Machine learning based depolarization identification and arrhythmia localization visualization

A medical device system comprising:

• a medical device configured to:
• sense a cardiac electrogram of a patient via a plurality of electrodes;
• and
• store episode data for an episode, wherein the episode is associated with a period of time, and the episode data comprises the cardiac electrogram sensed by the medical device during the period of time;
• and
• processing circuitry configured to:
• receive the episode data;
• apply one or more arrhythmia classification machine learning models to the episode data, the one or more arrhythmia classification machine learning models configured to output a respective arrhythmia type likelihood value for each of a plurality of arrhythmia type classifications, each of the arrhythmia type likelihood values representing a likelihood that the respective arrhythmia type classification occurred at any point during the period of time;
• apply one or more depolarization detection machine learning models to the episode data, the one or more depolarization detection machine learning models configured to output a set of depolarization likelihood values, each of the depolarization likelihood values of the set representing a likelihood that a depolarization occurred at a respective time during the period of time;
• identify one or more depolarizations during the episode based on the arrhythmia type likelihood values and depolarization likelihood values;
• and
• display, to a user, at least one of the arrhythmia type likelihood values and an indication of the one or more identified depolarizations over the period of time.

A non-transitory computer-readable medium comprising instructions that, when executed by processing circuitry of a computing system, cause the computing system to:

• receive episode data for an episode stored by a medical device of a patient, wherein the episode is associated with a period of time, and the episode data comprises a cardiac electrogram sensed by the medical device during the period of time;
• apply one or more arrhythmia classification machine learning models to the episode data, the one or more arrhythmia classification machine learning models configured to output a respective arrhythmia type likelihood value for each of a plurality of arrhythmia type classifications, each of the arrhythmia type likelihood values representing a likelihood that the respective arrhythmia type classification occurred at any point during the period of time;
• apply one or more depolarization detection machine learning models to the episode data, the one or more depolarization detection machine learning models configured to output a set of depolarization likelihood values, each of the depolarization likelihood values of the set representing a likelihood that a depolarization occurred at a respective time during the period of time;
• identify one or more depolarizations during the episode based on the arrhythmia type likelihood values and depolarization likelihood values;
• and
• display at least one of the arrhythmia type likelihood values and an indication of the one or more identified depolarizations over the period of time.