Passive arrhythmias detection based on photoplethysmogram (ppg) inter-beat intervals and morphology

A method for event detection in a user-wearable device, comprising:

• receiving, from a first sensor implemented in the user-wearable device, photoplethysmogram (PPG) signals;
• processing, at a processor, the PPG signals to obtain PPG signal samples;
• detecting, at the processor, beats in the PPG signal samples;
• dividing the PPG signal samples into PPG signal segments;
• extracting at least one morphology based feature in each PPG signal segment, the morphology based features be associated with statistical characteristics of the PPG signal samples or waveform characteristics of the PPG signal samples;
• classifying, at the processor, each PPG signal segment using the extracted morphology based feature associated with the PPG signal segment and using a machine learning model;
• in response to the classifying, generating, at the processor, an event prediction result based on the extracted morphology based feature;
• and
• displaying the event prediction result at the user-wearable device.

An apparatus, comprising:

• a sensor module comprising a first sensor configured to measure photoplethysmogram (PPG) signals;
• and
• a processor comprising:
• a data processing module configured to process the PPG signals to obtain PPG signal samples, to detect beats in the PPG signal samples, and to divide the PPG signal samples into PPG signal segments;
• an inter-beat interval detection module configured to extract at least one inter-beat interval (IBI) feature in each PPG signal segment;
• a morphology detection module configured to extract at least one morphology based feature in each PPG signal segment;
• and
• a classification module configured to classify each PPG signal segment using the extracted IBI feature and the extracted morphology based feature associated with the segment and using a machine learning model, the classification module further configured to generate an event prediction result based on the extracted IBI feature and the extracted morphology based feature.