Wearable medical treatment device with motion/position detection

Information

  • Patent Grant
  • 10582858
  • Patent Number
    10,582,858
  • Date Filed
    Thursday, December 21, 2017
    6 years ago
  • Date Issued
    Tuesday, March 10, 2020
    4 years ago
Abstract
A cardiac monitoring system includes cardiac sensing electrodes for external placement proximate to the patient to sense ECG signals of the patient; one or more accelerometers configured to generate patient activity data based on signals corresponding to changes in the patient's body position and movement; and a monitoring computer configured to correlate the ECG signals of the patient with the patient activity data generated from the one or more accelerometers to determine ECG signal contamination, analyze the ECG signals of the patient to extract at least heart rate data of the patient and detect a cardiac arrhythmia based at least in part on the heart rate data, and change a confidence in the detected cardiac arrhythmia based on the determined ECG signal contamination; wherein at least the patient activity data and the heart rate data are analyzed to determine a change in a physiological condition of the patient over time.
Description
FIELD OF THE INVENTION

The present invention is directed toward wearable defibrillators and, more particularly, toward wearable defibrillators capable of detecting the motion and/or position of a patient wearing the wearable defibrillator and using the sensed motion and/or position to determine an appropriate treatment.


BACKGROUND OF THE INVENTION

The wearable defibrillator consists of a monitor and a vest (or belt) which are both worn by the patient. The device monitors the patient's ECG with sensing electrodes, such as 10a, 10b, 10c, 10d, to detect life-threatening arrhythmias and delivers a cardioverting or defibrillating shock through therapy pads, such as 18, if treatment is needed.


There is also a third piece of equipment called the battery charger that can provide two functionalities: first it can charge the monitor batteries, 41, and second it can provide a gateway for the monitor to download data to a central server. The monitor can communicate to the charger through a wireless Bluetooth link and the charger can connect to a central server through the internet.


The accelerometer(s) can allow for the computer in the wearable defibrillator to determine the position of the monitor, belt and/or patient, and the corresponding applied forces. This information can be used in a confidence based arrhythmia detection algorithm to accelerate the timing to hasten the occurrence of or hold off therapy based on past and present body motion and/or position history.


Other devices that could be used instead of an accelerometer would include, but are not limited to: gyroscope, magnetometer, hall-effect devices, and other force motion or position sensors.


SUMMARY OF THE INVENTION

Accelerometers are able to measure x, y, and z positions. This improvement covers having an accelerometer in either the belt, which is worn on the patient's upper body, or in the monitor, which is worn on the patient's lower body; or both locations; or other positions on the patient.


The accelerometer in the belt can be used to determine patient position since it is located on the upper torso of the patient. As a result, the belt accelerometer will be chosen such that high sensitivity data can be measured or determined from possible body positions (e.g. patient position). The accelerometer in the monitor can be chosen so that high sensitivity data (such as breathing or other minor motion) can be measured; low sensitivity data (such as mechanical shock) can also be measured; or both. Additional accelerometers can also be used so that both low sensitivity and high sensitivity data can be measured, or an accelerometer capable of both low sensitivity and high sensitivity can be used.


Some embodiments of the invention include a patient wearable treatment device having treatment elements adjacent to said patient in proximity to said patient's skin, and at least one patient motion detector connected to the device and generating a signal indicative of patient activity, and at least one control to evaluate signals from said motion detectors to determine if said signal is indicative of patient activity appropriate for treatment.


Embodiments include using at least one accelerometer including at least one multi-axis accelerometer and can include two three-axis accelerometers with one of said accelerometers mounted on a patient vest portion and another of said accelerometers is mounted on the monitor or other portion. Some embodiments can include a visual display on said monitor portion where the orientation of said visual display is controlled by the output of said accelerometer.


The body orientation of the patient is determined by the output of at least one accelerometer including standing and prone.


Treatment can be accelerated or delayed based upon the output of the motion detector.


The invention can detect a patient condition based upon the level of activity of a patient over a period of time based upon said stored output of said accelerometer, and can be used to detect conditions such as, for example, congestive heart failure or sleep disorders.


A method of cardiac treatment of a patient including sensing a cardiac condition, sensing patient motion with sensors such as, for example, accelerometers worn by said patient and evaluating said sensed patient activity to determine treatment is appropriate to said cardiac condition.


Embodiments can detect vertical and prone positions and determine a patient activity and a patient body orientation.


The following is a list of the functionality, information that can be provided by the motion detection or accelerometers:


Patient body state: Patient is vertical, Patient is horizontal (On left side, On right side).


Patient is moving in repetitive pattern: Vibrating (environmental), Convulsing.


Patient is accelerating: Patient is falling.


Equipment (belt and monitor) state: x/y/z position.


Equipment acceleration.


Equipment mechanical shock (high force impact, acceleration).


Verification of tactile motor operation in belt node.





BRIEF DESCRIPTION OF FIGURES


FIG. 1 shows a diagrammatic representation of accelerometer positioning in certain embodiments with cardiac sensors and treatment electrodes.



FIG. 2 is a block diagram of an embodiment using two accelerometers.



FIG. 3 is a block diagrammatic representation of an embodiment using an accelerometer in a monitor.



FIG. 4 is a block diagram of one embodiment using a belt node accelerometer.



FIG. 5 is a logic diagram of an algorithm that can be used with one embodiment of the invention.





