Método de diagnóstico da síndrome da apneia obstrutiva do sono por aprendizado de máquina

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: SOARES, Brenda Irla Cardoso Feitosa lattes
Orientador(a): BARROS FILHO, Allan Kardec Duailibe lattes
Banca de defesa: BARROS FILHO, Allan Kardec Duailibe lattes, SANTANA, Ewaldo Eder Carvalho lattes, TOMAZ, Carlos Alberto Bezerra lattes
Tipo de documento: Dissertação
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/3540
Resumo: Obstructive sleep apnea syndrome (OSAS) is characterized by fragmentation and repetitive hypoxia during sleep, if this syndrome is not properly diagnosed and treated, it becomes the cause of serious complications such as cardiovascular problems. The diagnosis of this syndrome requires a detailed clinical examination called polysomnography, which consists of several tests that perform an analysis of brain (EEG), heart (ECG), muscle (EMG) and eye (EOG) activity. Due to the complexity of performing polysomnography, the present study aims to classify and diagnose two groups of subjects, healthy and with normal apnea, based on the use of ECG signals applied in a supervised machine learning algorithm along with Principal Component Analysis (PCA). Using the feature extraction methodology adapted for the diagnosis of obstructive sleep apnea, the results were sampled in two and three dimensions with 95% accuracy.