Detalhes bibliográficos
Ano de defesa: |
2022 |
Autor(a) principal: |
SOARES, Brenda Irla Cardoso Feitosa
 |
Orientador(a): |
BARROS FILHO, Allan Kardec Duailibe
 |
Banca de defesa: |
BARROS FILHO, Allan Kardec Duailibe
,
SANTANA, Ewaldo Eder Carvalho
,
TOMAZ, Carlos Alberto Bezerra
 |
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: |
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Palavras-chave em Inglês: |
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Área do conhecimento CNPq: |
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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. |