Pulse Wave Analysis of Photoplethysmography Signals to Enhance Classification of Cardiac Arrhythmias

dc.contributor.authorJeanningros, Loïc
dc.contributor.authorBraun, Fabian
dc.contributor.authorZaen, Jérôme Van
dc.contributor.authorLe Bloa, Mathieu
dc.contributor.authorPorretta, Alessandra
dc.contributor.authorTeres, Cheryl
dc.contributor.authorHerrera, Claudia
dc.contributor.authorDomenichini, Giulia
dc.contributor.authorCarroz, Patrice
dc.contributor.authorGraf, Denis
dc.contributor.authorPascale, Patrizio
dc.contributor.authorVesin, Jean-Marc
dc.contributor.authorThiran, Jean-Philippe
dc.contributor.authorPruvot, Etienne
dc.contributor.authorLemay, Mathieu
dc.description.abstractPhotoplethysmography (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.
dc.identifier.citationCinC 2022, Tampere (Finland)
dc.titlePulse Wave Analysis of Photoplethysmography Signals to Enhance Classification of Cardiac Arrhythmias
dc.typeProceedings Article
dc.type.csemresearchareasDigital Health