Heartbeat Detection in Photoplethysmography Signals for the Monitoring of Cardiac Arrhythmias
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Author
Jeanningros, Loïc
Meister, Théo A.
Soria, Rodrigo
Tanner, Hildegard
Vesin, Jean-Marc
Thiran, Jean-Philippe
Lemay, Mathieu
Braun, Fabian
Rexhaj, Emrush
Abstract
Cardiac Arrhythmias (CAs) are a critical health issue associated with serious complications, such as stroke or heart failure. Wearable devices based on photoplethysmography (PPG), which can be worn in everyday life, have the potential to detect CAs earlier than current methods relying on electrocardiography (ECG). They could therefore enable a more preventive approach to treatment. While most PPG-based heartbeat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation in clinical settings, their performance in patients with other cardiac arrhythmias in ambulatory settings remains unexplored to date.
Methods: The PPG-beats framework, developed by Charlton and colleagues, evaluates the performance of several open-source heartbeat detectors. We applied the PPG-beats framework on a newly acquired dataset including forty-four patients referred for an ambulatory Holter ECG at Inselspital in Bern. This dataset comprises not only atrial fibrillation, but also bigeminy and premature contractions.
Results: The heartbeat detector named MSPTD, performed best on normal sinus rhythm (with a median F1 score of 94.6%) and the detector named QPPG was top-ranked both on atrial fibrillation (91.6%) and bigeminy (80.0%).
Conclusions: The heartbeats detectors named QPPG and MSPTD consistently achieved higher performance than other detectors. However, the detection of heartbeats from PPG signals is compromised in presence of bigeminy. This study sets the stage for continuous monitoring of cardiac arrhythmias in everyday life.
Publication Reference
CVRC, Bern (Switzerland)
Year
2024-01-24