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Artificial Neural Networks for Classification of Pathologies Based on Moments of Cardiac Cycle
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Author(s): Jonathan Araujo Queiroz (Federal University of Maranhão, Brazil), Gean Sousa (Federal University of Maranhão, Brazil), Priscila Lima Rocha (Federal University of Maranhão, Brazil), Yonara Costa Magalhões (Federal University of Maranhão, Brazil)and Allan Kardec Barros Filho (Federal University of Maranhão, Brazil)
Copyright: 2024
Pages: 19
Source title:
Advances in Neuroscience, Neuropsychiatry, and Neurology
Source Author(s)/Editor(s): Cândida Lopes Alves (Federal University of Maranhão, Brazil), Katie Moraes Almondes (Federal University of Rio Grande do Norte, Brazil)and Gilberto Sousa Alves (Federal University of Maranhão, Brazil)
DOI: 10.4018/979-8-3693-0851-6.ch002
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Abstract
The advancement of cardiac pathology quantification hinges on the utilization of computer algorithms. To transform this vision into reality, these algorithms must distinguish among the most prevalent cardiac disorders. While some studies have leveraged the R-R interval for data extraction from ECG signals to diagnose various arrhythmias, this approach falls short in measuring changes in other ECG waves, like distortions in the P wave indicative of atrial fibrillation. This chapter introduces a new metric bi-level based on Shannon entropy to gauge the information within cardiac cycles, accounting for both the events themselves and their momentary decomposition. Experimental results reveal the method's high accuracy in classifying four distinct cardiac signal types (including one healthy signal and three pathological ones), achieving a classification rate ranging from 97.28% to 100% when employing a multilayer perceptron neural network. It holds great promise in aiding the diagnosis of cardiac pathologies.
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