Patent Grant
8725239
This application relates to the following commonly assigned co-pending application entitled:
“Identifying Seizures Using Heart Data From Two or More Windows” U.S. patent application 13/093,613, filed Apr. 25, 2011, Reference No. 100.235.
1. Technical Field of the Present Disclosure
The present disclosure relates generally to the field of seizure identification and more particularly to the field of identifying seizures by monitoring changes in heart rates.
2. Background of the Present Disclosure
Seizures are characterized by abnormal or excessive neural activity in the brain. Seizures may involve loss of consciousness or awareness, and result in falls, uncontrollable convulsions, etc. Significant injuries may result not only from the neuronal activity in the brain but also from the associated loss of motor function from falls or the inability of the patient to perceive and/or respond appropriately to potential danger or harm.
It is desirable to identify a seizure event as quickly as possible after the beginning of the seizure, to allow appropriate responsive action to be taken. Such actions may include sending an alert signal to the patient or a caregiver, taking remedial action such as making the patient and/or the immediate environment safe (e.g., terminating operation of equipment, sitting or lying down, moving away from known hazards), initiating a treatment therapy, etc. Where rapid detection is not possible or feasible, it is still desirable to be able to identify seizures after they have begun to allow a physician and/or caregiver to assess the patient's condition and determine whether existing therapies are effective or require modification and/or additional therapy modalities (for example, changing or adding additional drug therapies or adding a neurostimulation therapy). Seizure detection algorithms have been proposed using a variety of body parameters, including brain waves (e.g., electroencephalogram or EEG signals), heart beats (e.g., electrocardiogram or EKG), and movements (e.g., triaxial accelerometer signals). See, e.g., U.S. Pat. No. 5,928,272 and U.S. application Ser. No. 12/770,562, both of which are hereby incorporated by reference herein.
Detection of seizures using heart data requires that the seizure detection algorithm distinguish—or attempt to distinguish—between pathological changes in the detected heart signal (which may indicate a seizure) and non-pathological changes that may be similar to pathological changes but involve normal physiological functioning. For example, the patient's heart rate may increase both when a seizure event occurs and when the patient exercises, climbs stairs or performs other physiologically demanding acts. In some instances, state changes such as rising from a prone or sitting position to a standing position, such as in rising after a sleep period, may produce cardiac changes similar to seizure events. Thus, seizure detection algorithms must distinguish between changes in heart rate due to a seizure and those due to exertional or positional/postural changes.
Current algorithms fail to provide rapid and accurate detection. There is a need for improved algorithms that can more accurately distinguish between ictal and non-ictal heart rate changes. There is also a need for algorithms that may provide an initial detection to allow early warning or therapeutic intervention, and which allows for continued signal analysis subsequent to the initial detection, and permitting the initial detection to be subsequently confirmed or rejected as a seizure based on the signal data acquired after the initial detection. The present invention addresses limitations associated with existing cardiac-based seizure detection algorithms.
In one respect, disclosed is a method for detecting a seizure event, the method comprising receiving heart beat data versus time for a patient, detecting an increase in the heart rate of a patient from a baseline heart rate to an elevated heart rate, detecting a decrease in heart rate from the elevated heart rate, for a time interval occurring during said decrease in heart rate, determining at least one of a) a rate of decrease in heart rate and b) a rate of change in a rate of decrease in heart rate, and detecting a seizure event in response to determining at least one of a) a rate of decrease in heart rate greater than a threshold rate of decrease, and b) a rate of change in the rate of decrease less than a threshold rate of change in a rate of decrease.
In another respect, disclosed is a system for detecting a seizure event in a patient, the system comprising one or more processors, one or more memory units coupled to the one or more processors, the system being configured to receive data of heart beat versus time, detect an increase in the heart rate from a baseline heart rate to an elevated heart rate, detect a decrease in heart rate from the elevated heart rate, for a time interval occurring during said decrease in heart rate, determine at least one of a) a rate of decrease in heart rate and b) a rate of change in a rate of decrease in heart rate, and detect a seizure event in response to determining at least one of a) that a rate of decrease in heart rate is greater than a threshold rate of decrease, and b) that the rate of change in the rate of decrease is less than a threshold rate of change in a rate of decrease.