DESCRIPTION OF SOME EMBODIMENTS OF ALGORITHMS AND METHODS OF USING INFORMATION


FIG. 1 shows a patient 1 with a wearable defibrillator. Typically the devices shown would be worn as a vest, belt and/or other clothing. In this embodiment, four sensing electrodes 10a, b, c, d, or sensors are shown. While this embodiment is for cardiac monitoring and treatment, other medical functions could also be appropriately monitored or treated. In this embodiment, a node, 11, is used and the sensors 10a, b, c, d and treatment devices 18 connect to the node. The node 11 could be on the belt or on other patient locations. The therapy pads or treatment devices 18 provide treatment when a sensed condition indicates a preferred treatment. Any motion sensor can be used. In the present preferred embodiment accelerometers are used. Such sensors indicate accelerating movements. Because of the nature of human movements, generally comprising short distance and short duration, accelerometers give a very acceptable indication of patient movement. Single axis accelerometers can be used as well as multi-axis sensors.


In this embodiment, two accelerometers 16, 17 are used. One accelerometer 17 is located on the node device 11 and a second 16 is used on the monitor 15. It is understood that some embodiments will use a single accelerometer or position/force/motion detector, and still other embodiments may use three or more. Using multiple sensors permit the treatment algorithm to evaluate accelerometer (sensor) differentials to predict patient activity and accelerometer reliability. The use of multiple accelerometers permit separate evaluation of different patient movements and comparing such separate movements to best determine patient activity and equipment function. The actual treatment algorithm used will usually depend upon the diagnostic requirement of each individual doctor and the condition(s) he wishes to monitor. Any or all of the activities determined by the invention can be used. These can be combined with other inputs.



FIG. 2 shows a block diagram of a device in which the accelerometer and microcontroller are housed on the patient, 1, wearable vest or belt. The vest worn devices can communicate via a cable or wireless communication link to a monitoring device that contains a second accelerometer 31. Each accelerometer 21, 31 can indicate the respective movement of its position of the patient, and/or be used in an algorithm that uses the combined signals to more reliably indicate patient activity. Processing of accelerometer data can be performed by the microcontroller 22 in the belt/vest node 20 or a system computer 32 located in the monitor 30 or at both processing locations. Accelerometers 21, 31, indicate change in velocity. Patients will normally have an activity level when conscious that includes changes in both velocity and direction. This is in contrast to the lack of change of body motion present in an unconscious patient. Other sensors such as, for example, gyroscopes can be used, with appropriate software to indicate motion or lack of motion. Outputs from sensors may be integrated, compared or differentiated to best predict patient activity, and reduce interference or error signals.


Patient Movement During Arrhythmia


The use of accelerometers can be used to determine a patient's body state during the detection of an arrhythmia. It also may be used to detect if a mechanically noisy environment is the cause of an erroneous arrhythmia detection.


Patient Movement Used in the Confidence Algorithm Factor


A confidence algorithm, which is influenced by many inputs including the patient's body state as determined by the accelerometers, is used to determine if a patient's heart arrhythmias requires defibrillation.


Generally, cardiac treatment is not required if the patient is conscious. By using accelerometers the patient body state can be monitored. If there has been no change in patient body state for a period of time as detected by the accelerometer(s) then there will be an increased confidence of the algorithm that the patient is unconscious. If a change in patient body state has been detected by the accelerometer(s) then there will be a decreased confidence of the algorithm that the patient is unconscious. The wearable defibrillator can hasten its decision to apply treatment if a high level of confidence exists that the patient is unconscious. If patient motion is detected while other sensors and algorithms indicate that a treatable rhythm is present, treatment delivery can be delayed to provide the patient additional time to respond to system messaging.


False Arrhythmia Detection Due to Physical Motion


Sometimes a false arrhythmia is detected by the system due to physical motion, i.e. electrode or cables moving against the body or clothing, which creates false deviations in the patient's ECG. If an arrhythmia is detected and vibration or high patient/equipment acceleration is detected then the patient can be alerted to this condition. The tactile stimulator may be turned on or the audio volume may be increased to notify the patient reference 1. This information may also be applied to the treatment confidence algorithm thereby causing a decrease in confidence given that the physical motion can cause a false positive detection. Use of the accelerometer (s) can reduce undesired treatment of false arrhythmias.


Correlation of ECG Artifact with Belt Motion


Motion of the electrode belt may cause interference with ECG signal pickup and possible false detections. The signals obtained from the accelerometer can be correlated with an ECG signal to determine if ECG signal contamination exists. The quality of the correlation can be used as an additional confidence factor in the arrhythmia detection algorithm. If an arrhythmia is detected and there is a high degree of correlation between the ECG signal and the accelerometer signal the confidence in the arrhythmia detection can be reduced. No signal correlation indicates increased confidence that the arrhythmia detection is accurate.


Treatment Verification


The accelerometers may also be used to verify that a treatment has been applied by detecting sudden movements or muscle spasms in a patient immediately following the treatment. Often after defibrillation a patient's muscles spasm from the energy pulse. The muscle spasm will cause detectable movements on the accelerometers similar to convulsing.


Detection of Bystanders/Unsuccessful Defibrillation


Post shock motion of the patient after several unsuccessful defibrillation attempts may indicate the presence of bystanders. The bystanders could be rescue personnel such as an EMT.


In this case special alarms or voice messages could be generated to inform the bystander of the equipment and treatment status. Additional shocks could be delayed or cancelled to prevent a shock to the bystanders or rescue personnel.


