POSTER: Atrial Fibrillation Detection Using a Double-Layer Bi-Directional LSTM Neural Networks

Abstract

Atrial fibrillation (AF) is the most common heart disorder manifested as an abnormal rhythm of irregular heartbeats that could lead to strokes and death. In this paper, we propose a double-layer bi-directional long short term memory (LSTM) neural network to classify a short segment of ECG signal transformed into spectrogram. We also use a preprocessing step to augment the dataset to achieve better classification performance. We conducted different experiments on different segment lengths and different network parameters using PhysioNet Challenge 2017 dataset and we achieved a total accuracy of 91.4% of classifying AF signals outperforming existing methods.