Cardiac Monitoring System with Sleep and Activity Correlation

A processing device, comprising:

• one or more processors;
• and
• memory having stored computer-readable instructions that are executable by the one or more processors to perform operations comprising:
• obtaining measurements of a user generated by a wearable monitoring device during an observation period, the measurements including accelerometer data and electrical potential measurements;
• detecting sleep periods and wake periods based on the accelerometer data;
• generating sleep stage classifications based on the accelerometer data during the sleep periods;
• generating activity level classifications based on detected steps during the wake periods;
• processing the electrical potential measurements using a machine learning model trained to correlate patterns in the electrical potential measurements to cardiac rhythm classifications;
• correlating the sleep stage classifications and the activity level classifications with concurrent cardiac rhythm classifications to generate arrhythmia correlations;
• generating a cardiac wellness prediction based on the arrhythmia correlations;
• and
• outputting a health report including the cardiac wellness prediction.

A system comprising:

• a wearable monitoring device that is wearable by a user to detect one or more measurements of the user during an observation period, the one or more measurements including accelerometer measurements and electrical potential measurements of a heart of the user;
• and
• a computing device configured to:
• receive the one or more measurements from the wearable monitoring device;
• identify exercise periods performed by the user during the observation period based on the accelerometer measurements;
• process the electrical potential measurements using a machine learning model trained to correlate patterns in the electrical potential measurements to cardiac rhythm classifications;
• generate arrhythmia correlations for periods before, during, and after the exercise periods based on concurrent cardiac rhythm classifications;
• and
• generate a cardiac wellness prediction based on the arrhythmia correlations.