, in at least some examples of the present disclosure. It will be further understood that the control portion 1600 (or another control portion) may also be employed to operate general functions of the various care devices/systems described throughout the present disclosure. In some examples, the care usage engine 1610 and the care engine 1609 may include at least some of substantially the same features as described by the care usage engine 1106 and the care engine 1108 of at least
In response to or based upon commands received via a user interface (e.g., user interface 1640 in
For purposes of this application, in reference to the controller 1602, the term “processor” shall mean a presently developed or future developed processor (or processing resources) that executes machine readable instructions contained in a memory. In some examples, execution of the machine readable instructions, such as those provided via memory 1604 of control portion 1600 cause the processor to perform the above-identified actions, such as operating controller 1602 to implement the sensing, monitoring, identifying the care cycle, stimulation, treatment, etc. as generally described in (or consistent with) at least some examples of the present disclosure. The machine readable instructions may be loaded in a random access memory (RAM) for execution by the processor from their stored location in a read only memory (ROM), a mass storage device, or some other persistent storage (e.g., non-transitory tangible medium or non-volatile tangible medium), as represented by memory 1604. In some examples, the machine readable instructions may comprise a sequence of instructions, a processor-executable machine learning model, or the like. In some examples, memory 1604 comprises a computer readable tangible medium providing non-volatile storage of the machine readable instructions executable by a process of controller 1602. In some examples, the computer readable tangible medium may sometimes be referred to as, and/or comprise at least a portion of, a computer program product. In some examples, hard wired circuitry may be used in place of or in combination with machine readable instructions to implement the functions described. For example, controller 1602 may be embodied as part of at least one application-specific integrated circuit (ASIC), at least one field-programmable gate array (FPGA), and/or the like. In at least some examples, the controller 1602 is not limited to any specific combination of hardware circuitry and machine readable instructions, nor limited to any particular source for the machine readable instructions executed by the controller 1602.
In some examples, control portion 1600 may be entirely implemented within or by a stand-alone device.
In some examples, the control portion 1600 may be partially implemented in one of the sensors, sensing element, respiration determination elements, monitoring devices, stimulation devices, IMDs (or portions thereof), etc. and partially implemented in a computing resource (e.g., at least one external resource) separate from, and independent of, the IMDs (or portions thereof) but in communication with the IMDs (or portions thereof). For instance, in some examples control portion 1600 may be implemented via a server accessible via the cloud and/or other network pathways. In some examples, the control portion 1600 may be distributed or apportioned among multiple devices or resources such as among a server, an apnea treatment device (or portion thereof), and/or a user interface.
In some examples, control portion 1600 includes, and/or is in communication with, a user interface 1640 as shown in
Although specific examples have been illustrated and described herein, a variety of alternate and/or equivalent implementations may be substituted for the specific examples shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the specific examples discussed herein.
This application claims the benefit of the filing date of U.S. Provisional Application Ser. No. 63/176,518, filed Apr. 19, 2021 and entitled “Setting a Data Event Parameter for an Implantable Medical Device Based on a Care Usage Pattern,” the entire teachings of which are incorporated herein by reference.
Number | Date | Country | |
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63176518 | Apr 2021 | US |