Abstract
The analysis and processing of ECG signals are a key approach in the diagnosis of cardiovascular diseases. The main field of work in this area is classification, which is increasingly supported by machine learning-based algorithms. In this work, a deep neural network was developed for the automatic classification of primary ECG signals. The research was carried out on the data contained in a PTB-XL database. Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the convolutional network, but with additional entropy-based features. The dataset was divided into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively. The studies were conducted for 2, 5, and 20 classes of disease entities. The convolutional network with entropy features obtained the best classification result. The convolutional network without entropy-based features obtained a slightly less successful result, but had the highest computational efficiency, due to the significantly lower number of neurons.
Highlights
With the recognition of two classes, the network based on the convolutional network achieved
The network based on the convolutional network with entropy features achieved 89.82% ACC with the recognition of two classes and 76.5% with the recognition of five classes
The convolutional neural network with entropy features achieved the highest accuracy in every classification task, scoring 89.2%, 76.5%, and 69.8%
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. According to publicly available reports, cardiovascular disease remains the leading cause of mortality worldwide [1]. One of the main causes of cardiovascular diseases is cardiac arrhythmia, in which the heartbeat deviates from typical beating patterns [2]
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