Post Shock Motion Detection


When a shock is delivered the patient may move suddenly and then return to a state where there is a lack of motion. If no further motion is detected a high confidence can exist that the arrhythmia is still present. This information can be used as an additional post-shock confidence factor for the detection algorithm and that a continuing condition exists. If post-shock motion continues or if the patient body position changes from a horizontal to vertical position, there is high confidence that the defibrillation was successful and additional shocks can be delayed.


Belt Quality Feedback


Overall belt quality can be examined by gathering data using the accelerometers during certain failure states such as electrode fall-off and therapy pad fall-off detection.


Reduce Electrode and Therapy Pad Fall-Offs


If one of the electrodes 10 or therapy pads 18 fall off of the patient, then the system will record the patient body state during the fall-off event. Patient positions include sitting up, lying down; left side, right side. If vibration or patient falling is detected then that would also be recorded as well since it might be the cause of the falloff event.


Over time the data can be analyzed and used to determine positions that may tend to cause fall-offs. This information can then be used to improve the belt design reducing and possibly eliminating the fall-offs in those certain activities or positions.


An example would be if post analysis of data over a several month period of time shows that 75% of ECG fall-offs occur when the patient is laying on their left side then the belt design on the left side could be examined to determine what might be making it susceptible to fall-offs in that patient position.


Provide Recommendations to Patients


Accelerometers data collected over time could also be used to inform new patients of patient's body states that tend to be more comfortable. Patients who have worn the device for an extended time will most likely have experimented with different positions (sleeping positions, sitting positions, etc.) and will tend to use the most comfortable ones. This data can be recorded and used to improve the belt for the other positions and also provide recommendations to new patients.


Improve Belt Comfort


The accelerometer data collected during patient use can be used to improve the comfort of the belt by studying patient sleep habits, or habits during other selected activities.


If 80% of the patients tend to sleep on their right side then the assumption can be made that something about the belt makes it less comfortable for the patients to lie on their left side. With this information research can be performed to determine what about that position causes the belt to be uncomfortable and engineering can be performed to improve the belt comfort.


Belt Self Diagnostics


Self diagnostics may also be provided such as Belt Node Tactile Stimulator (vibration/acceleration) self test.


The tactile stimulator 12 (a patient notification device) is a motor with an unbalancing weight on its shaft. When the motor is on it causes the belt to vibrate much like a cell-phone in vibration mode.


When the tactile is activated the accelerometer 17 in the node can be used to verify that the node is vibrating which means that the tactile is working.


Patient Notification of Physical Events


The accelerometers can be used to provide feedback to the patient regarding certain mechanical events. They may also be used to adjust the device audio volume outputs based on the current state of the patient.


Equipment Abuse Notification


If certain mechanical conditions that may lead to equipment damage such as mechanical shock or vibration are detected by the accelerometers then the system can notify the patient of such conditions and advise the patient by the monitor computer screen on 15 of the condition.


If the monitor or belt is dropped or if they are hit with some other object causing a force greater than a predefined acceptable force, then the monitor will provide either an audio or visual (display) indication to the patient that the event has occurred and warn against allowing such an event to occur again.


If continuous vibration above a certain predefined acceptable threshold is detected for a period of time then the monitor on 15 may also provide a warning to the patient. Such vibration could lead to electrode or therapy pad fall-off or even cause false arrhythmia detection if enough physical motion is applied to the electrode and cables.


Adjust Device Alarm Volumes


If the accelerometers have recorded the patient body state to be unchanged for an extended time and the patient is either lying or sitting down then the monitor will assume the patient is sleeping and will then increase the audio volume output of any audio message if necessary to awaken the patient. The monitor may also enable the tactile stimulator to awaken the patient in the event of a critical audio message.


Adjust Display Rotation


The monitor accelerometer can be used to determine the proper rotation of the system display or LCD output on 15. The monitor 15 contains a display that can either be used to deliver a visual message to the patient or for initial patient setup by care givers. For patient visual messages, since the monitor is positioned approximately at the patient's mid section, the display would be upside down (rotated 180 degrees) with respect to the monitor. However, during patient setup, the monitor could be held right side up in front of the skilled personnel. As a result, the display would be right side up. The monitor accelerometer data can be used to adjust the display accordingly depending on how the display is attempting to be read.


Detect Equipment Abuse


Detect equipment abuse during use as well as during shipping. Equipment abuse can be determined by parameters such as number of times dropped and intensity. Detection of equipment abuse can trigger such actions as internal diagnostics, auto download, and equipment service recommendations.


Equipment Drop Detection


If the accelerometers detect a mechanical shock above a pre-determined acceptable threshold then the monitor will record a drop event. Other parameters such as date/time stamp and current operating mode will be recorded as well.


The date/time stamp should allow correlation between the monitor location and the damaging event allowing further information to be obtained using the carrier tracking numbers if such damage occurred during shipping.


If it is not during shipping and is during patient use and there is some form of equipment malfunction after the drop then that could be tied to the root cause of the equipment failure. Such information could be used to advice patients of the types of mechanical shocks that may damage the equipment. It also may be used to improve the robustness of the equipment to survive such forces in the future.


Equipment Service Recommendation


If the monitor accelerometer 16, belt accelerometer 17 have recorded a mechanical shock above a predefined acceptable threshold or if a predefined acceptable number of mechanical shocks has occurred then the monitor will recommend with an audio or visual (display) message via 15 to the patient that the equipment should be serviced. The monitor on 15 will also, during the next download, notify the manufacturer that it should be serviced.


