Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters.
Annu Int Conf IEEE Eng Med Biol Soc
; 2016: 3203-3206, 2016 Aug.
Article
em En
| MEDLINE
| ID: mdl-28268989
ABSTRACT
We investigated using heart rate variability (HRV), ECG derived respiration and cardiopulmonary coupling features (CPC) calculated from night-time single lead ECG signals to classify one-minute epochs for the presence or absence of sleep apnoea. We used the 35 training recordings of the M.I.T. Physionet Apnea-ECG database. Performance was assessed with leave-one-record-out cross-validation. The best classification performance was achieved using the CPC features in conjunction with the time-domain based HRV parameters. The cross-validated results on the 17,045 epochs of the dataset were an accuracy of 89.8%, a specificity of 92.9%, a sensitivity of 84.7%, and a kappa value of 0.78. These results are comparable with best results reported on this database.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Respiração
/
Síndromes da Apneia do Sono
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Algoritmos
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Processamento de Sinais Assistido por Computador
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Frequência Cardíaca
Tipo de estudo:
Diagnostic_studies
Limite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Annu Int Conf IEEE Eng Med Biol Soc
Ano de publicação:
2016
Tipo de documento:
Article