In yet another respect, disclosed is a computer program product embodied in a computer-operable medium, the computer program product comprising logic instructions, the logic instructions being effective to process data of heart rate (HR) versus time, and detect an increase in the heart rate of a patient from a baseline heart rate to an elevated heart rate, detect a decrease in heart rate from the elevated heart rate, for a time interval occurring during said decrease in heart rate, determine at least one of a) a rate of decrease in heart rate and b) a rate of change in a rate of decrease in heart rate, and detect a seizure event in response to determining at least one of a) a rate of decrease in heart rate greater than a threshold rate of decrease, and b) a rate of change in the rate of decrease less than a threshold rate of change in a rate of decrease.
In yet another respect, disclosed is a method for detecting a seizure event, the method comprising receiving heart beat data versus time for a patient, determining at least one of a) a rate of decrease in heart rate and b) a rate of change in a rate of decrease in heart rate, and detecting a seizure event in response to determining at least one of a) a rate of decrease in heart rate greater than a threshold rate of decrease, and b) a rate of change in the rate of decrease less than a threshold rate of change in a rate of decrease.
In yet another respect, disclosed is a method for detecting a seizure event, the method comprising receiving heart beat data versus time for a patient, detecting an increase in the heart rate of the patient from a baseline heart rate to an elevated heart rate, detecting a decrease in heart rate from the elevated rate to a first intermediate rate between the elevated rate and the baseline rate, and further detecting a decrease in heart rate to a second intermediate rate between the first intermediate rate and the baseline rate, determining at least one of a) a rate of decrease from said first intermediate rate to said second intermediate rate and b), a rate of change in a rate of decrease in heart rate from said first intermediate rate to said second intermediate rate, and detecting a seizure event in response to determining at least one of a) that the rate of decrease of heart rate from said first intermediate rate to said second intermediate rate is greater than a threshold rate of decrease and b) the rate of change in the rate of decrease from said first intermediate rate to said second intermediate rate is less than a threshold rate of change in a rate of decrease.
Numerous additional embodiments are also possible.
Other objects and advantages of the present disclosure may become apparent upon reading the detailed description and upon reference to the accompanying drawings.
While the present disclosure is subject to various modifications and alternative forms, specific embodiments of the claimed subject matter are shown by way of example in the drawings and the accompanying detailed description. The drawings and detailed description are not intended to limit the presently claimed subject matter to the particular embodiments. This disclosure is instead intended to cover all modifications, equivalents, and alternatives falling within the scope of the presently claimed subject matter.
One or more embodiments of the present claimed subject matter are described below. It should be noted that these and any other embodiments are exemplary and are intended to be illustrative of the claimed subject matter rather than limiting. While the present claimed subject matter is widely applicable to different types of systems, it is impossible to include all of the possible embodiments and contexts of the present claimed subject matter in this disclosure. Upon reading this disclosure, many alternative embodiments of the presently claimed subject matter will be apparent to persons of ordinary skill in the art.
The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed here may be implemented as electronic/computer hardware, computer software, or combinations of the two. Various illustrative components, blocks, modules, circuits, and steps are described generally in terms of their functionality. Whether such functionality is implemented as hardware or software, or allocated in varying degrees to hardware and software respectively, may depend upon the particular application and imposed design constraints. The described functionality may be implemented in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the presently claimed subject matter.
Graph 110 shows the rise of a subject's heart rate (HR) from a pre-ictal baseline HR to a peak HR (at point 140) following the onset of a seizure at time S 145. Graph 110 also shows the decrease of a subject's heart rate (HR) from peak HR 140 to a post-ictal baseline HR (at point 150) following the end of a seizure. For some patients, the post-ictal baseline HR may be different from the pre-ictal baseline HR.