Internal Diagnostics


If the accelerometer does detect an excessive mechanical shock on the belt or monitor then it may initiate internal self-diagnostics. Both the monitor 15 and node 11 have built-in circuitry as shown in FIGS. 3 and 4 to allow most of its components to be tested with self diagnostics.


Auto Download to Manufacturer


If there is a significant mechanical shock to the belt or monitor then the monitor may immediately initiate an automatic download to the manufacturer requesting service.


Monitor Patient Activity Over Time


Accelerometer data can be measured and stored over time to study patient activity. Patient activity data can be used to provide feedback to doctors about a patient's specific condition.


Patient Activity Data and Treatment


After a treatment event, patient activity data taken before, up to, and including the event can be downloaded. This data can be collected among patients and used to make correlations between patient activity derived from accelerometers 16, 17 and the probability of a possible treatment event occurring. These correlations can be used to take precautionary measures with patients who have similar activities as those who had past treatment events.


Patient Activity Data and Doctor Feedback


Patient activity data can be used over a period of time by doctors or data evaluation systems to determine if proper patient activity levels are met. Examples to study would be extremely low patient activity; patient performing recommended exercises; and/or patient real time activity level and corresponding heart rate data. Patients who are experiencing congestive heart failure can be monitored for physical activity and at rest body position. Gradual reduction in patient activity indicated by lack of motion can indicate a worsening of the congestive heart failure condition. Body position at rest can also indicate patient deterioration if body position at rest is primarily vertical since congestive heart failure patients may have difficulty resting in a horizontal position.



FIG. 1 shows the location of the accelerometers 16, 17 with respect to the patient and other system assemblies. The accelerometer 16 located in the front is the monitor accelerometer. The accelerometer located in the back is the belt node 17 accelerometer. The trunk cable 13 allows communication between the belt node computer and the main monitor computer in this embodiment. This permits belt node accelerometer data to be transferred to the main monitor computer 47. In addition, the trunk cable 13 allows monitor power supplies 41, 51 to be used to power the belt node computer and peripherals. The two accelerometers or motion detectors allow the system to determine parameters such as patient body position, patient body movement, patient body acceleration as well as perform certain system self-diagnostics. The monitor can contain either a high-G or a low-G accelerometer. A high-G low-sensitivity accelerometer would allow the system in addition to detect patient and equipment physical shock.



FIG. 3 shows the circuitry used to acquire data from the accelerometer 43 in the monitor (15 on FIG. 1). From the main battery 41, power regulators 42 are used to supply the electronics with needed voltages. The computer 47 controls various system parameters such as accelerometer sensitivity, multiplexer (MUX) 45 channel select, the analog to digital converter (ADC) 46, and serial communications. The Freescale Semiconductor MMA7260Q three axis low-g micromachined accelerometer can be used. The g-select control line between 47 and 43 allow the sensitivity to be varied from, for example, 1.5 g to 6 g. A high-G low sensitivity accelerometer could also be used instead of the MMA7260Q. This can allow patient/equipment shock to be detected. Resistor-capacitor (RC) filtering 44 can be used on each of the accelerometer outputs to minimize clock noise from the accelerometer internal switched capacitor filter circuit. The MUX 45 select lines can be controlled by the computer 47 and may allow each axis output of the accelerometer to be switched to the ADC 46 input. The ADC 46 can also be controlled by the computer 47 via a serial interface.



FIG. 4 is a block diagram that shows circuitry that can be used to acquire data from the accelerometer 52 on the belt node (11 on FIG. 1). Power supplies and regulators 51 derived from the monitor can be used to power the electronics in the belt node. The Freescale Semiconductor MMA7260Q three axis low-g micromachined accelerometer 52 can be used. The belt node computer 54 controls the g-select lines that again can allow the sensitivity to be varied from 1.5 g to 6 g. RC filtering as well as amplitude scaling 53 can be used on each of the accelerometer outputs. An internal MUX and ADC in the belt node computer 54 can allow the accelerometer analog outputs to be interfaced digitally directly to the computer.


An arrhythmia detection algorithm can be implemented by assigning various confidence coefficients or weighting values to the various detectors used by the algorithm. This can be done prior to using the motion detection confidence algorithm. For example, the monitor can be designed with two independent ECG data streams for analysis. The algorithm can execute independent algorithms on each data stream that analyze the signal to extract heart rate, morphology, frequency information, and other information. Additional analysis is performed, independently on each channel, to analyze the signal for noise contamination that may result from patient motion or biological signals such as muscle noise. Other secondary inputs to the basic detection algorithm can include a patient response button and inputs from the accelerometers.


A weighting value can be assigned to each detector, response button and accelerometer and the combination for all detectors can be used to make the decision that a treatable arrhythmia condition exists. In addition, the weighting values can be used to manipulate the timing of therapy delivery.


The action of the algorithm in the presence of a noisy ECG channel could be to place more weight on the heart rate detector in the clean ECG channel. For the accelerometer enhanced confidence algorithm, a weighting could be assigned that would delay delivery of treatment while patient motion is detected as shown in the flow diagram of FIG. 5.


