Introduction: Photoplethysmography (PPG) has recently gained increasing interest for less obtrusive long-term cardiovascular monitoring. Interestingly, most research and available PPG devices have focused on the detection of atrial fibrillation (AF). However, other cardiac arrhythmias (CA), that have been less studied, can induce errors in common AF detectors. To address this, we investigate the discriminative power of novel features extracted by pulse wave analysis (PWA) for the PPG-based detection of both AF and non-AF CAs.
Methods: PPG signals were acquired with a wrist-bracelet concurrently with 12-lead ECG (used for gold-standard annotation of CAs) from 42 patients referred for catheter ablation at the Lausanne University Hospital. PPG segments of 30 s were classified as sinus rhythm (SR), AF or non-AF based on spectral and temporal features extracted from raw PPG time series and from inter-beat interval series. In addition, novel PWA features providing insights into the morphology of individual pulses were extracted by detecting specific extrema in the PPG waveform and its derivatives. Their discriminative power was evaluated based on the Relief algorithm for feature selection. Finally, we compared performance metrics for CA classification with and without PWA features.
Results: The classification accuracy using ridge regression was significantly increased by 2.8%, from 73.5% to 76.3% (p = 0.009), when using PWA features on top of temporal and spectral features. Likewise, the sensitivity in detecting AF increased by 3.4%, from 77.3% to 80.7% (p = 0.09). The most discriminative PWA features were the acceleration magnitude of the pulse upstroke and the timing of the dicrotic notch.
Conclusion: PWA features showed some potential for the detection and classification of AF and non-AF CAs. Although further work is required to collect a larger number of arrhythmic events, these results show the potential for improving our understanding of the peripheral hemodynamic signature of CAs.