Seizures are often characterized by an increase in HR from an initial or baseline HR to an elevated HR, followed by a decrease in HR from the elevated HR back toward the baseline HR. The increase in HR may begin before, at, or shortly after the electrographic or clinical onset of the seizure, and the decrease in HR may begin at the time the seizure ends. The baseline heart rate may be determined as a statistical measure of central tendency of HR during a desired time window, typically a window prior to an increase in HR associated with a seizure or exertional tachycardia. In one nonlimiting example, the baseline HR may be a median, average or similar statistical measure of HR in a 500 second window. In another embodiment, a number-of-beats window may be used instead of a time window. Various forms of weighting may also be employed to determine the baseline HR, such as exponential forgetting.
In some embodiments, heart rate data analyzer 210 is configured to receive and analyze heart rate data 225. Heart rate data 225 may be a series of heart rate values at given points in time. The heart rate data may be received in real time or near real time from a subject or the heart rate data may be data that was previously recorded and is being received from a storage device.
In some embodiments, heart rate data analyzer 210 is configured to analyze the data and identify seizure events that the subject may have suffered and/or is currently suffering. Heart rate data analyzer 210 is additionally configured to distinguish seizure events from nonpathologic events that may have similar effects on a subject's HR. The functionality of heart rate data analyzer 210 may be implemented using one or more processors such as processor(s) 215 and one or more memory units coupled to the one or more processors such as memory unit(s) 220.
Heart rate data analyzer 210 may be configured to identify the offset of a seizure by examining the rate and/or profile with which the HR drops during the offset of the seizure as discussed here.
In some embodiments, systems and methods are disclosed for detecting a seizure event by examining data of the heart rate (HR) versus time of a subject. The subject's heart rate may be obtained in real time or near real time using various methods, including well-known electrocardiogram (ECG) processes. In alternative embodiments, previously stored/recorded HR data may be provided to embodiments of the present invention for analysis.
In some embodiments, heart rate data analyzer 210 may identify a seizure by identifying body signal changes associated with the end of the seizure. Existing seizure detection algorithms focus on identifying the beginning of the seizure (i.e., onset of the ictal state from a non-ictal or pre-ictal state), typically as exemplified by a significant change in a body signal, such as an increase in HR from a baseline HR to an elevated HR. Various attempts to distinguish ictal HR increases from non-ictal increases have been made, but prior art approaches have unacceptably high rates of false positives (i.e., detecting non-ictal changes as a seizure) and false negatives (i.e., failure to detect ictal changes).
In contrast to prior art approaches, the present invention involves identifying a seizure by changes associated with the end of a seizure (i.e., the ictal-to-post-ictal transition). Without being bound by theory, it is believed that changes associated with the end of a seizure may provide improved methods of distinguishing between ictal and non-ictal HR changes.
In some embodiments, a seizure may be identified by determining one or more characteristics of a decrease in HR from an elevated HR back towards a baseline HR. More specifically, an episode of elevated heart rate followed by a return towards a baseline rate may be analyzed and classified as a seizure or as a non-seizure event (for example, exertional tachycardia associated with exercise or normal activity).
In one embodiment, a time interval during a decrease in HR from an elevated HR is analyzed to determine one or more of a) a rate of decrease in HR or b) a rate of change of the rate of decrease in HR. The rate of decrease may be determined from actual data or smoothed data (e.g., by fitting a higher order polynomials to one or more segments of actual data). The rate of decrease may be compared to a threshold rate of decrease associated with a seizure event and/or a threshold rate of decrease associated with a non-seizure event. The rate of change in a rate of decrease may be compared to a threshold rate of change of a rate of decrease associated with a seizure event and/or a threshold rate of change of a rate of decrease associated with a non-seizure event. The event may be detected as a seizure event if the rate of decrease from an elevated heart rate back toward a baseline heart rate exceeds a threshold rate of decrease, or if the rate of change of a rate of decrease is less than a threshold rate of change of a rate of decrease.
