Visualization of arrhythmia detection by machine learning

Abstract

Techniques are disclosed for explaining and visualizing an output of a machine learning system that detects cardiac arrhythmia in a patient. In one example, a computing device receives cardiac electrogram data sensed by a medical device. The computing device applies 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 and a level of confidence in the determination that the episode of arrhythmia has occurred in the patient. In response to determining that the level of confidence is greater than a predetermined threshold, the computing device displays, to a user, a portion of the cardiac electrogram data, an indication that the episode of arrhythmia has occurred, and an indication of the level of confidence that the episode of arrhythmia has occurred.

What is claimed is: 
     
         1 . A computing device comprising:
 a storage medium; and   processing circuitry operably coupled to the storage medium and configured to:
 receive cardiac electrogram data 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; and 
 determine a level of confidence in the determination that the episode of arrhythmia has occurred in the patient; 
 
 determine that the level of confidence in the determination that the episode of arrhythmia has occurred in the patient is greater than a predetermined threshold; and 
 in response to determining that the level of confidence is greater than the predetermined threshold, output for display to a user an indication that the episode of arrhythmia has occurred in the patient. 
   
     
     
         2 . The computing device of  claim 1 , wherein the processing circuitry is further configured to:
 output for display to the user, at least a portion of the cardiac electrogram data,   wherein the at least a portion of the cardiac electrogram data comprises an electrocardiogram (ECG) waveform, and   wherein the indication that the episode of arrhythmia has occurred in the patient comprises an annotation to the ECG waveform.   
     
     
         3 . The computing device of  claim 1 , wherein the processing circuitry is further configured to:
 output for display to the user, an indication of the level of confidence that the episode of arrhythmia has occurred in the patient,   wherein the indication of the level of confidence comprises one or more of a color, an image, a light, a sound, or a textual notification.   
     
     
         4 . The computing device of  claim 1 , wherein the predetermined threshold comprises a first predetermined threshold, wherein the processing circuitry is further configured to:
 in response to determining that the level of confidence is between the first predetermined threshold and a second predetermined threshold, output for display to a user the indication that the episode of arrhythmia has occurred in the patient.   
     
     
         5 . The computing device of  claim 1 , wherein the processing circuitry is further configured to:
 in response to determining that the level of confidence is greater than the predetermined threshold, output for display to the user the indication that the episode of arrhythmia has occurred in the patient, at least a portion of the cardiac electrogram data, an indication of the level of confidence that the episode of arrhythmia has occurred in the patient;   receive a user input from the user via a user input device, wherein the user input corresponds to an arrhythmia type from a plurality of different arrhythmia types; and   update one or more of the indication that the episode of arrhythmia has occurred in the patient, the at least a portion of the cardiac electrogram data, or the indication of the level of confidence that the episode of arrhythmia has occurred in the patient to correspond to the arrhythmia type.   
     
     
         6 . The computing device of  claim 1 , wherein the processing circuitry is further configured to:
 determine, from a plurality of confidence intervals, that the level of confidence corresponds to a second interval, wherein the plurality of confidence intervals includes at least a first interval, the second interval, and a third interval, wherein the first interval corresponds to higher confidence levels than the second interval and higher confidence levels than the third interval, and wherein the second interval corresponds to higher confidence intervals than the third interval.   
     
     
         7 . The computing device of  claim 1 , wherein the episode of arrhythmia in the patient is at least one of an episode of bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block. 
     
     
         8 . The computing device 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 in a patient of the plurality of patients. 
     
     
         9 . The computing device of  claim 1 , wherein to apply the machine learning model to the received cardiac electrogram data, the processing circuitry is further configured to apply the machine learning model to at least one of:
 electrocardiogram (ECG) data of the patient;   characteristics correlated to arrhythmia in the patient;   a type of the 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.   
     
     
         10 . The computing device of  claim 1 , wherein to output the at least a portion of the cardiac electrogram data, the processing circuitry is further configured to:
 identify a subsection of an electrocardiogram (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; and   output the subsection of the ECG.   
     
     
         11 . A method of operating a computing device, the method comprising:
 receiving, by the computing device comprising processing circuitry and a storage medium, cardiac electrogram data 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; and 
 determine a level of confidence in the determination that the episode of arrhythmia has occurred in the patient; 
   determining, by the computing device, that the level of confidence in the determination that the episode of arrhythmia has occurred in the patient is greater than a predetermined threshold; and   in response to determining that the level of confidence is greater than the predetermined threshold, outputting, by the computing device and for display to a user an indication that the episode of arrhythmia has occurred in the patient.   
     
     
         12 . The method of  claim 11 , further comprising:
 outputting, by the computing device and for display to the user, at least a portion of the cardiac electrogram data,   wherein the at least a portion of the cardiac electrogram data comprises an electrocardiogram (ECG) waveform, and   wherein the indication that the episode of arrhythmia has occurred in the patient comprises an annotation to the ECG waveform.   
     
     
         13 . The method of  claim 11 , further comprising:
 outputting, by the computing device and for display to the user, an indication of the level of confidence that the episode of arrhythmia has occurred in the patient,   wherein the indication of the level of confidence comprises one or more of a color, an image, a light, a sound, or a textual notification.   
     
     
         14 . The method of  claim 11 , further comprising:
 in response to determining that the level of confidence is between the predetermined threshold and a second predetermined threshold, outputting, by the computing device and for display to a user the indication that the episode of arrhythmia has occurred in the patient.   
     
     
         15 . The method of  claim 11 , further comprising:
 in response to determining that the level of confidence is greater than the predetermined threshold, outputting, by the computing device and for display to the user the indication that the episode of arrhythmia has occurred in the patient, at least a portion of the cardiac electrogram data, an indication of the level of confidence that the episode of arrhythmia has occurred in the patient;   receiving a user input from the user via a user input device, wherein the user input corresponds to an arrhythmia type from a plurality of different arrhythmia types; and   updating one or more of the indication that the episode of arrhythmia has occurred in the patient, the at least a portion of the cardiac electrogram data, or the indication of the level of confidence that the episode of arrhythmia has occurred in the patient to correspond to the arrhythmia type.   
     
     
         16 . The method of  claim 11 , further comprising:
 determining, from a plurality of confidence intervals, that the level of confidence corresponds to a second interval, wherein the plurality of confidence intervals includes at least a first interval, the second interval, and a third interval, wherein the first interval corresponds to higher confidence levels than the second interval and higher confidence levels than the third interval, and wherein the second interval corresponds to higher confidence intervals than the third interval.   
     
     
         17 . The method of  claim 11 , wherein the episode of arrhythmia in the patient is at least one of an episode of bradycardia, tachycardia, atrial fibrillation, ventricular fibrillation, or AV Block. 
     
     
         18 . The method of  claim 11 , 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 in a patient of the plurality of patients. 
     
     
         19 . The method of  claim 11 , wherein applying the machine learning model to the received cardiac electrogram data comprises applying the machine learning model to at least one of:
 electrocardiogram (ECG) data of the patient;   characteristics correlated to arrhythmia in the patient;   a type of the 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.   
     
     
         20 . The method of  claim 11 , wherein outputting the at least a portion of the cardiac electrogram data comprises:
 identifying a subsection of an electrocardiogram (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; and   outputting the subsection of the ECG.