Application US20200357519

REDUCED POWER MACHINE LEARNING SYSTEM FOR ARRHYTHMIA DETECTION
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US20200357519

1. A method comprising: (11) (5)

sensing, by a medical device comprising processing circuitry and a storage medium, cardiac electrogram data of a patient;

performing, by the medical device, feature-based delineation of the sensed cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data and indicative of an episode of arrhythmia in the patient;

determining, by the medical device and based on the feature-based delineation, that the cardiac features satisfy threshold criteria for application of a machine learning model for verifying that the episode of arrhythmia has occurred in the patient;

in response to determining that the cardiac features satisfy the threshold criteria for application of the machine learning model, applying, by the medical device, the machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to verify, based on the machine learning model, that the episode of arrhythmia has occurred in the patient; and

in response to verifying, by the machine learning model, that the episode of arrhythmia has occurred in the patient: generating, by the medical device, a report comprising an indication that the episode of arrhythmia has occurred in the patient and one or more of the cardiac features that coincide with the episode of arrhythmia; and

outputting, by the medical device and for display, the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia.

2. The method of claim 1, (0) (4)

wherein the episode of arrhythmia in the patient is an episode of arrhythmia of a first classification in the patient,

wherein applying the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred in the patient comprises applying the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia of the first classification has occurred in the patient,

wherein the method further comprises applying the machine learning model to the sensed cardiac electrogram data to determine that an episode of arrhythmia of a second classification has occurred in the patient in response to determining, based on the feature-based delineation, that the episode of arrhythmia of the first classification has occurred in the patient, and

wherein generating the report comprising the indication that the episode of arrhythmia has occurred in the patient and one or more of the cardiac features that coincide with the episode of arrhythmia comprises generating a report comprising an indication that the episode of arrhythmia of the first classification has occurred in the patient, an indication that the episode of arrhythmia of the second classification has occurred in the patient, and the one or more of the cardiac features that coincide with the episode of arrhythmia of the first classification.

3. The method of claim 1, wherein determining that the cardiac features satisfy the threshold criteria comprises determining that at least one of a physiological parameter of the patient or a parameter of the medical device satisfies the threshold criteria. (0)

4. The method of claim 1, wherein applying the machine learning model to the sensed cardiac electrogram data comprises applying the machine learning model to the sensed cardiac electrogram data in response to determining that the cardiac features satisfy the threshold criteria and determining that a noise of at least one of the cardiac features is less than a predetermined threshold. (0)

5. The method of claim 1, wherein applying the machine learning model to the sensed cardiac electrogram data comprises applying the machine learning model to the sensed cardiac electrogram data in response to: (0) (2)

6. The method of claim 1, wherein the method further comprises, in response to outputting the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia: (0) (2)

7. The method of claim 1, (0) (2)

8. The method of claim 1, (0) (2)

9. The method of claim 1, further comprising determining a burden of the episode of arrhythmia in the patient in response to verifying that the episode of arrhythmia has occurred in the patient, wherein the burden of the episode of arrhythmia in the patient comprises a ratio of a length of time of the episode of arrhythmia to a length of time during of monitoring of the patient by the medical device. (0)

10. The method of claim 1, (1) (2)

wherein the method further comprises processing, by the medical device, the sensed cardiac electrogram data to generate filtered cardiac electrogram data,

wherein applying the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred in the patient comprises applying the machine learning model to the filtered cardiac electrogram data to verify that the episode of arrhythmia has occurred in the patient.

11. The method of claim 10, wherein processing the sensed cardiac electrogram data to generate the filtered cardiac electrogram data comprises: (1) (2)

determining at least one of a period of time of a high level of activity of the patient or a period of time of highly-varying input impedance of the medical device;

in response to determining the at least one of the period of time of the high level of activity of the patient or the period of time of highly-varying input impedance of the medical device, discarding at least a portion of the sensed cardiac electrogram data that coincides with the at least one of the period of time of the high level of activity of the patient or the period of time of highly-varying input impedance of the medical device to generate the filtered cardiac electrogram data.

12. The method of claim 11, wherein discarding at least a portion of the sensed cardiac electrogram data that coincides with the at least one of the period of time of the high level of activity of the patient or the period of time of highly-varying input impedance of the medical device comprises discarding one or more of a sensed input impedance of the medical device, a sensed activity level of the patient, or a sensed posture change of the patient that coincides with the at least one of the period of time of the high level of activity of the patient or the period of time of highly-varying input impedance of the medical device. (0)

13. The method of claim 1, further comprising: (0) (3)

wherein applying the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred in the patient comprises at least one of a first determination, based on the machine learning model, that the episode of arrhythmia has not occurred in the patient or a second determination, based on the machine learning model, that an episode of arrhythmia of a different type has occurred in the patient;

in response to the at least one of the first determination and the second determination, updating, by the medical device, a counter of incorrectly detected episodes of arrhythmia in the patient; and

in response to determining that a value of the counter is greater than a predetermined threshold, switching from performing feature-based delineation of the sensed cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data and indicative of an episode of arrhythmia in the patient to applying a second machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to obtain, based on the machine learning model, cardiac features present in the cardiac electrogram data and indicative of an episode of arrhythmia in the patient.

