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Artificial Neural Networks for Classification of Pathologies Based on Moments of Cardiac Cycle

Artificial Neural Networks for Classification of Pathologies Based on Moments of Cardiac Cycle

Jonathan Araujo Queiroz, Gean Sousa, Priscila Lima Rocha, Yonara Costa Magalhões, Allan Kardec Barros Filho
Copyright: © 2024 |Pages: 19
ISBN13: 9798369308516|EISBN13: 9798369308523
DOI: 10.4018/979-8-3693-0851-6.ch002
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MLA

Queiroz, Jonathan Araujo, et al. "Artificial Neural Networks for Classification of Pathologies Based on Moments of Cardiac Cycle." Advances in Neuroscience, Neuropsychiatry, and Neurology, edited by Cândida Lopes Alves, et al., IGI Global, 2024, pp. 17-35. https://doi.org/10.4018/979-8-3693-0851-6.ch002

APA

Queiroz, J. A., Sousa, G., Rocha, P. L., Magalhões, Y. C., & Barros Filho, A. K. (2024). Artificial Neural Networks for Classification of Pathologies Based on Moments of Cardiac Cycle. In C. Alves, K. Almondes, & G. Alves (Eds.), Advances in Neuroscience, Neuropsychiatry, and Neurology (pp. 17-35). IGI Global. https://doi.org/10.4018/979-8-3693-0851-6.ch002

Chicago

Queiroz, Jonathan Araujo, et al. "Artificial Neural Networks for Classification of Pathologies Based on Moments of Cardiac Cycle." In Advances in Neuroscience, Neuropsychiatry, and Neurology, edited by Cândida Lopes Alves, Katie Moraes Almondes, and Gilberto Sousa Alves, 17-35. Hershey, PA: IGI Global, 2024. https://doi.org/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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