In some embodiments, the threshold rate of decrease and/or the threshold rate of change of the rate of decrease may be determined from nonpathologic rates of decrease and/or rates of change of rates of decrease from nonpathologic events that also result in patterns of increasing HR followed by decreasing HR. Such nonpathologic events may include, for example, physical exertion during exercising, climbing or descending stairs, walking, or postural changes. In other embodiments, the threshold rate of decrease and/or the threshold rate of change of the rate of decrease may be determined from seizure events. In some embodiments, different thresholds may be established for different types of seizures, e.g., tonic-clonic seizures, complex partial, simple partial, etc. Thresholds may also be established that are patient-specific, i.e., determined from seizure events of the patient, or from aggregated patient data from multiple patients.
In some embodiments, the rate of decrease in HR (which will be referred to here equivalently as heart beat acceleration, HBA, heart rate drop or HRD), may correspond to an instantaneous or time-interval-specific (e.g., a 15-second moving window) slope of a graph of the HR versus time. This slope may be determined at a specific point(s) and/or for specific intervals during the decrease in HR from an elevated heart rate back towards a baseline heart rate. In one embodiment, the peak heart rate during a tachycardia event (i.e., a heart rate increase above a baseline heart rate followed by a decrease toward the baseline rate) and the baseline rate may be used to determine a peak-to-baseline (PTB) value that is useful for performing calculations according to certain embodiments. For a given point along the decreasing HR curve from the peak heart rate, one useful rate of decrease may be determined as the average slope (or average rate of decrease) from the peak to the given point. In other embodiments, short-term rates of decrease may be established for a short-term time window along the decreasing HR curve from peak to baseline. Short-term rates of decrease may be determined for a 5-second or 5-beat window, for example, or from the last two heart beats.
In certain embodiments, particular short-term rates of decrease may be useful to compare to later short-term rates of decrease. It has been appreciated by the present inventor that PTB decreases in heart rate for seizure events and non-seizure events differ qualitatively. In particular, decreases in HR for seizure events tend to maintain a relatively constant rate of decrease during most of the PTB decline. In non-seizure events, by contrast, rates of decrease tend to decline as the HR approaches the baseline HR. Thus, for seizure events the slope of the PTB heart rate curve tends to be relatively straight. The slope of the PTB heart rate curve for non-seizure tachycardia episodes, on the other hand, tends to flatten as the HR approaches the baseline heart rate, resulting in a HR curve that is “upwardly concave” near the baseline for non-seizure events.
Because the differences in HR decline between seizure and non-seizure events is most prominent near the baseline, in some embodiments, rates of decline and/or rates of change of rates of decline are determined at rates below the rate halfway between the peak and the baseline heart rate.
In some embodiments, a seizure end may be identified in response to determining that the HR drop at a specific point during the PTB transition is greater (in absolute value since during a heart rate decrease the slope is negative) than a seizure threshold value. In some embodiments, HRDs during PTB transitions in healthy subjects for nonpathologic events are smaller than HRDs during a corresponding time during a seizure event. The threshold HRD may accordingly be chosen in order to maximize the accuracy of the seizure identification process. Binary classification statistics may be used to maximize the accuracy of the detection by appropriately balancing the sensitivity and specificity of the identification process.
In some embodiments, the HRD (the slope of the HR v. time graph) at a particular point may be computed numerically from the HR v. time data using well-known numerical computation techniques for calculating slope using numerical data.