14. The method of claim 1, (0) (2)

15. A medical device comprising: (5) (2)

storage medium; and

processing circuitry operably coupled to the storage medium and configured to: sense cardiac electrogram data of a patient;

perform feature-based delineation of the sensed cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data and indicative of an episode of arrhythmia in the patient;

determine, based on the feature-based delineation, that the cardiac features satisfy threshold criteria for application of a machine learning model for verifying that the episode of arrhythmia has occurred in the patient;

in response to determining that the cardiac features satisfy the threshold criteria for application of the machine learning model, apply the machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to verify, based on the machine learning model, that the episode of arrhythmia has occurred in the patient; and

in response to verifying, by the machine learning model, that the episode of arrhythmia has occurred in the patient: generate a report comprising an indication that the episode of arrhythmia has occurred in the patient and one or more of the cardiac features that coincide with the episode of arrhythmia; and

output, for display, the report comprising the indication that the episode of arrhythmia has occurred in the patient and the one or more of the cardiac features that coincide with the episode of arrhythmia.

16. The medical device of claim 15, (0) (4)

wherein the episode of arrhythmia in the patient is an episode of arrhythmia of a first classification in the patient,

wherein to apply the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred in the patient, the processing circuitry is configured to apply the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia of the first classification has occurred in the patient,

wherein the processing circuitry is further configured to apply the machine learning model to the sensed cardiac electrogram data to determine that an episode of arrhythmia of a second classification has occurred in the patient in response to determining, based on the feature-based delineation, that the episode of arrhythmia of the first classification has occurred in the patient, and

wherein to generate the report comprising the indication that the episode of arrhythmia has occurred in the patient and one or more of the cardiac features that coincide with the episode of arrhythmia, the processing circuitry is configured to generate a report comprising an indication that the episode of arrhythmia of the first classification has occurred in the patient, an indication that the episode of arrhythmia of the second classification has occurred in the patient, and the one or more of the cardiac features that coincide with the episode of arrhythmia of the first classification.

17. The medical device of claim 15, wherein to determine that the cardiac features satisfy the threshold criteria, the processing circuitry is configured to determine that at least one of a physiological parameter of the patient or a parameter of the medical device satisfies the threshold criteria. (0)

18. The medical device of claim 15, wherein to apply the machine learning model to the sensed cardiac electrogram data, the processing circuitry is configured to apply the machine learning model to the sensed cardiac electrogram data in response to determining that the cardiac features satisfy the threshold criteria and determining that a noise of at least one of the cardiac features is less than a predetermined threshold. (0)

19. The medical device of claim 15, wherein to apply the machine learning model to the sensed cardiac electrogram data, the processing circuitry is configured to apply the machine learning model to the sensed cardiac electrogram data in response to: (0) (2)

20. The medical device of claim 15, (0) (2)

21. A medical device comprising: (4) (2)

storage medium; and

processing circuitry operably coupled to the storage medium and configured to: sense cardiac electrogram data of a patient;

perform feature-based delineation of the sensed cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data;

determine a similarity of the obtained cardiac features to cardiac features of each entry of a plurality of entries of an arrhythmia dictionary of the medical device, wherein each entry of the plurality of entries of the arrhythmia dictionary comprises a classification of arrhythmia of a plurality of classifications of arrhythmia in the patient and cardiac features that demonstrate the classification of arrhythmia;

in response to determining that the obtained cardiac features are not similar to the cardiac features of each entry of the plurality of entries of the arrhythmia dictionary, apply a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the sensed cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia of a first classification has occurred in the patient; and

store, in the arrhythmia dictionary, a first entry comprising the first classification of the episode of arrhythmia and the obtained cardiac features.

22. The medical device of claim 21, wherein, after storing the entry comprising the first classification of the episode of arrhythmia and the obtained cardiac features, the processing circuitry is further configured to: (0) (2)

23. The medical device of claim 21, wherein the processing circuitry is further configured to: (0) (4)

sense second cardiac electrogram data of a patient;

perform feature-based delineation of the sensed second cardiac electrogram data to obtain second cardiac features present in the second cardiac electrogram data;

determine the second cardiac features are similar to the cardiac features of the first entry of the plurality of entries of the arrhythmia dictionary of the medical device;

in response to determining that the second cardiac features are similar to the cardiac features of the first entry of the plurality of entries of the arrhythmia dictionary: generate a report comprising an indication that the episode of arrhythmia of the first classification has occurred in the patient and one or more of the second cardiac features that coincide with the episode of arrhythmia of the first classification; and

output, for display, the report comprising the indication that the episode of arrhythmia of the first classification has occurred in the patient and the one or more of the second cardiac features that coincide with the episode of arrhythmia of the first classification.

24. The medical device of claim 21, wherein to determine a similarity of the obtained cardiac features to the cardiac features of each entry of the plurality of entries of the arrhythmia dictionary of the medical device, the processing circuitry is configured to determine that a difference between at least one parameter of the obtained cardiac features and at least one parameter of the cardiac features of each entry of the plurality of entries of the arrhythmia dictionary of the medical device is greater than a predetermined threshold. (0)

25. The medical device of claim 21, wherein in response to determining that the obtained cardiac features are similar to cardiac features of a first entry of the plurality of entries of the arrhythmia dictionary of the medical device, the processing circuitry is configured to: (0) (2)

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Application US20200357519

REDUCED POWER MACHINE LEARNING SYSTEM FOR ARRHYTHMIA DETECTION

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