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Atrial Fibrillation (AFIB) is a common atrial arrhythmia that affects millions of people worldwide. However, most of the time, AFIB is paroxysmal and can pass unnoticed in medical exams therefore regular screening is required. This paper proposes machine learning methods to detect AFIB from short-term ECG and PPG signals. Several experiments were conducted across five different databases with three of them containing ECG signals and the other two consisting of only PPG signals. A total of 269,842 signal segments were analyzed across all datasets (212,266 were of normal sinus rhythm (NSR) and 57,576 corresponded to AFIB segments). Experiments were conducted to investigate the hypothesis that a machine learning model trained to predict AFIB from ECG segments, could be used to predict AFIB from PPG segments. A random forest machine learning algorithm achieved the best accuracy and achieved a 90% accuracy rate on the UMMC dataset (216 samples) and a 97% accuracy rate on the MIMIC-III dataset (2,134 samples). The ability to detect AFIB with significant accuracy using machine learning algorithms from PPG signals, which can be acquired via non-invasive contact or contactless, is a promising step forward toward the goal of achieving large-scale screening for AFIB. |
