Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/44004
Título: Automatic Apnea Identification by Transformation of the Cepstral Domain
Autores/as: Travieso, Carlos M. 
Alonso, Jesús B. 
del Pozo-Baños, Marcos
Ticay-Rivas, Jaime R.
Lopez-de-Ipiña, Karmele
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Automatic apnea detection Artifacts removal Hidden Markov model Kernel building Machine learning Pattern recognition Nonlinear processing
Fecha de publicación: 2013
Editor/a: 1866-9956
Publicación seriada: Cognitive Computation 
Resumen: A new approach based on the transformation of the Cepstral domain is developed on this work. This approach reaches an automatic diagnosis for the syndrome of obstructive sleep apnea that includes a specific block for the removal of electrocardiogram (ECG) artifacts and the R wave detection. The system is modeled by a transformation of the Cepstral domain sequence using hidden Markov model (HMM). The final decision is done with two different approaches: one based on HMM as a classifier and a second one based on support vector machines classification and a parameterization based on the transformation of HMM by a kernel. The later approach reached results up to 99.23 %, using all test samples from Physionet Apnea-ECG Database.
URI: http://hdl.handle.net/10553/44004
ISSN: 1866-9956
DOI: 10.1007/s12559-012-9184-x
Fuente: Cognitive Computation[ISSN 1866-9956],v. 5, p. 558-565
Colección:Artículos
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