In some embodiments, average HRDs may be used over one or more intervals for identifying a seizure offset. Intervals may be chosen anywhere between a peak HR and the return towards a baseline HR, the peak HR being the highest HR value reached during the seizure or nonpathologic event, and the baseline HR being the HR of the subject prior to the tachycardia event under consideration (whether pathological or non-pathological). For example, a First Half HRD may be computed for an interval between the peak HR value and the HR that is halfway between the baseline HR and the peak HR. Similarly, a Middle Half HRD may be computed for an interval between the HR that is 25% of the way between the peak HR and the baseline HR and the HR that is 75% of the way between the peak HR and the baseline HR, and a Second Half HRD may be computed for the interval between the HR that is 50% of the distance from peak-to-baseline, and the baseline HR itself. Similarly, a First Third HRD may be computed between the peak HR and the HR that is ⅓ of the way from the peak HR to the baseline HRD, and a Final Third HRD may be computed between the HR that is ⅔ of the way from the peak HR to the baseline HR and the baseline HR itself. Similar intervals may be constructed, and the HRD computed, depending upon the points in the decline from peak to baseline that provides a desirable level of discrimination between seizure and non-seizure events. More generally, in some embodiments, an average HRD over an interval from point A to point B may be computed by dividing the HR change from point A to point B by the time change from point A to point B.
In some embodiments, the offset of a seizure may be identified in response to determining that the First Half HRD and Middle Half HRD are substantially equal. For example, the offset of the seizure may be identified in response to determining that the First Half HRD and the Middle Half HRD are within a certain percentage of each other. It should be noted that other appropriate intervals/average HRDs may be selected and used in various combinations to identify a seizure.
In some embodiments, a seizure may be identified by comparing HRDs at one or more points and/or by comparing average HRDs over one or more intervals to HRDs threshold values. In some embodiments, the threshold HRD values may be determined by examining typical corresponding values of HRDs for seizure and nonpathologic events. For example, a seizure may be identified in response to determining that an average One Third HRD is above a certain threshold, which is determined by examining corresponding One Third HRD values for typical seizures as well as nonpathologic events.
In some embodiments, a general profile of the HR versus time during a seizure offset may be determined and compared to known HR versus time profiles during seizures and nonpathologic events. In some embodiments, a seizure offset may be identified in response to determining that there exists a substantial match between the determined profile and the known seizure profiles, or a substantial dissimilarity between the determined profile and one or more known nonpathologic profiles. In some embodiments, a seizure may be identified in response to determining that a seizure profile is substantially similar to a linear seizure profile and substantially dissimilar to a nonpathologic profile such as an asymptotically decreasing profile (for example, a decreasing exponential profile), a concave decreasing profile, etc.
In some embodiments, heart rate data analyzer 310 is configured to receive and analyze heart rate data 325. Heart rate data 325 may be a series of heart rate values at given points in time. The heart rate data may be received in real time or near real time from heart rate detection equipment connected to a subject, such as HR detector 330. HR detector 330, in some embodiments may comprise electrocardiogram equipment, which is configured to couple to a subject's body in order to detect the subject's heart beat.
In some embodiments, heart rate data analyzer 310 is configured to analyze the data and identify seizure events that the subject may have suffered and/or is currently suffering. The functionality of heart rate data analyzer 310 may be implemented using one or more processors such as processor(s) 315 and one or more memory units coupled to the one or more processors such as memory unit(s) 320.
Heart rate data analyzer 310 may be configured to identify the offset of a seizure by examining the rate and generally the profile with which the HR drops during the offset of the seizure as discussed here.
Heart rate data analyzer 310 may also be coupled to human interface input device 335 and human interface output device 340. Human interface input device 335 may be configured to provide a user of the system a means with which to input data into the system and with which to generally control various options. Accordingly, human interface input device 335 may be at least one of a computer keyboard, a touch screen, a microphone, a video camera, etc.
Human interface output device 340 may be configured to provide information to a user of the system visually, audibly, etc. Accordingly, human interface output device 340 may be at least one of a computer display, one or more audio speakers, haptic feedback device, etc. In some embodiments, human interface input device 335 and human interface output device may be combined into a single unit.
A particular embodiment of a system for monitoring heart beat data from a subject is shown in the Figure and generally designated 400. System 400 may include, a heart beat sensor 440, a controller 455, and a computer 410.
