Publications

Abstract : Cardiovascular disease is a condition that causes damage to the heart muscle, valves, rhythm, or blockage in the blood vessels. It requires early diagnosis, as it is the leading cause for the sudden death in humans. Electrocardiogram (ECG) is the most important biomedical signal used extensively by the cardiologist to diagnose cardiovascular disease. The classification of ECG signals helps physicians to make decisions in the diagnosis of cardiac diseases. There are many conventional machine learning and deep learning algorithms used in the literature for the automatic classification of ECG signals. Conventional machine learning algorithms require handcrafted features. There are many features such as morphological feature extraction, computation of RR interval, QRS peak detection, ST segment, ST distance, and amplitude computation. The classical machine learning algorithms used to classify the extracted features are shallow neural network, K nearest neighbor, support vector machines (SVM), random forest, and decision tree. Deep learning algorithms learn the features from the given training data, which outperformed the handcrafted features used in the conventional machine learning algorithms. There are state-of-the-art architectures such as convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The existing architectures rely on disease-specific approach. This chapter aims to provide a single architecture by transferable approach for cardiac disease classification using ECG. We plan to achieve this by the analysis of the different state-of-the-art deep learning architectures such as CNN, LSTM, RNN, and GRU to classify cardiac diseases using ECG signal. Arrhythmia, myocardial infarction, and atrial fibrillation are the cardiac diseases we considered for the study. We have used publically available datasets from Kaggle and PhysioNet for experimental evaluation. In the proposed methodology, the deep learning architectures such as RNN, LSTM, and GRU trained to classify the atrial fibrillation from the ECG signal were used to classify other cardiac diseases such as arrhythmia and myocardial infarction. Similarly, CNN, which is trained to classify arrhythmia and myocardial infarction, is used to classify atrial fibrillation. We kept the network parameters (also known as hyperparameters) such as learning rate, batch size, and number of epochs the same for the entire experimental analysis. We evaluated the performance of the proposed methodology using the metrics: precision, recall, and F1 score. We observed that LSTM and GRU performed well compared to the RNN and CNN. LSTM and GRU offer the maximum precision and recall score, which varies between 0.97–0.98 for all three diseases. The computational cost of GRU is less compared to the RNN. Our results show that the deep learning architectures are transferable. Unlike deep learning, which is the data-driven approach, the machine learning algorithms are not adaptable or transferable. To validate this, comparison between the different machine learning algorithms like Naive Bayes, K-nearest neighbor, SVMs with two different kernels such as linear and RBF, AdaBoost, random forest, decision tree, and logistic regression are conducted for all three diseases considered for the study.