Semantic Segmentation of QRS Complex in Single Channel ECG with Bidirectional LSTM Networks

Abstract

In this paper, bidirectional Long Short-Term Memory (BiLSTM) networks are designed to realize the semantic segmentation of QRS complex in single channel electrocardiogram (ECG) for the tasks of R peak detection and heart rate estimation. Three types of seq2seq BiLSTM networks are introduced, including the densely connected BiLSTM with attention model, the BiLSTM U-Net, and the BiLSTM U-Net++. To alleviate the sparse problem of the QRS labels, symmetric label expansion is applied by extending the single R peak into a time interval of fixed length. Linear ensemble method is introduced that averages the outputs of different BiLSTM networks. The cross-validation results show significant increase of the accuracy and decrease of discontinuous gaps in the QRS interval prediction by the ensembling over singular neural networks.