Arrhythmia is characterized by aberrant electrical activity of the heart, which can be identified the changes in the Electrocardiogram (ECG). Automatic ECG detection is needed because it is mainly used for detecting arrhythmias. Although several algorithms are implemented for the automatic classification of cardiac arrhythmias based on the characteristics of the ECG, their stratification rate is very less because of the unreliable features of signal characteristics or limited generalization capability of the classifier and it is still difficult to diagnose the arrhythmia disease automatically. At this work, they propose a new hybrid deep learning technique for the classification of arrhythmia from the ECG signal. Initially, the wanted ECG signal is collected from the standard websites and then it is assigned to the preprocessing technique. The preprocessing techniques includes the artifacts removal and peak detection techniques noise removal for the elimination of the unwanted distortions and the noise present in the signal then the resultant signal is fed to the Short-time Fourier transform (STFT) to achieve the spectrogram signals and then the spectrogram signal. Thus, the resultant spectrogram signal is given to the hybrid deep learning architecture that includes the 3DCNN-ResNet for diagnosing the arrhythmia disease. Here, the parameter optimizations take place using the hybrid Artificial Showering Dolphin Swarm Optimization (ASDSO) to increase the classification performance. It classifies the signal into five prominent classes that is Premature Ventricular Contraction (V), Right Bundle Branch Block (RBBB or R), Normal Sinus Rhythm (N), Left Bundle Branch Block (LBBB or L), and Atrial Premature Beat (A). The success of the proposed model is validated through diverse benchmark datasets with the performance validation like recall, precision, accuracy, f-measure and some negative measures.
Early and accurate classification of arrhythmia helps the experts to select the treatment for the patient to increase the recovery rate. The deep learning method of convolution neural network (CNN) is used for classification, and this has an overfitting problem. In this research, the multi-task group bi-directional long short term memory (MTGBi-LSTM) method is proposed to increases the performance of arrhythmia classification. The multi-task learning technique learns two ECG signals in shared representation for effective learning. The global and intra LSTM method selects the relevant feature and easily escapes from local optima. The MTGBi-LSTM model learns the unique features in shared representation that helps to overcome overfitting problem and increases the learning rate of the model. The MTGBi-LSTM model in arrhythmia classification is evaluated on MIT-BIH dataset. The MTGBi-LSTM model has 96.48% accuracy, 97.73% sensitivity, existing AFibNet has 96.36% accuracy, and 93.65% sensitivity for arrhythmia classification in CPSC 2018 dataset.
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