The flow diagram in FIG. 5 shows that if patient motion is detected prior to the detection of a treatable arrhythmia, the timing of treatment delivery can be modified based on the accelerometer 16, 17 inputs when the arrhythmia is detected. If the patient becomes motionless coinciding with the arrhythmia detection, there is an increased confidence that the arrhythmia diagnosis is accurate and the delivery of treatment can occur sooner. If motion continues after the arrhythmia detection, the confidence of a valid detection can be decreased because lethal arrhythmias usually result in a lack of consciousness. In this case, the delivery of treatment can be delayed to allow time for audio voice messages to prompt the patient to respond by pressing the response button. The response button provides a responsiveness test input to the algorithm. In some embodiments, it may be desirable to never deliver a shock to a conscious patient. This algorithm can reduce the possibility of false treatment based on invalid rhythm diagnosis due to corrupt ECG inputs caused by excessive patient movement or other environmental factors.



FIG. 5 shows how a typical algorithm that detects arrhythmia can have an increased confidence by serially feeding through a subsequent confidence algorithm using input from a motion detector or accelerometer(s). Other motion detecting devices or confidence algorithms can use various motion detection criteria as desired by physicians and based upon the treatable condition or patient.


As it will usually be desirable to track and store data in the patient wearable device, the addition of motion data can also be stored, tracked and evaluated. One such use would be to evaluate historical motion/activity levels to detect conditions such as congestive heart failure. Such diseases are often found in patients who would be wearing a cardiac treatment device.

Claims
  • 1. A cardiac monitoring system comprising: cardiac sensing electrodes configured for external placement proximate to a patient and to sense ECG signals of the patient;one or more accelerometers configured to generate patient activity data based on signals corresponding to changes in the patient's body position and movement; anda monitoring computer disposed in the cardiac monitoring system and operationally coupled to the cardiac sensing electrodes and the one or more accelerometers, the monitoring computer configured to correlate the ECG signals of the patient with the patient activity data generated from the one or more accelerometers to determine ECG signal contamination,analyze the ECG signals of the patient to extract at least heart rate data of the patient and detect a cardiac arrhythmia based at least in part on the heart rate data,change a confidence in the detected cardiac arrhythmia based on the determined ECG signal contamination by evaluating the patient activity data for a change in body state during the detection of the cardiac arrhythmia, wherein changing a confidence in the detected cardiac arrhythmia comprisesdetermining that the patient is unconscious if there has been no change in patient body state during the detection of the cardiac arrhythmia,determining that the patient is conscious if there has been a change in patient body state during the detection of the cardiac arrhythmia,increasing the confidence in the detected cardiac arrhythmia based on determining that the patient is unconscious, anddecreasing the confidence in the detected cardiac arrhythmia based on determining that the patient is conscious,wherein at least the patient activity data and the heart rate data are analyzed to determine a change in a physiological condition of the patient over a period of time.
  • 2. The cardiac monitoring system of claim 1, wherein the patient activity data is measured and stored in the monitoring computer over time and provided as feedback to a doctor about the physiological condition of the patient.
  • 3. The cardiac monitoring system of claim 1, wherein the patient activity data is communicated to at least one of a doctor and a data evaluation system to analyze the patient activity data.
  • 4. The cardiac monitoring system of claim 3, wherein the patient activity data is analyzed to determine patient deterioration in a congestive heart failure patient based at least on the patient's body position at rest.
  • 5. The cardiac monitoring system of claim 3, wherein the patient activity data is analyzed to determine whether the patient activity data is indicative of a reduction in patient activity over the period of time.
  • 6. The cardiac monitoring system of claim 3, wherein the patient activity data is analyzed to determine whether the patient activity data is indicative of worsening congestive heart failure.
  • 7. The cardiac monitoring system of claim 1, wherein the one or more accelerometers includes at least two three-axis accelerometers.
  • 8. The cardiac monitoring system of claim 7, wherein one of the at least two three-axis accelerometers is mounted on a patient vest portion of the cardiac monitoring system.
  • 9. The cardiac monitoring system of claim 3, further comprising treatment elements, said treatment elements adjacent said patient in proximity to the patient's skin; andwherein the monitoring computer is operationally coupled to the treatment elements, the monitoring computer configured todetect the cardiac arrhythmia of the patient based on at least one of the patient activity data and the ECG signals of the patient, andprovide a treatment directed to the cardiac arrhythmia via the treatment elements.
  • 10. A wearable defibrillation device comprising: cardiac sensing electrodes configured for external placement proximate to a patient and to sense ECG signals of the patient;one or more therapy pads disposed on the patient;one or more accelerometers configured to generate patient activity data based on signals corresponding to changes in the patient's body position and movement; anda monitoring computer disposed in the wearable defibrillation device, the monitoring computer configured to correlate the ECG signals of the patient with the patient activity data generated from the one or more accelerometers to determine ECG signal contamination,analyze the ECG signals of the patient to extract at least heart rate data of the patient,determine from at least the heart rate data and the patient activity data that a treatable arrhythmia condition exists,increase or decrease a confidence in a determined treatable arrhythmia condition based on the determined ECG signal contamination by evaluating the patient activity data for a change in body state during detection of the treatable arrhythmia condition, wherein increasing or decreasing a confidence in the detected treatable arrhythmia condition comprises determining that the patient is unconscious if there has been no change in patient body state during the detection of the treatable arrhythmia condition,decreasing the confidence in the detected treatable arrhythmia condition based on determining that the patient is conscious, andincreasing the confidence in the detected treatable arrhythmia condition based on determining that the patient is unconscious, andresponsive to an increase in the confidence in the determined treatable arrhythmia condition, deliver a therapeutic shock to the patient through the one or more therapy pads;wherein at least the patient activity data and the heart rate data are further analyzed to determine a change in a physiological condition of the patient over a period of time.
  • 11. The wearable defibrillation device of claim 10, wherein the patient activity data is measured and stored in the monitoring computer over time and provided as feedback to a doctor about the physiological condition of the patient.
  • 12. The wearable defibrillation device of claim 10, wherein the patient activity data is communicated to at least one of a doctor and a data evaluation system to determine whether proper patient activity levels are met.
  • 13. The wearable defibrillation device of claim 12, wherein the patient is a congestive heart failure patient, and wherein the patient activity data is analyzed to determine patient deterioration based at least on the patient's body position at rest.
  • 14. The wearable defibrillation device of claim 12, wherein the patient activity data is analyzed to determine whether the patient activity data is indicative of worsening congestive heart failure.
  • 15. The wearable defibrillation device of claim 10, wherein the one or more accelerometers includes at least two three-axis accelerometers.
  • 16. The wearable defibrillation device of claim 15, wherein one of the at least two three-axis accelerometers is mounted on a patient vest portion of the wearable defibrillation device.
  • 17. A method of monitoring a patient, the method comprising: sensing ECG signals of the patient;generating patient activity data based on signals from one or more accelerometers, the signals corresponding to changes in the patient's body position and movement;correlating the ECG signals of the patient with the patient activity data to determine ECG signal contamination;analyzing the ECG signals of the patient to extract at least heart rate data of the patient and detect a cardiac arrhythmia based at least in part on the heart rate data;changing a confidence in the detected cardiac arrhythmia based on the determined ECG signal contamination by evaluating the patient activity data for a change in body state during the detection of the cardiac arrhythmia, wherein changing a confidence in the detected cardiac arrhythmia comprises determining that the patient is unconscious if there has been no change in patient body state during the detection of the cardiac arrhythmia, and increasing the confidence in the detected cardiac arrhythmia based on determining that the patient is unconscious, anddetermining that the patient is conscious if there has been a change in patient body state during the detection of the cardiac arrhythmia, and decreasing the confidence in the detected cardiac arrhythmia based on determining that the patient is conscious; andanalyzing at least the patient activity data and the heart rate data to determine a change in a physiological condition of the patient over a period of time.
  • 18. The method of monitoring a patient of claim 17, further comprising measuring and storing the patient activity data over the period of time and providing the stored patient activity data to a doctor as feedback about the physiological condition of the patient.
  • 19. The method of monitoring a patient of claim 18, further comprising providing a treatment directed to the physiological condition.
CROSS-REFERENCE TO RELATED APPLICATIONS

