An Attention-based Hybrid LSTM-CNN Model for Arrhythmias Classification

Electrocardiogram (ECG) signal based arrhythmias classification is an important task in healthcare field. Based on domain knowledge and observation results from large scale data, we find that accurately classifying different types of arrhythmias relies on three key characteristics of ECG: overall variation trends, local variation features and their relative location. However, these key factors are not yet well studied by existing methods. To tackle this problem, we design an attention-based hybrid LSTM-CNN model which is comprised of a stacked bidirectional LSTM (SB-LSTM) and a two-dimensional CNN (TD-CNN). Specifically, SB-LSTM and TD-CNN are utilized to extract the overall variation trends and local features of ECG, respectively. Furthermore, we add a trend attention gate (TAG) to SB-LSTM, meanwhile, add a feature attention mechanism (FAM) and a location attention mechanism (LAM) to TD-CNN. Thus, the effects of important trends and features at key locations in ECG can be enhanced, which is conducive to obtaining a better understanding of the fluctuation pattern of ECG. Experimental results on the MIT-BIH arrhythmias dataset indicate that our model outperforms three state-of-the-art methods, and achieve 99.3% of accuracy, 99.6% of sensitivity and 98.1% of specificity, respectively.