Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/642214
Title: An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals
Authors: Changyue Song;Kaibo Liu;Xi Zhang;Lili Chen;Xiaochen Xian
subject: hidden Markov model (HMM)|Electrocardiogram (ECG)|temporal dependence|obstructive sleep apnea (OSA)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/175843
http://localhost/handle/Hannan/642214
ISSN: 0018-9294
1558-2531
volume: 63
issue: 7
Appears in Collections:2016

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Title: An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals
Authors: Changyue Song;Kaibo Liu;Xi Zhang;Lili Chen;Xiaochen Xian
subject: hidden Markov model (HMM)|Electrocardiogram (ECG)|temporal dependence|obstructive sleep apnea (OSA)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/175843
http://localhost/handle/Hannan/642214
ISSN: 0018-9294
1558-2531
volume: 63
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7320974.pdf1.25 MBAdobe PDFThumbnail
Preview File
Title: An Obstructive Sleep Apnea Detection Approach Using a Discriminative Hidden Markov Model From ECG Signals
Authors: Changyue Song;Kaibo Liu;Xi Zhang;Lili Chen;Xiaochen Xian
subject: hidden Markov model (HMM)|Electrocardiogram (ECG)|temporal dependence|obstructive sleep apnea (OSA)
Year: 2016
Publisher: IEEE
Abstract: 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.
URI: http://localhost/handle/Hannan/175843
http://localhost/handle/Hannan/642214
ISSN: 0018-9294
1558-2531
volume: 63
issue: 7
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7320974.pdf1.25 MBAdobe PDFThumbnail
Preview File