Paroxysmal Atrial Fibrillation Detection by Combined Recurrent Neural Network and Feature Extraction on ECG Signals

Paroxysmal atrial fibrillation (AFib) or intermittent atrial fibrillation is one type of atrial fibrillation which occurs rapidly and stops spontaneously within days. Its episodes can last several seconds, hours, or even days before returning to normal sinus rhythm. A lack of intervention may lead the paroxysmal into persistent atrial fibrillation, causing severe risk to human health. However, due to its intermittent characteristics, it is generally neglected by patients. Therefore, real-time monitoring and accurate automatic algorithms are highly needed for early screening. This study proposes a two-stage algorithm, including a BiLSTM network to classify healthy and atrial fibrillation, followed by a feature-extraction-based neural network (NN) to identify the persistent, paroxysmal atrial fibrillation onsets. The extracted features include the entropy and standard deviation of the RR intervals. The two steps can achieve 90.14% and 92.56% accuracy in the validation sets on small segments. This overall algorithm also has the advantage of the low computing load, which shows a high potential for a portable embedded device.