Automated detection of arrhythmia from electrocardiogram signal based on new convolutional encoded features with bidirectional long short-term memory network classifier

Early detection of cardiac arrhythmia is needed to reduce mortality. Automatically detecting the cardiac arrhythmias is a very challenging task. In this paper, a new deep convolutional encoded feature (CEF) based on non-linear compression composition is applied to diminish the ECG signal segment size. Bidirectional long short-term memory (BLSTM) network classifier has been proposed to detect arrhythmias from the ECG signal, which is encoded by the convolutional encoder. These encoded features are used as the input to BLSTM network classifier. For performance comparison, three other classifiers, namely unidirectional long short-term memory (ULSTM) network, gated recurrent Unit (GRU) and multilayer perceptron, are designed. The experimental studies detect and classify arrhythmias present in the MIT-BIH arrhythmia database into five different heartbeat classes. These heartbeat classes are normal (N), left bundle branch block (L), right bundle branch block(R), paced (P) and premature ventricular contraction (V). Evaluation of performance and system efficiency has been done with the help of four different types of evaluation criteria which are overall accuracy, precision, recall, and F-score. The experimental results indicate that the BLSTM network has achieved an overall accuracy of 99.52% with the processing time of only 6.043 s.