In some embodiments, heart beat and/or heart rate data may be collected by using an external or implanted heart beat sensor and related electronics (such as heart beat sensor 440), and a controller that may be wirelessly (or via wire) coupled to the sensor for detecting seizure events based upon the patient's heart signal, such as controller 455. In one embodiment, sensor 440 may comprise electrodes in an externally worn patch adhesively applied to a skin surface of patient 485. In some embodiments, sensor 440 may be implanted under the patient's skin. The patch may include electronics for sensing and determining a heart beat signal (e.g., an ECG signal), such as an electrode, an amplifier and associated filters for processing the raw heart beat signal, an A/D converter, a digital signal processor, and in some embodiments, an RF transceiver wirelessly coupled to a separate controller unit, such as controller 455. In some embodiments, the controller unit may be part of the patch electronics.
The controller 455 may implement an algorithm for detection of seizure events based on the heart signal. It may comprise electronics and memory for performing computations of, e.g. HR parameters such as median HR values for the first and second windows, determination of ratios and/or differences of the first and second HR measures, and determination of seizure onset and offset times according to the foregoing disclosure. In some embodiments, the controller 455 may include a display and an input/output device. The controller 455 may comprise part of a handheld computer such as a PDA or smartphone, a cellphone, an iPod® or iPad®, etc.
In the example shown, sensor 440 may be placed on a body surface suitable for detection of heart signals. Electrical signals from the sensing electrodes may be then fed into patch electronics for filtering, amplification and A/D conversion and other preprocessing, and creation of a time-of-beat sequence (e.g., an R-R interval data stream), which may then be transmitted to controller 455. Sensor 440 may be configured to perform various types of processing to the heart rate data, including filtering, determination of R-wave peaks, calculation of R-R intervals, etc. In some embodiments, the patch electronics may include the functions of controller 455, illustrated in
The time-of-beat sequence may be then provided to controller 455 for processing and determination of seizure onset and offset times and related seizure metrics. Controller 455 may be configured to communicate with computer 410. Computer 410 may be located in the same location or computer 410 may be located in a remote location from controller 455. Computer 410 may be configured to further analyze the heart data, store the data, retransmit the data, etc. Computer 410 may comprise a display for displaying information and results to one or more users as well as an input device from which input may be received by the one or more users. In some embodiments, controller 455 may be configured to perform various tasks such as calculating first and second HR measures, HR parameters, comparing HR parameters to appropriate thresholds, and determining of seizure onset and seizure end times, and other seizure metrics.
In some embodiments, the method illustrated in this figure may be performed by one or more of the systems illustrated in
At block 510, heart beat data versus time for a patient is received.
At block 515, an increase in the heart rate of a patient is detected from a baseline heart rate to an elevated heart rate.
At block 520, a decrease in heart rate is detected from the elevated heart rate.
At block 525, for a time interval occurring during said decrease in heart rate, at least one of a) a rate of decrease in heart rate and b) a rate of change in a rate of decrease in heart rate, is determined.
At block 530, a seizure event is detected in response to determining at least one of a) a rate of decrease in heart rate greater than a threshold rate of decrease, and b) a rate of change in the rate of decrease less than a threshold rate of change in a rate of decrease. In some embodiments, detecting of the seizure event comprises determining the end of a seizure event. The threshold rate of decrease or threshold rate of change of rate of decrease may in some embodiments be selected after examining previous such rates for seizures as well as nonpathologic events.
In some embodiments, the method illustrated in this figure may be performed by one or more of the systems illustrated in
At block 610, data of heart rate (HR) versus time is provided. In some embodiments, the data may be provided in real time or near real time or the data may be retrieved from storage.
At block 615, an HR drop rate or HRD (which corresponds to a slope of the HR versus time data) is determined at one or more points of the provided data. In some embodiments, instead of an HRD at a single point, an average HRD may be determined over an interval of the HRD versus time data/graph.
