Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors

Matteo Migliorini; Anna M. Bianchi; Juha M. Kortelainen; Edgar Arce-Santana; Sergio Cerutti; Martin O. Mendez; Domenico Nisticò

doi:10.1109/IEMBS.2010.5627217

Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors

Matteo Migliorini, Anna M. Bianchi, Juha M. Kortelainen, Edgar Arce-Santana, Sergio Cerutti, Martin O. Mendez, Domenico Nisticò

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

43 Citations (Scopus)

Abstract

This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51% and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers.

Original language	English
Title of host publication	2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010)
Subtitle of host publication	Buenos Aires, Argentina, 31 Aug. - 4 Sept. 2010
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	3273-3276
ISBN (Print)	978-1-4244-4123-5, 978-1-4244-4124-2
DOIs	https://doi.org/10.1109/IEMBS.2010.5627217
Publication status	Published - 2010
MoE publication type	A4 Article in a conference publication

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Migliorini, M., Bianchi, A. M., Kortelainen, J. M., Arce-Santana, E., Cerutti, S., Mendez, M. O., & Nisticò, D. (2010). Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors. In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010): Buenos Aires, Argentina, 31 Aug. - 4 Sept. 2010 (pp. 3273-3276). IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/IEMBS.2010.5627217

Migliorini, Matteo ; Bianchi, Anna M. ; Kortelainen, Juha M. et al. / Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors. 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010): Buenos Aires, Argentina, 31 Aug. - 4 Sept. 2010. Piscataway, NJ, USA : IEEE Institute of Electrical and Electronic Engineers, 2010. pp. 3273-3276

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title = "Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors",

abstract = "This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51% and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers.",

author = "Matteo Migliorini and Bianchi, {Anna M.} and Kortelainen, {Juha M.} and Edgar Arce-Santana and Sergio Cerutti and Mendez, {Martin O.} and Domenico Nistic{\`o}",

year = "2010",

doi = "10.1109/IEMBS.2010.5627217",

language = "English",

isbn = "978-1-4244-4123-5",

pages = "3273--3276",

booktitle = "2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010)",

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Migliorini, M, Bianchi, AM, Kortelainen, JM, Arce-Santana, E, Cerutti, S, Mendez, MO & Nisticò, D 2010, Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors. in 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010): Buenos Aires, Argentina, 31 Aug. - 4 Sept. 2010. IEEE Institute of Electrical and Electronic Engineers, Piscataway, NJ, USA, pp. 3273-3276. https://doi.org/10.1109/IEMBS.2010.5627217

Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors. / Migliorini, Matteo; Bianchi, Anna M.; Kortelainen, Juha M. et al.
2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010): Buenos Aires, Argentina, 31 Aug. - 4 Sept. 2010. Piscataway, NJ, USA: IEEE Institute of Electrical and Electronic Engineers, 2010. p. 3273-3276.

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AU - Migliorini, Matteo

AU - Bianchi, Anna M.

AU - Kortelainen, Juha M.

AU - Arce-Santana, Edgar

AU - Cerutti, Sergio

AU - Mendez, Martin O.

AU - Nisticò, Domenico

PY - 2010

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N2 - This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51% and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers.

AB - This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant for staging sleep in REM, nonREM and WAKE periods. The performances of all the possible combinations of feature extractors and classifiers are compared in terms of accuracy and kappa index, using clinical polysomographyc evaluation as golden standard. 17 recordings from healthy subjects, including also polisomnography, were used to train and test the algorithms. When automatic classification is compared. QD-TVAM algorithm achieved a total accuracy of 76.81 ± 7.51% and kappa index of 0.55 ± 0.10, while LD-WDT achieved a total accuracy of 79 ± 10% and kappa index of 0.51 ± 0.17. The results suggest that a good sleep evaluation can be achieved through non-conventional recording systems that could be used outside sleep centers.

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DO - 10.1109/IEMBS.2010.5627217

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SN - 978-1-4244-4124-2

SP - 3273

EP - 3276

BT - 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010)

PB - IEEE Institute of Electrical and Electronic Engineers

CY - Piscataway, NJ, USA

ER -

Migliorini M, Bianchi AM, Kortelainen JM, Arce-Santana E, Cerutti S, Mendez MO et al. Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors. In 2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2010): Buenos Aires, Argentina, 31 Aug. - 4 Sept. 2010. Piscataway, NJ, USA: IEEE Institute of Electrical and Electronic Engineers. 2010. p. 3273-3276 doi: 10.1109/IEMBS.2010.5627217

Automatic sleep staging based on ballistocardiographic signals recorded through bed sensors

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