Apnea Detection Based on Hidden Markov Model Kernel

被引：0
|
作者：
Travieso, Carlos M. [1 ]
Alonso, Jesus B. [1 ]
Ticay-Rivas, Jaime R. [1 ]
del Pozo-Banos, Marcos [1 ]
机构：
[1] Univ Las Palmas Gran Canaria, Inst Technol Dev & Innovat Commun IDETIC, Signals & Commun Dept, Las Palmas Gran Canaria, Spain
来源：
ADVANCES IN NONLINEAR SPEECH PROCESSING | 2011年 / 7015卷
关键词：
Apnea Detection; Hidden Markov Model; Kernel Building; Pattern Recognition; Non-linear Processing; SLEEP-APNEA; ELECTROCARDIOGRAM;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work presents a new system to diagnose the syndrome of obstructive sleep apnea (OSA) that includes a specific block for the removal of Electrocardiogram (ECG) artifacts and the R wave detection. The system is modeled by ECG cepstral coefficients. The final decision is done with two different approaches. The first one is based on Hidden Markov Model (HMM), as classifier. On the other hand, another classification system is based on Support Vector Machines, being the parameterization based on the transformation of HMM by a kernel. Our results reached up to 98.67%.
引用
收藏
页码：71 / 79
页数：9
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