Detalhes bibliográficos
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: |
|
Palavras-chave em Inglês: |
|
Área do conhecimento CNPq: |
|
Link de acesso: |
https://tedebc.ufma.br/jspui/handle/tede/2503
|
Resumo: |
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. |