Photoplethysmography (PPG) has recently gained increasing interest for less obtrusive long-term cardiovascular monitoring. As for cardiac arrhythmia (CA), most research and available PPG devices have focused on the detection of atrial fibrillation (AF), the most common CA. However, other less studied CAs can induce errors in standard AF detectors.
To address the PPG-based detection of both AF and non-AF CAs, we investigate novel features, extracted by pulse wave analysis (PWA), that provide insight into the morphology of individual pulses. Their discriminative power was evaluated based on the RELIEFF algorithm for feature selection, and we compared performance metrics for CA classification with and without PWA features.
The classification accuracy using ridge regression was increased by 0.4%, from 75.6% to 76.0%, when using PWA features on top of temporal and spectral features. Likewise, the classification of non-AF CAs was globally improved.
These results show the potential of extracting measures about individual pulse morphologies to improve detection of various CAs.