ECG Signal Analysis Using Convolution Neural Networks to avoid Uncertainty in Classification

Abstract

This paper presents the study of electrocardiogram (ECG) signals analysis using convolution neural networks (CNNs) to avoid uncertainty in classification. MIT-BIH ECG dataset with five classes of beats, i.e., nonectopic, supraventricular, ventricular, fusion, unknown, is used for testing and training. The role of pre-processing of dataset was analyzed in improving the network efficiency. All the classes were balanced by doing under-sampling. The data was transformed by adding Gaussian noise to generalize training. CNN was designed with convolutional layer, max-pooling layer, concatenation layer and fully connected layer to classify the ECG signal. A dropout layer with the value of 0.4 was incorporated. Dropout layers are critical in CNN training because they minimize the training data from overfitting which leads to reduce the uncertainty in classification. Sigmoid activation function is used for eventual classification decision making. The presented network offers a high accuracy of 89.2%.