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Lying position classification based on ECG waveform and random forest during sleep in healthy people

BACKGROUND: Several different lying positions, such as lying on the left side, supine, lying on the right side and prone position, existed when healthy people fell asleep. This article explored the influence of lying positions on the shape of ECG (electrocardiograph) waveform during sleep, and then...

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Autores principales: Pan, Hongze, Xu, Zhi, Yan, Hong, Gao, Yue, Chen, Zhanghuang, Song, Jinzhong, Zhang, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6118000/
https://www.ncbi.nlm.nih.gov/pubmed/30165874
http://dx.doi.org/10.1186/s12938-018-0548-7
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author Pan, Hongze
Xu, Zhi
Yan, Hong
Gao, Yue
Chen, Zhanghuang
Song, Jinzhong
Zhang, Yu
author_facet Pan, Hongze
Xu, Zhi
Yan, Hong
Gao, Yue
Chen, Zhanghuang
Song, Jinzhong
Zhang, Yu
author_sort Pan, Hongze
collection PubMed
description BACKGROUND: Several different lying positions, such as lying on the left side, supine, lying on the right side and prone position, existed when healthy people fell asleep. This article explored the influence of lying positions on the shape of ECG (electrocardiograph) waveform during sleep, and then lying position classification based on ECG waveform features and random forest was achieved. METHODS: By means of de-noising the overnight sleep ECG data from ISRUC website dataset, as well as extracting the waveform features, we calculated a total of 30 ECG waveform features, including 2 newly proposed features, S/R and ∠QSR. The means and significant difference level of these features within different lying positions were calculated, respectively. Then 12 features were selected for three kinds of classification schemes. RESULTS: The lying positions had comparatively less effect on time-limit features. QT interval and RR interval were significantly lower than that in supine ([Formula: see text] ). Significant differences appeared in most of the amplitude and double-direction features. When lying on the left side, the height of P wave and T wave, QRS area and T area, the QR potential difference and ∠QSR were significantly lower than those in supine ([Formula: see text] ). However, S/R was significantly greater on left than those in supine ([Formula: see text] ) and on right ([Formula: see text] ). The height of T wave and area under T wave were significantly higher in supine than those on right ([Formula: see text] ). For the subject specific classifier, a mean accuracy of 97.17% with Cohen’s kappa statistic κ of 0.91, and AUC > 0.97 were achieved. While the accuracy and κ dropped to 63.87% and 0.32, AUC > 0.66, respectively when the subject independent classifier was considered. CONCLUSIONS: When subjects were lying on the left side during sleep, due to the effect of gravity on heart, the position of heart changed, for example, turned and rotated, causing changes in the vectorcardiogram of frontal plane and horizontal plane, which lead to a change in ECG. When lying on the right side, the heart was upheld by the mediastinum, so that the degree of freedom was poor, and the ECG waveform was almost unchanged. The proposed method could be used as a technique for convenient lying position classification.
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spelling pubmed-61180002018-09-05 Lying position classification based on ECG waveform and random forest during sleep in healthy people Pan, Hongze Xu, Zhi Yan, Hong Gao, Yue Chen, Zhanghuang Song, Jinzhong Zhang, Yu Biomed Eng Online Research BACKGROUND: Several different lying positions, such as lying on the left side, supine, lying on the right side and prone position, existed when healthy people fell asleep. This article explored the influence of lying positions on the shape of ECG (electrocardiograph) waveform during sleep, and then lying position classification based on ECG waveform features and random forest was achieved. METHODS: By means of de-noising the overnight sleep ECG data from ISRUC website dataset, as well as extracting the waveform features, we calculated a total of 30 ECG waveform features, including 2 newly proposed features, S/R and ∠QSR. The means and significant difference level of these features within different lying positions were calculated, respectively. Then 12 features were selected for three kinds of classification schemes. RESULTS: The lying positions had comparatively less effect on time-limit features. QT interval and RR interval were significantly lower than that in supine ([Formula: see text] ). Significant differences appeared in most of the amplitude and double-direction features. When lying on the left side, the height of P wave and T wave, QRS area and T area, the QR potential difference and ∠QSR were significantly lower than those in supine ([Formula: see text] ). However, S/R was significantly greater on left than those in supine ([Formula: see text] ) and on right ([Formula: see text] ). The height of T wave and area under T wave were significantly higher in supine than those on right ([Formula: see text] ). For the subject specific classifier, a mean accuracy of 97.17% with Cohen’s kappa statistic κ of 0.91, and AUC > 0.97 were achieved. While the accuracy and κ dropped to 63.87% and 0.32, AUC > 0.66, respectively when the subject independent classifier was considered. CONCLUSIONS: When subjects were lying on the left side during sleep, due to the effect of gravity on heart, the position of heart changed, for example, turned and rotated, causing changes in the vectorcardiogram of frontal plane and horizontal plane, which lead to a change in ECG. When lying on the right side, the heart was upheld by the mediastinum, so that the degree of freedom was poor, and the ECG waveform was almost unchanged. The proposed method could be used as a technique for convenient lying position classification. BioMed Central 2018-08-30 /pmc/articles/PMC6118000/ /pubmed/30165874 http://dx.doi.org/10.1186/s12938-018-0548-7 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Pan, Hongze
Xu, Zhi
Yan, Hong
Gao, Yue
Chen, Zhanghuang
Song, Jinzhong
Zhang, Yu
Lying position classification based on ECG waveform and random forest during sleep in healthy people
title Lying position classification based on ECG waveform and random forest during sleep in healthy people
title_full Lying position classification based on ECG waveform and random forest during sleep in healthy people
title_fullStr Lying position classification based on ECG waveform and random forest during sleep in healthy people
title_full_unstemmed Lying position classification based on ECG waveform and random forest during sleep in healthy people
title_short Lying position classification based on ECG waveform and random forest during sleep in healthy people
title_sort lying position classification based on ecg waveform and random forest during sleep in healthy people
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6118000/
https://www.ncbi.nlm.nih.gov/pubmed/30165874
http://dx.doi.org/10.1186/s12938-018-0548-7
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