Classificação de arritmias utilizando variações de tensão do electrocardiograma

• Ano de defesa: 2018
• Autor(a) principal: QUIEROZ, Jonathan Araujo
• Orientador(a): BARROS FILHO, Allan Kardec Duailibe
• Banca de defesa: BARROS FILHO, Allan Kardec Duailibe , SANTANA, Ewaldo Eder Carvalho , SOUZA, Francisco das Chagas de , YEHIA, Hani Camille , CARVALHO FILHO, Antonio Oseas de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA DE ELETRICIDADE/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Ciclo cardíaco; Intervalo R-R; Morfologia do ECG; Estatística de alta ordem
• Palavras-chave em Inglês: Heartbeat; R-R interval; Morphological information; Statistical moments; Atrial fibrillation
• Área do conhecimento CNPq: Engenharia Elétrica
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/tede/2503

Abstract Based on the electrocardiogram (ECG), several authors use the R-R interval, to propose support systems for the diagnosis of arrhythmias. However, R-R interval analysis does not measure alterations in the amplitude of waves, and also does not detect the absence of the P-wave caused by atrial fibrillation. In this context, we proposed a method capable of measuring the amplitude of ECG waves. In this study we proposed to investigate the voltage variation occurring at each heartbeat interval using statistical moments. Unlike the R-R interval in which each heartbeat is associated with a single real number, the proposed method associates each heartbeat to a set of points, that is, a vector. The heartbeats were obtained from the following databases: MIT-BIH Normal Sinus Rhythm, MIT-BIH Atrial Fibrillation (AF), and MIT-BIH Arrhythmia, and the classifiers used to evaluate the proposed method were linear discriminant analysis, k-nearest neighbors, and support vector machine. The experiments were conducted using 80% of the patients for training (16 healthy patients, 41 patients with arrhythmia and 20 patients with AF) and 20% of the patients for testing (2 healthy patients, 6 patients with arrhythmia and 3 patients with AF). ECG window is eficient in healthy heartbeat (100% average accuracy) in all evaluated cases (heartbeat, P-wave, QRS complex, T-wave, PQ segment). In addition, the proposed method proved to be eficient in solving General (accuracy is up to 99.78% in the arrhythmia classification) and specific (accuracy of 100% in the AF classification) heartbeat problems. The results obtained by the proposed method can be used to support decision-making in clinical practice and also detect arrhythmia autonomously without the need for a medical report, as a kind of automatic alert and thus inform the patient and the health team about the abnormality found.