Abstract
Cardiovascular diseases (CVD) are the dominant cause of deaths in the world, of which 90% are curable. The electrocardiogram (ECG) measures the electrical stimulus of the heart noninvasively. Convolutional neural networks (CNN) act as one of the powerful machine learning techniques to classify ECG arrhythmia classification and other CVDs. Nonetheless, they have some functional flaws like ignorance of spatial hierarchies between the features and are unable to acquire a rotational invariance. To overcome these problems of CNN, a novel neural network named capsule network (CapsNet) is proposed as an efficient algorithm to provide error-free implementation of deep learning over the databases. The main focus of this work is to apply and implement CapsNet for ECG signal classification from the MIT-BIH database and compare its efficiency with the pretrained CNN networks.
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