Abstract
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.
Highlights
Arrhythmia has been a common phenomenon characterized by cardiac conduction disorder, which may increase the risk of sudden death, and it can generally be manifested by electrocardiogram (ECG) [1]
The contributions of this study can be summarized as follows: 1) We propose a novel algorithm, called DELM-LRFBLSTM, for ECG signals recognition by combining the advantages of DELM-local receptive fields (LRFs) and Bidirectional long-short time memory network (BLSTM) neural networks
3) We examine the performance of the DELMLRF-BLSTM algorithm on the MIT-Beth Israel Hospital (BIH) dataset
Summary
Arrhythmia has been a common phenomenon characterized by cardiac conduction disorder, which may increase the risk of sudden death, and it can generally be manifested by electrocardiogram (ECG) [1]. The ECG collects electrical signals from external electrodes connected to the skin and can accurately capture the cardiac electrical activity over a period of time [2], [3]. Studies on ECG signals are generally divided into two parts: detection and classification. Studies on detection concentrate on QRS complex detection and determining heartbeats within the ECG data obtained for a certain period of time. The classification of detected heartbeats is an important step in the treatment of ECG signals. The rapid development of machine learning makes recognizing arrhythmia automatically from the ECG signals possible.
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