The research of sleep staging based on single-lead electrocardiogram and deep neural network
The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a...
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| Published in: | Biomedical engineering letters Vol. 8; no. 1; pp. 87 - 93 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Korea
The Korean Society of Medical and Biological Engineering
01.02.2018
Springer Nature B.V 대한의용생체공학회 |
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| ISSN: | 2093-9868, 2093-985X, 2093-985X |
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| Abstract | The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen’s kappa coefficient of about 0.56 for the classification of Wake, REM and NREM. |
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| AbstractList | The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen’s kappa coefficient of about 0.56 for the classification of Wake, REM and NREM. The polysomnogram (PSG) analysis is consideredthe golden standard for sleep staging under the clinicalenvironment. The electroencephalogram (EEG) signal isthe most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEGsignal presents us with a few technical challenges. Electrocardiogramsignals on the other hand, are easier torecord, and can provide an attractive alternative for homesleep monitoring. In this paper we describe a method basedon deep neural network (DNN), which can be used for theclassification of the sleep stages into Wake (W), rapid-eyemovement(REM) and non-rapid-eye-movement (NREM)sleep stage. We apply the sleep stage stacked autoencoderto constitute a 4-layer DNN model. In order to test theaccuracy of our method, eighteen PSGs from the MIT-BIHPolysomnographic Database were used. A total of 11 featureswere extracted from each electrocardiogram recordingThe experimental design employs cross-validationacross subjects, ensuring the independence of the trainingand the test data. We obtained an accuracy of 77% and aCohen’s kappa coefficient of about 0.56 for the classificationof Wake, REM and NREM. KCI Citation Count: 1 The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen's kappa coefficient of about 0.56 for the classification of Wake, REM and NREM.The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a few technical challenges. Electrocardiogram signals on the other hand, are easier to record, and can provide an attractive alternative for home sleep monitoring. In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. We apply the sleep stage stacked autoencoder to constitute a 4-layer DNN model. In order to test the accuracy of our method, eighteen PSGs from the MIT-BIH Polysomnographic Database were used. A total of 11 features were extracted from each electrocardiogram recording The experimental design employs cross-validation across subjects, ensuring the independence of the training and the test data. We obtained an accuracy of 77% and a Cohen's kappa coefficient of about 0.56 for the classification of Wake, REM and NREM. |
| Author | Wang, Jinhai Wei, Ran Zhang, Xinghua Dang, Xin |
| Author_xml | – sequence: 1 givenname: Ran surname: Wei fullname: Wei, Ran organization: School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin Medical Electronic Treating-Technology Engineering Center – sequence: 2 givenname: Xinghua surname: Zhang fullname: Zhang, Xinghua organization: School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin Medical Electronic Treating-Technology Engineering Center – sequence: 3 givenname: Jinhai surname: Wang fullname: Wang, Jinhai email: tjpubme@126.com organization: School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin Medical Electronic Treating-Technology Engineering Center – sequence: 4 givenname: Xin surname: Dang fullname: Dang, Xin organization: School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin Medical Electronic Treating-Technology Engineering Center |
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| Cites_doi | 10.1016/j.compbiomed.2016.09.018 10.1152/ajpheart.2000.278.6.H2039 10.1109/TBME.2005.869773 10.2337/diaspect.29.1.5 10.1088/0967-3334/36/10/2027 10.1109/TBME.2014.2301462 10.1590/1517-86922014200201357 10.1001/archpsyc.1969.01740140118016 10.1109/JBHI.2013.2276083 10.1037/a0037489 10.1007/s11356-016-7812-9 10.1109/TSMCA.2012.2192264 10.1109/TBME.2003.817636 10.1504/IJBET.2010.032695 10.1016/j.cmpb.2013.06.007 10.1109/IEMBS.2008.4649365 |
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| SubjectTerms | Artificial neural networks Biological and Medical Physics Biomedical Engineering and Bioengineering Biomedicine Biophysics Classification EEG EKG Electrocardiography Electroencephalography Engineering Feature extraction Medical and Radiation Physics Model accuracy Model testing Neural networks NREM sleep Original Original Article Recording REM sleep Sleep Sleep and wakefulness 의공학 |
| Title | The research of sleep staging based on single-lead electrocardiogram and deep neural network |
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