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Machine Learning-Based Application for Long-Term Electrocardiogram Analysis
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Author(s): Jonathan Araujo Queiroz (Federal University of Maranhão, Brazil), Juliana M. Silva (Federal University of Maranhão, Brazil), Yonara Costa Magalhães (Federal University of Maranhão, Brazil), Will Ribamar Mendes Almeida (Emil Brunner World University, Brazil), Bárbara Barbosa Correia (Equatorial Energia, Brazil), José Ricardo Santo de Lima (Emil Brunner World University, Brazil), Edilson Carlos Silva Lima (Universidade Estadual do Maranhão, Brazil), Marcos Jose Dos Passos Sa (Universidade Estadual do Maranhão, Brazil)and Allan Kardec Barros Filho (Federal University of Maranhão, Brazil)
Copyright: 2024
Pages: 14
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.ch004
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Abstract
Electrocardiogram (ECG) analyses can only be performed by health professionals whose demand for care is often greater than the availability. In this context, this work consists of the development of an application capable of processing long-lasting ECG signals to assist health professionals in making decisions. The application has an interactive interface that allows view the entire ECG signal in a single image generated by all overlapping cardiac cycles. The proposed application still has email communication between users with the objective of facilitating patient follow-up. The application was tested on three different ECG signals, one artificial and two real. The first signal was an artificial signal generated in software Matlab. The second ECG signal has normal sinus rhythm, available in the MIT-BIH normal sinus rhythm database. The third ECG sign diagnosed with arrhythmia can be found in the MIT-BIH arrhythmia database. The results obtained by the proposed method can be used to support decision-making in clinical practice.
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