At decision 620, a determination is made as to whether the HRD is above a threshold HRD. In some embodiments, the threshold HRD may be chosen by examining previous seizure and nonpathologic HRDs.
If the HRD is not above the threshold HRD, decision 620 branches to the “no” branch, and processing returns to block 610 where additional data is received for processing. On the other hand, if the HRD is above the threshold HRD, decision 620 branches to the “yes” branch, and processing continues at block 625.
At block 625, the examined HRD is indicated as indicative of the end of a seizure, and thus a seizure event is identified. Subsequently, processing returns to block 610 where additional data is provided for processing.
Graph 710 shows the rise of a subject's heart rate (HR) from a baseline HR to a peak HR and then the fall of the HR back toward the baseline HR after some time for a typical seizure case and for a non-pathological case. Point ½ HR marks the HR value between the peak HR and the baseline HR, and point ¾ HR marks the HR value that is ¾ of the way from the peak HR to the baseline HR. In the figure, the non-pathological HR drop is indicated by the dotted line.
In some embodiments, in order to determine whether the fall in the HR corresponds to the end of a seizure, the slope of the graph (i.e., HRD) may be computed. In some embodiments, the instantaneous slope may be computed at a point. In alternative embodiments, an average slope may be computed between two points.
For example, the instantaneous slope may be computed at point 725 and corresponding point 730 for the non-pathological case. The two slopes for the typical seizure case and the non-pathological case are illustrated by dashed lines 727 and 732 respectively.
Alternatively, an average slope may be computed between points 725 and 726 and between corresponding points 730 and 731 for the non-pathological case. The two average slopes for the typical seizure case and the non-pathological case are illustrated by dashed lines 728 and 733 respectively.
Regardless of the method used to compute the slope, a seizure may be identified in response to determining that the slope is below (or above in absolute value) a certain threshold value. As seen by the figure, typical seizure cases exhibit slopes that are smaller (or larger in absolute value) when compared to non-pathological cases as indicated by dashed lines representing these slopes.
In alternative embodiments, a seizure may be identified in response to determining that the average HRDs in two intervals is substantially equal. For example, the average HRD may be computed and compared for two intervals by dividing the difference in HR by the difference in time at the beginning and end of the intervals. Then, as discussed here, the seizure is identified in response to determining that the HRDs for the two intervals are substantially equal, or differ by only a threshold slope difference. By comparison, a typical non-pathological case will exhibit a greater difference in the average slope between two different intervals.
Similarly, the concavity of the graph may be computed for a certain interval and compared to certain threshold concavities. As can be seen by the figure, typical seizure cases exhibit concavities that are typically larger compared to the concavities of non-pathological events. In some embodiments, the concavity may be computed by determining the second time derivative of the HR. Thus, a seizure may be identified in response to determining that the concavity (average or at a given point) is higher than a threshold concavity value.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present claimed subject matter. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the claimed subject matter. Thus, the present claimed subject matter is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed here.
The benefits and advantages that may be provided by the present claimed subject matter have been described above with regard to specific embodiments. These benefits and advantages, and any elements or limitations that may cause them to occur or to become more pronounced are not to be construed as critical, required, or essential features of any or all of the claims. As used here, the terms “comprises,” “comprising,” or any other variations thereof, are intended to be interpreted as non-exclusively including the elements or limitations which follow those terms. Accordingly, a system, method, or other embodiment that comprises a set of elements is not limited to only those elements and may include other elements not expressly listed or inherent to the claimed embodiment.
While the present claimed subject matter has been described with reference to particular embodiments, it should be understood that the embodiments are illustrative and that the scope of the claimed subject matter is not limited to these embodiments. Many variations, modifications, additions and improvements to the embodiments described above are possible. It is contemplated that these variations, modifications, additions and improvements fall within the scope of the present disclosure as detailed within the following claims.
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| Number | Date | Country | |
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| 20120271181 A1 | Oct 2012 | US |