This Application is a Continuation of U.S. patent application Ser. No. 15/187,952, titled “Wearable Medical Treatment Device With Motion/Position Detection”, filed on Jun. 21, 2016, which is a Continuation of U.S. patent application Ser. No. 14/180,775 (now U.S. Pat. No. 9,398,859), titled “Wearable Medical Treatment Device With Motion/Position Detection”, filed on Feb. 14, 2014, which is a Continuation of U.S. patent application Ser. No. 12/840,633 (now U.S. Pat. No. 8,676,313), titled “Wearable Medical Treatment Device With Motion/Position Detection”, filed on Jul. 21, 2010, which is a Continuation of U.S. patent application Ser. No. 12/002,469 (now U.S. Pat. No. 7,974,689), titled “Wearable Medical Treatment Device With Motion/Position Detection”, filed on Dec. 17, 2007, each of which is hereby incorporated herein by reference in its entirety. U.S. application Ser. No. 12/002,469 claims the benefit of U.S. Provisional Patent Application Ser. No. 60/934,404 titled “Wearable Medical Treatment Device With Motion/Position Detection”, filed on Jun. 13, 2007. The following U.S. patents are hereby incorporated by reference: U.S. Pat. Nos. 4,928,690, 6,065,154, 5,944,669, 5,741,306, 6,681,003, 6,253,099, and 5,078,134.

