The proposed 1-D convolutional deep residual neural network with SMOTE achieved an average accuracy of 98.63% for the classification of five heartbeat types.
Does a 1-D convolutional deep residual neural network with SMOTE improve the classification accuracy of ECG heartbeats?
A 1-D convolutional deep residual neural network combined with SMOTE for data balancing achieves high accuracy (98.63%) in classifying five types of ECG heartbeats.
Absolute Event Rate: 98.63% vs 95%
An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier's performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.
Khan et al. (Tue,) conducted a other in Arrhythmia (n=47). 1-D convolutional deep residual neural network (ResNet) with SMOTE vs. Model without SMOTE was evaluated on Classification accuracy. The proposed 1-D convolutional deep residual neural network with SMOTE achieved an average accuracy of 98.63% for the classification of five heartbeat types.