Systems and methods for atrial fibrillation (af) and cardiac disorders detection from biological signals

Abstract

Continuous monitoring of subject's cardiac system using biological signal(s) (BS) during day-to-day activities is essential for managing personal cardiac health/disorders, etc. Conventional systems/methods lack in improvising overall classification results and configured for specific device/signal say ECG or PPG and so on. Present disclosure provides systems and methods for classifying BS obtained from users, wherein BS are preprocessed to obtain filtered signals (FS). Corresponding feature extraction module is utilized for feature set extraction based on features in FS. The feature set is reduced and segmented into test and training data. Biological signal classification model(s) are generated using training data and a BCM is applied on test data to classify biological signals (BS) as one of Atrial Fibrillation (AF), a non-AF, a cardiac arrythmia disorder, or ischemia. Accelerometer features of connected device associated with the users can be obtained to detect activities which in conjunction with the BCM's output improvises above classification.