An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals

Obstructive sleep apnea (OSA) syndrome is a common sleep disorder suffered by an increasing number of people worldwide. As an alternative to polysomnography (PSG) for OSA diagnosis, the automatic OSA detection methods used in the current practice mainly concentrate on feature extraction and classifi...

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Vydané v:IEEE transactions on biomedical engineering Ročník 63; číslo 7; s. 1532 - 1542
Hlavní autori: Song, Changyue, Liu, Kaibo, Zhang, Xi, Chen, Lili, Xian, Xiaochen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.07.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Adult
Algorithms
Automation
Biomedical measurement
Diagnosis
Disorders
Electrocardiogram (ECG)
Electrocardiography
Electrocardiography - methods
Feature extraction
hidden Markov model (HMM)
Hidden Markov models
Humans
Markov analysis
Markov Chains
Markov models
Markov processes
Mathematical models
Middle Aged
obstructive sleep apnea (OSA)
Signal Processing, Computer-Assisted
Sleep
Sleep apnea
Sleep Apnea, Obstructive - diagnosis
Support Vector Machine
temporal dependence
hidden Markov model (HMM)
Electrocardiogram (ECG)
obstructive sleep apnea (OSA)
temporal dependence
ISSN:0018-9294, 1558-2531, 1558-2531
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Popis
Shrnutí:Obstructive sleep apnea (OSA) syndrome is a common sleep disorder suffered by an increasing number of people worldwide. As an alternative to polysomnography (PSG) for OSA diagnosis, the automatic OSA detection methods used in the current practice mainly concentrate on feature extraction and classifier selection based on collected physiological signals. However, one common limitation in these methods is that the temporal dependence of signals are usually ignored, which may result in critical information loss for OSA diagnosis. In this study, we propose a novel OSA detection approach based on ECG signals by considering temporal dependence within segmented signals. A discriminative hidden Markov model (HMM) and corresponding parameter estimation algorithms are provided. In addition, subject-specific transition probabilities within the model are employed to characterize the subject-to-subject differences of potential OSA patients. To validate our approach, 70 recordings obtained from the Physionet Apnea-ECG database were used. Accuracies of 97.1% for per-recording classification and 86.2% for per-segment OSA detection with satisfactory sensitivity and specificity were achieved. Compared with other existing methods that simply ignore the temporal dependence of signals, the proposed HMM-based detection approach delivers more satisfactory detection performance and could be extended to other disease diagnosis applications.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2015.2498199