US Referenced Citations (179)
Number Name Date Kind
4094310 McEachern et al. Jun 1978 A
4432368 Russek Feb 1984 A
4632122 Johansson et al. Dec 1986 A
4698848 Buckley Oct 1987 A
4928690 Heilman et al. May 1990 A
4978926 Zerod et al. Dec 1990 A
5062834 Gross et al. Nov 1991 A
5078134 Heilman Jan 1992 A
5176380 Evans et al. Jan 1993 A
5330505 Cohen Jul 1994 A
5348008 Bomn et al. Sep 1994 A
5353793 Bomn Oct 1994 A
5357696 Gray et al. Oct 1994 A
5365932 Greenhut Nov 1994 A
5544661 Davis et al. Aug 1996 A
5564429 Bomn et al. Oct 1996 A
5662689 Elsberry et al. Sep 1997 A
5718242 McClure et al. Feb 1998 A
5738102 Lemelson Apr 1998 A
5741306 Glegyak et al. Apr 1998 A
5758443 Pedrazzini Jun 1998 A
5792190 Olson et al. Aug 1998 A
5929601 Kaib et al. Jul 1999 A
5944669 Kaib Aug 1999 A
6016445 Baura Jan 2000 A
6045503 Grabner et al. Apr 2000 A
6049730 Kristbjarnarson Apr 2000 A
6065154 Hulings et al. May 2000 A
6097987 Milani Aug 2000 A
6148233 Owen et al. Nov 2000 A
6169387 Kaib Jan 2001 B1
6169397 Steinbach et al. Jan 2001 B1
6253099 Oskin et al. Jun 2001 B1
6280461 Glegyak et al. Aug 2001 B1
6390996 Halperin et al. May 2002 B1
6406426 Reuss et al. Jun 2002 B1
6666830 Lehrman et al. Dec 2003 B1
6681003 Linder et al. Jan 2004 B2
6690969 Bystrom et al. Feb 2004 B2
6790178 Mault et al. Sep 2004 B1
6804554 Ujhelyi et al. Oct 2004 B2
6827695 Palazzolo et al. Dec 2004 B2
6865413 Halperin et al. Mar 2005 B2
6908437 Bardy Jun 2005 B2
6990373 Jayne et al. Jan 2006 B2
7074199 Halperin et al. Jul 2006 B2
7108665 Halperin et al. Sep 2006 B2
7118542 Palazzolo et al. Oct 2006 B2
7122014 Palazzolo et al. Oct 2006 B2
7149579 Koh et al. Dec 2006 B1
7177684 Kroll et al. Feb 2007 B1
7220235 Geheb et al. May 2007 B2
7277752 Matos Oct 2007 B2
7295871 Halperin et al. Nov 2007 B2
7340296 Stahmann et al. Mar 2008 B2
7427921 Van Woudenberg Sep 2008 B2
7453354 Reiter et al. Nov 2008 B2
7476206 Palazzolo et al. Jan 2009 B2
7488293 Marcovecchio et al. Feb 2009 B2
7702390 Min Apr 2010 B1
7810172 Williams Oct 2010 B2
7831303 Rueter et al. Nov 2010 B2
7974689 Volpe et al. Jul 2011 B2
8121683 Bucher et al. Feb 2012 B2
8140154 Donnelly et al. Mar 2012 B2
8271082 Donnelly et al. Sep 2012 B2
8369944 Macho et al. Feb 2013 B2
8600486 Kaib et al. Dec 2013 B2
8649861 Donnelly et al. Feb 2014 B2
8706215 Kaib et al. Apr 2014 B2
8904214 Volpe et al. Dec 2014 B2
8909335 Radzelovage Dec 2014 B2
9008801 Kalb et al. Apr 2015 B2
9135398 Kaib et al. Sep 2015 B2
9398859 Volpe et al. Jul 2016 B2
20020107435 Swetlik et al. Aug 2002 A1
20030004547 Owen et al. Jan 2003 A1
20030023277 Owen et al. Jan 2003 A1
20030032988 Fincke Feb 2003 A1
20030055460 Owen et al. Mar 2003 A1
20030095648 Kaib et al. May 2003 A1
20030149462 White et al. Aug 2003 A1
20030158593 Heilman et al. Aug 2003 A1
20030174049 Beigel et al. Sep 2003 A1
20030195567 Jayne et al. Oct 2003 A1
20030212311 Nova et al. Nov 2003 A1
20030233129 Matos Dec 2003 A1
20040007970 Ma et al. Jan 2004 A1
20040049233 Edwards Mar 2004 A1
20040133079 Mazar Jul 2004 A1
20040143297 Ramsey Jul 2004 A1
20050049515 Misczynski et al. Mar 2005 A1
20050131465 Freeman et al. Jun 2005 A1
20060017575 McAdams Jan 2006 A1
20060036292 Smith et al. Feb 2006 A1
20060085049 Cory et al. Apr 2006 A1
20060129067 Grajales et al. Jun 2006 A1
20060178706 Lisogurski et al. Aug 2006 A1
20060220809 Stigall et al. Oct 2006 A1
20060264776 Stahmann et al. Nov 2006 A1
20060270952 Freeman et al. Nov 2006 A1
20070073120 Li et al. Mar 2007 A1
20070118056 Wang et al. May 2007 A1
20070129769 Bourget et al. Jun 2007 A1
20070161913 Farrell et al. Jul 2007 A1
20070169364 Townsend et al. Jul 2007 A1
20070239220 Greenhut et al. Oct 2007 A1
20070265671 Roberts et al. Nov 2007 A1
20070299474 Brink Dec 2007 A1
20080004536 Baxi et al. Jan 2008 A1
20080015454 Gal Jan 2008 A1
20080030656 Watson et al. Feb 2008 A1
20080031270 Tran et al. Feb 2008 A1
20080033495 Kumar Feb 2008 A1
20080045815 Derchak et al. Feb 2008 A1
20080058884 Matos Mar 2008 A1
20080221631 Dupelle Sep 2008 A1
20080249591 Gaw et al. Oct 2008 A1
20080287770 Kurzweil et al. Nov 2008 A1
20080306562 Donnelly et al. Dec 2008 A1
20080312520 Rowlandson et al. Dec 2008 A1
20080312709 Volpe et al. Dec 2008 A1
20090073991 Landrum et al. Mar 2009 A1
20090076336 Mazar et al. Mar 2009 A1
20090076340 Libbus et al. Mar 2009 A1
