Publication

Abstract : Medical diagnosis is the process of determining a patient’s health condition by the observation of symptoms and test results. Cardiovascular diseases are one of the most common causes of death worldwide. Electrocardiogram (ECG) is one of the effective ways for diagnosing heart conditions. ECG records and detects the strength and timing of electrical activity of the heart. A proper diagnosis can reduce mortality rate. Artificial intelligence (AI) has shown its inexplicable contribution in the field of medical science, especially in diagnosis. Convolutional neural network (CNN) is the most popular deep learning algorithm, which captures the relevant features by itself. Deep learning requires a massive amount of data to train the network, which increases the computational complexity. This chapter aims to reduce the computational complexity. We consider the cardiac diseases such as arrhythmia and myocardial infarction (MI) for our experimental analysis. We have used heartbeat segmented and preprocessed ECG data available at Kaggle. We aim to reduce the computational complexity of the existing deep learning architecture for cardiac disease classification by using the feature-extracted data. We propose the single-layer CNN for the classification of ECG beats of arrhythmia and MI. We also evaluated the performance of the proposed model by using the following evaluation metrics: precision, recall, and F1 score. The performance of the proposed architecture is high compared to state-of-the-art methods.