Arrythmia detection with feature delineation and machine learning

Abstract

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

What is claimed is: 
     
         1 . A method comprising:
 receiving, by a computing device comprising processing circuitry and a storage medium, cardiac electrogram data of a patient sensed by a medical device;   applying, by the computing device, a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient;   performing, by the computing device, feature-based delineation of the received cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data;   in response to determining that the episode of arrhythmia has occurred in the patient:
 generating, by the computing 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 computing 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 , wherein performing feature-based delineation of the cardiac electrogram data to obtain the cardiac features present in the cardiac electrogram data comprises performing at least one of QRS detection, refractory processing, noise processing, or delineation of the cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data. 
     
     
         3 . The method of  claim 1 , wherein applying the machine learning model to determine that the episode of arrhythmia has occurred in the patient comprises applying the machine learning model to determine that an episode of at least one of bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block has occurred in the patient. 
     
     
         4 . The method of  claim 1 , wherein the cardiac features present in the cardiac electrogram data are one or more of a mean heartrate of the patient, a minimum heartrate of the patient, a maximum heartrate of the patient, a PR interval of a heart of the patient, a variability of heartrate of the patient, one or more amplitudes of one or more features of an electrocardiogram (ECG) of the patient, or an interval between the or more features of the ECG of the patient. 
     
     
         5 . The method of  claim 1 , wherein the machine learning model trained using cardiac electrogram data for the plurality of patients comprises a machine learning model trained using a plurality of electrocardiogram (ECG) waveforms, each ECG waveform labeled with one or more episodes of arrhythmia of one or more types in a patient of the plurality of patients. 
     
     
         6 . The method of  claim 1 , wherein applying the machine learning model to the received cardiac electrogram data further comprises applying the machine learning model to at least one of:
 one or more characteristics of the received cardiac electrogram data correlated to arrhythmia in the patient;   an activity level of the medical device;   an input impedance of the medical device; or   a battery level of the medical device.   
     
     
         7 . 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:
 receiving, by the computing device and from a user, an adjustment to the feature-based delineation of the cardiac electrogram data; and   performing, in accordance with the adjustment, feature-based delineation of the cardiac electrogram data to obtain second cardiac features present in the cardiac electrogram data.   
     
     
         8 . The method of  claim 1 ,
 wherein the cardiac electrogram data of the patient comprises an electrocardiogram (ECG) of the patient, and   wherein generating 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 comprises:
 identifying a subsection of the ECG of the patient, wherein the subsection comprises ECG data for a first time period prior to the episode of arrhythmia, a second time period during the episode of arrhythmia, and a third time period after the episode of arrhythmia, and wherein a length of time of the ECG of the patient is greater than the first, second, and third time periods; 
 identifying one or more of the cardiac features that coincide with the first, second, and third time periods; and 
 including, in the report, the subsection of the ECG and the one or more of the cardiac features that coincide with the first, second, and third time periods. 
   
     
     
         9 . The method of  claim 1 ,
 wherein the method further comprises processing, by the computing device, the received cardiac electrogram data to generate an intermediate representation of the received cardiac electrogram data,   wherein applying the machine learning model, trained using cardiac electrogram data for the plurality of patients, to the received cardiac electrogram data to determine that the episode of arrhythmia has occurred in the patient comprises applying a machine learning model, trained using intermediate representations of cardiac electrogram data for a plurality of patients, to the intermediate representation of the received cardiac electrogram data and the cardiac features present in the cardiac electrogram data to determine, based on the machine learning model, that the episode of arrhythmia has occurred in the patient.   
     
     
         10 . The method of  claim 9 , wherein processing the received cardiac electrogram data to generate the intermediate representation of the received cardiac electrogram data comprises at least one of:
 applying a filter to the received cardiac electrogram data;   performing signal decomposition on the received cardiac electrogram data.   
     
