A Comparative Study on Detecting Heart Beats in Photoplethysmography Signals in Presence of Various Cardiac Arrhythmias
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Author
Jeanningros, Loic
Le Bloa, Mathieu
Teres, Cheryl
Herrera, Claudia
Porretta, Alessandra
Pascale, Patrizio
Luca, Adrian
Solana Muñoz, Jorge
Domenichini, Giulia
Vesin, Jean-Marc
DOI
10.22489/CinC.2023.119
Abstract
Wearable devices based on photoplethysmography (PPG) allow for the screening of large populations at risk of cardiovascular disease. While PPG has shown the ability to discriminate atrial fibrillation (AF) - the most common cardiac arrhythmia (CA) - versus normal sinus rhythm, it is not clear whether such AF detectors are efficient in presence of CAs other than AF. We propose to apply a simple recurrent neural network (RNN) on a newly acquired dataset containing eight different types of CAs. The classifier takes sequences of inter-beat intervals (IBIs) as input and discriminates between normal and abnormal rhythm. The RNN achieved 84% accuracy in detecting abnormal rhythms. Some CAs were well detected (AF: 99.6%; atrial tachycardia: 100%), whereas other CAs were more difficult to detect (atrial flutter: 65.4%; bigeminy: 72.4%; ventricular tachycardia 80%). This study shows the potential of PPG technology to detect not only AF but also other types of CA. It highlights the strengths and weaknesses of IBI-based detection of abnormal rhythms and paves the way towards continuous monitoring of CAs in everyday life.
Publication Reference
CinC 2023, Atlanta (USA), pp. 1-4
Year
2023-01-10