Improved systolic peak detection in photoplethysmography signals: focus on atrial fibrillation

Abstract

Photoplethysmography (PPG) is a widely recognized non-invasive optical technique for monitoring blood volume changes. Recently, PPG signals have gained prominence in healthcare applications, including the detection of cardiac arrhythmias. Cardiac arrhythmias represent a significant global health challenge, with particular focus on identifying atrial fibrillation (AF), the most prevalent type. Accurate detection of systolic peaks in PPG signals is crucial for arrhythmia detection and for other applications such as heart rate estimation and heart rate variability analysis. Despite the high accuracy of existing beat detection methods in healthy subjects, the performance in the presence of cardiac arrhythmias is lower. This study employs a deep learning method to enhance the detection of systolic peaks in PPG signals, even in the presence of AF. The model was trained on a dataset comprising 2,477 10-second PPG segments with over 37,000 annotated PPG peaks, including data from AF patients. Our model achieved an F1 score of 97.3 % on the test dataset and F1 score of 94.8 % on the test dataset when considering only AF patients.Photoplethysmography (PPG) is a widely recognized non-invasive optical technique for monitoring blood volume changes. Recently, PPG signals have gained prominence in healthcare applications, including the detection of cardiac arrhythmias. Cardiac arrhythmias represent a significant global health challenge, with particular focus on identifying atrial fibrillation (AF), the most prevalent type. Accurate detection of systolic peaks in PPG signals is crucial for arrhythmia detection and for other applications such as heart rate estimation and heart rate variability analysis. Despite the high accuracy of existing beat detection methods in healthy subjects, the performance in the presence of cardiac arrhythmias is lower. This study employs a deep learning method to enhance the detection of systolic peaks in PPG signals, even in the presence of AF. The model was trained on a dataset comprising 2,477 10-second PPG segments with over 37,000 annotated PPG peaks, including data from AF patients. Our model achieved an F1 score of 97.3 % on the test dataset and F1 score of 94.8 % on the test dataset when considering only AF patients.