20090076341 James et al. Mar 2009 A1
20090076342 Amurthur et al. Mar 2009 A1
20090076343 James et al. Mar 2009 A1
20090076344 Libbus et al. Mar 2009 A1
20090076345 Manicka et al. Mar 2009 A1
20090076346 James et al. Mar 2009 A1
20090076348 Manicka et al. Mar 2009 A1
20090076349 Libbus et al. Mar 2009 A1
20090076350 Bly et al. Mar 2009 A1
20090076363 Bly et al. Mar 2009 A1
20090076364 Libbus et al. Mar 2009 A1
20090076397 Libbus et al. Mar 2009 A1
20090076405 Amurthur et al. Mar 2009 A1
20090076410 Libbus et al. Mar 2009 A1
20090076559 Libbus et al. Mar 2009 A1
20090082691 Denison et al. Mar 2009 A1
20090093687 Telfort et al. Apr 2009 A1
20090138059 Ouwerkerk May 2009 A1
20090177100 Ternes Jul 2009 A1
20090234410 Libbus et al. Sep 2009 A1
20090264792 Mazar Oct 2009 A1
20090275848 Brockway et al. Nov 2009 A1
20090287120 Ferren et al. Nov 2009 A1
20090292194 Libbus et al. Nov 2009 A1
20100056881 Libbus et al. Mar 2010 A1
20100069735 Berkner Mar 2010 A1
20100076513 Warren et al. Mar 2010 A1
20100076533 Dar et al. Mar 2010 A1
20100083968 Wondka et al. Apr 2010 A1
20100179438 Heneghan et al. Jul 2010 A1
20100234715 Shin et al. Sep 2010 A1
20100234716 Engel Sep 2010 A1
20100295674 Hsieh et al. Nov 2010 A1
20100298899 Donnelly et al. Nov 2010 A1
20100312297 Volpe et al. Dec 2010 A1
20110077728 Li et al. Mar 2011 A1
20110196220 Storm Aug 2011 A1
20110288604 Kaib et al. Nov 2011 A1
20110288605 Kaib et al. Nov 2011 A1
20120011382 Volpe et al. Jan 2012 A1
20120112903 Kaib et al. May 2012 A1
20120144551 Guldalian Jun 2012 A1
20120146797 Oskin et al. Jun 2012 A1
20120158074 Hall Jun 2012 A1
20120158075 Kaib et al. Jun 2012 A1
20130325078 Whiting et al. Dec 2013 A1
20130325096 Dupelle et al. Dec 2013 A1
20140163334 Volpe et al. Jun 2014 A1
20140371806 Raymond et al. Dec 2014 A1
20150005588 Herken et al. Jan 2015 A1
20150039053 Kaib et al. Feb 2015 A1
20150217121 Subramanian et al. Aug 2015 A1
20150231403 Kaib et al. Aug 2015 A1
20160143585 Donnelly et al. May 2016 A1
Foreign Referenced Citations (38)
Number Date Country
101031334 Sep 2007 CN
101657229 Feb 2010 CN
101848677 Sep 2010 CN
0295497 Sep 1993 EP
0335356 Mar 1996 EP
1642616 Apr 2006 EP
1455640 Jan 2008 EP
1720446 Jul 2010 EP
S6368135 Mar 1988 JP
5115450 May 1993 JP
H07541 Jan 1995 JP
H10-28679 Feb 1998 JP
H11319119 Nov 1999 JP
2002-102361 Apr 2002 JP
2002200059 Jul 2002 JP
2002534231 Oct 2002 JP
2003235997 Aug 2003 JP
2004538066 Dec 2004 JP
2005275606 Oct 2005 JP
2007531592 Nov 2007 JP
2008302228 Dec 2008 JP
2009510276 Mar 2009 JP
2009518057 May 2009 JP
2009528909 Aug 2009 JP
2010530114 Sep 2010 JP
200002484 Jan 2000 WO
2004054656 Jul 2004 WO
2004067083 Aug 2004 WO
2005082454 Sep 2005 WO
2006050235 May 2006 WO
2007019325 Feb 2007 WO
20070057169 May 2007 WO
2007077997 Jul 2007 WO
2008137286 Nov 2008 WO
2009034506 Mar 2009 WO
2010014497 Feb 2010 WO
2010025432 Mar 2010 WO
2015127466 Aug 2015 WO
Non-Patent Literature Citations (7)
Entry
American Journal of Respiratory and Critical Care Medicine, vol. 166, pp. 111-117 (2002). American Thoracic Society, ATS Statement Guidelines for the Six-Minute Walk Test, available at http://ajrccm.atsjournals.org/cgi/content/full/166/1/111.
DeBock et al., “Captopril treatment of chronic heart failure in the very old,” J. Gerontol. (1994) 49: M148-M152.
Extended European Search Report from corresponding European Application No. 11804401.5 dated May 18, 2016.
Harnett, P.R. et al., “A Survey and Comparison of Laboratory Test Methods for Measuring Wicking”, Textile Research Journal, Jul. 1984.
International Search Report dated Nov. 21, 2011 from corresponding International Application No. PCT/US11/43360.
O'Keeffe et al., “Reproducability and responsiveness of quality of the assessment and six minute walk test in elderly heart failure patients,” Heart (1998) 80: 377-382.
Search Report and Written Opinion from French counterpart application No. 0853856, dated Jan. 4, 2011.
Related Publications (1)
Number Date Country
20180110419 A1 Apr 2018 US
Provisional Applications (1)
Number Date Country
60934404 Jun 2007 US
Continuations (4)
Number Date Country
Parent 15187952 Jun 2016 US
Child 15850435 US
Parent 14180775 Feb 2014 US
Child 15187952 US
Parent 12840633 Jul 2010 US
Child 14180775 US
Parent 12002469 Dec 2007 US
Child 12840633 US