A Fast and Accurate Recognition of ECG Signals Based on ELM-LRF and BLSTM Algorithm

Extreme learning machine based on local receptive fields (ELM-LRFs) is a very fast method that can be used for feature extraction and classification. Bidirectional long-short time memory network (BLSTM), a widely used type of recurrent neural network (RNN) architecture, has showed excellent performance in time series processing fields. In this paper, we combine the superiority of above algorithms and propose a fast and accurate hybrid deep learning model which is named DELM-LRF-BLSTM for ECG signal recognition. This model uses the segmented heartbeats as input and employs a deep ELM-LRF to gain significant local spatial features. Then we fed the features to a three-layer BLSTM and it can extract temporal features for ECG signal recognition. The combination of ELM-LRF and BLSTM can not only consider the local information in a heartbeat, but also consider the long-distance dependence between heartbeats. Experimental results on MIT-BIH Arrhythmia dataset show that the proposed DELM-LRF-BLSTM algorithm has high accuracy and sensitivity, up to 99.32% and 97.15% respectively, which verifies the effectiveness and feasibility of the model. Moreover, only 6.1 millisecond is needed for once heartbeat recognition operation. Due to it's high performance and low computational complexity, the proposed algorithm is feasible for practical use.