     
         11 . The method of  claim 10 , wherein performing signal decomposition on the received cardiac electrogram data comprises performing wavelet decomposition on the received cardiac electrogram data. 
     
     
         12 . A method comprising:
 receiving, by a computing device comprising processing circuitry and a storage medium, cardiac electrogram data of a patient sensed by a medical device;   obtaining, by the computing device, a first classification of arrhythmia in the patient determined by feature-based delineation of the received cardiac electrogram data, wherein the feature-based delineation identifies cardiac features present in the cardiac electrogram data;   applying, by the computing device, a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, a second classification of arrhythmia in the patient;   determining, by the computing device and based on the first classification and second classification, that an episode of arrhythmia has occurred in the patient; and   in response to determining that the episode of arrhythmia has occurred in the patient:
 generating, by the computing 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 computing 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. 
   
     
     
         13 . The method of  claim 12 , wherein determining, based on the first classification and second classification, that the episode of arrhythmia has occurred in the patient comprises:
 determining, by the computing device, a degree of similarity of the first classification and the second classification; and   based on the degree of similarity of the first classification and the second classification, determining, by the computing device, that the episode of arrhythmia has occurred in the patient.   
     
     
         14 . The method of  claim 12 ,
 wherein applying the machine learning model to the received cardiac electrogram data to determine the second classification of arrhythmia in the patient comprises applying the machine learning model to the received cardiac electrogram data and the cardiac features identified by the feature-based delineation of the received cardiac electrogram data to determine the second classification of arrhythmia in the patient; and   wherein determining, based on the first classification and second classification, that the episode of arrhythmia has occurred in the patient comprises:
 determining that the first classification is indicative that the episode of arrhythmia has occurred in the patient; and 
 in response determining that the first classification is indicative that the episode of arrhythmia has occurred in the patient, determining that the second classification verifies that the episode of arrhythmia has occurred in the patient; and 
 in response to determining that the second classification verifies that the episode of arrhythmia has occurred in the patient, determining that the episode of arrhythmia has occurred in the patient. 
   
     
     
         15 . The method of  claim 12 , wherein obtaining, by the computing device, the first classification of arrhythmia in the patient determined by feature-based delineation of the received cardiac electrogram data comprises performing, by the computing device, feature-based delineation of the received cardiac electrogram data to determine the first classification of arrhythmia in the patient. 
     
     
         16 . The method of  claim 12 , wherein obtaining, by the computing device, the first classification of arrhythmia in the patient determined by feature-based delineation of the received cardiac electrogram data comprises receiving, by the computing device and from the medical device, the first classification of arrhythmia in the patient determined by feature-based delineation by the medical device of the received cardiac electrogram data. 
     
     
         17 . The method of  claim 12 , wherein obtaining the first classification of arrhythmia in the patient determined by feature-based delineation of the received cardiac electrogram data comprises obtaining the first classification of arrhythmia in the patient determined by at least one of QRS detection, refractory processing, noise processing, or delineation of the cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data. 
     
     
         18 . The method of  claim 12 , wherein applying the machine learning model to determine the second classification of arrhythmia in the patient comprises applying the machine learning model to determine that an episode of at least one of bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block has occurred in the patient. 
     
     
         19 . The method of  claim 12 , wherein the cardiac features present in the cardiac electrogram data are one or more of a mean heartrate of the patient, a minimum heartrate of the patient, a maximum heartrate of the patient, a PR interval of a heart of the patient, a variability of heartrate of the patient, one or more amplitudes of one or more features of an electrocardiogram (ECG) of the patient, or an interval between the or more features of the ECG of the patient. 
     
     
         20 . A computing device comprising processing circuitry and a storage medium, wherein the processing circuitry is configured to:
 receive cardiac electrogram data of a patient sensed by a medical device;   apply a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient;   perform feature-based delineation of the received cardiac electrogram data to obtain cardiac features present in the cardiac electrogram data;   in response to determining 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.