Automatic arrhythmia recognition from electrocardiogram signals using different feature methods with long short-term memory network model

The high mortality rate that has been prevailing among cardiac patients can be reduced to some extent through early detection of the heart-related diseases which can be done with the help of automated computer-aided diagnosing machines. There is a need for an expert system that automatically detects the abnormalities in the heart rhythms. Various new feature extraction methods employing long short-term memory network (LSTM) model have been presented in this paper, which help in the detection of heart rhythms from electrocardiogram signals. Based on higher-order statistics, wavelets, morphological descriptors, and R–R intervals, the electrocardiogram signals are decomposed into 45 features. All these features are used as a sequence, for input, to a single LSTM model. The publically available MIT-BIH arrhythmia database has been used for training and testing. The proposed model has helped to classify five distinct arrhythmic rhythms (including normal beats). Performance evaluation of the proposed system model has obtained values like precision of 96.73%, accuracy of 99.37%, specificity of 99.14%, F-score of 95.77%, and sensitivity of 94.89%, respectively.