Abnormal Cardiac Rhythm Detection Based on Photoplethysmography Signals and a Recurrent Neural Network.

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.