Evaluation of the Sleep Quality based on bed sensor signals: Time-Variant Analysis

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

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

6 Citations (Scopus)

Abstract

Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM -Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and b) the performance of K-Nearest Neighbor (KNN) and Feed Forward Neural Networks (FFNN) classifiers. In the current analysis, 17 full polysomnography recordings from healthy subjects were used. The best agreement for Wake- NREM-REM with respect to the gold standard was 71.95 ± 7.47% of accuracy and 0.42 ± 0.10 of kappa index for TVAMLD while WD-FFNN shows 67.17 ± 11.88% of accuracy and 0.39 ± 0.13 of kappa index. The results suggest that the sleep quality assessment out of sleep centers could be possible and as consequence more people could be beneficiated.

Original languageEnglish
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages3994-3997
Number of pages4
ISBN (Electronic)978-1-4244-4124-2
ISBN (Print)978-1-4244-4123-5
DOIs
Publication statusPublished - 2010
MoE publication typeA4 Article in a conference publication
Event32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: 31 Aug 20104 Sep 2010

Conference

Conference32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Abbreviated titleEMBC'10
CountryArgentina
CityBuenos Aires
Period31/08/104/09/10

Fingerprint

Sensors
Wavelet decomposition
Eye movements
Feedforward neural networks
Classifiers
Sleep

Keywords

  • feature extraction
  • sleep
  • indexes
  • heart rate variability
  • accuracy
  • monitoring
  • pathology

Cite this

Mendez, M. O., Migliorini, M., Kortelainen, J. M., Nistico, D., Arce-Santana, E., Cerutti, S., & Bianchi, A. M. (2010). Evaluation of the Sleep Quality based on bed sensor signals: Time-Variant Analysis. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 (pp. 3994-3997). [5628005] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/IEMBS.2010.5628005
Mendez, Martin O. ; Migliorini, Matteo ; Kortelainen, Juha M. ; Nistico, Domenino ; Arce-Santana, Edgar ; Cerutti, Sergio ; Bianchi, Anna M. / Evaluation of the Sleep Quality based on bed sensor signals : Time-Variant Analysis. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10. IEEE Institute of Electrical and Electronic Engineers , 2010. pp. 3994-3997
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title = "Evaluation of the Sleep Quality based on bed sensor signals: Time-Variant Analysis",
abstract = "Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM -Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and b) the performance of K-Nearest Neighbor (KNN) and Feed Forward Neural Networks (FFNN) classifiers. In the current analysis, 17 full polysomnography recordings from healthy subjects were used. The best agreement for Wake- NREM-REM with respect to the gold standard was 71.95 ± 7.47{\%} of accuracy and 0.42 ± 0.10 of kappa index for TVAMLD while WD-FFNN shows 67.17 ± 11.88{\%} of accuracy and 0.39 ± 0.13 of kappa index. The results suggest that the sleep quality assessment out of sleep centers could be possible and as consequence more people could be beneficiated.",
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Mendez, MO, Migliorini, M, Kortelainen, JM, Nistico, D, Arce-Santana, E, Cerutti, S & Bianchi, AM 2010, Evaluation of the Sleep Quality based on bed sensor signals: Time-Variant Analysis. in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10., 5628005, IEEE Institute of Electrical and Electronic Engineers , pp. 3994-3997, 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, Buenos Aires, Argentina, 31/08/10. https://doi.org/10.1109/IEMBS.2010.5628005

Evaluation of the Sleep Quality based on bed sensor signals : Time-Variant Analysis. / Mendez, Martin O.; Migliorini, Matteo; Kortelainen, Juha M.; Nistico, Domenino; Arce-Santana, Edgar; Cerutti, Sergio; Bianchi, Anna M.

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10. IEEE Institute of Electrical and Electronic Engineers , 2010. p. 3994-3997 5628005.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

TY - GEN

T1 - Evaluation of the Sleep Quality based on bed sensor signals

T2 - Time-Variant Analysis

AU - Mendez, Martin O.

AU - Migliorini, Matteo

AU - Kortelainen, Juha M.

AU - Nistico, Domenino

AU - Arce-Santana, Edgar

AU - Cerutti, Sergio

AU - Bianchi, Anna M.

N1 - Project code: 18982

PY - 2010

Y1 - 2010

N2 - Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM -Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and b) the performance of K-Nearest Neighbor (KNN) and Feed Forward Neural Networks (FFNN) classifiers. In the current analysis, 17 full polysomnography recordings from healthy subjects were used. The best agreement for Wake- NREM-REM with respect to the gold standard was 71.95 ± 7.47% of accuracy and 0.42 ± 0.10 of kappa index for TVAMLD while WD-FFNN shows 67.17 ± 11.88% of accuracy and 0.39 ± 0.13 of kappa index. The results suggest that the sleep quality assessment out of sleep centers could be possible and as consequence more people could be beneficiated.

AB - Automatic detection of the sleep macrostructure (Wake, NREM -non Rapid Eye Movement- and REM -Rapid Eye Movement-) based on bed sensor signals is presented. This study assesses the feasibility of different methodologies to evaluate the sleep quality out of sleep centers. The study compares a) the features extracted from time-variant autoregressive modeling (TVAM) and Wavelet Decomposition (WD) and b) the performance of K-Nearest Neighbor (KNN) and Feed Forward Neural Networks (FFNN) classifiers. In the current analysis, 17 full polysomnography recordings from healthy subjects were used. The best agreement for Wake- NREM-REM with respect to the gold standard was 71.95 ± 7.47% of accuracy and 0.42 ± 0.10 of kappa index for TVAMLD while WD-FFNN shows 67.17 ± 11.88% of accuracy and 0.39 ± 0.13 of kappa index. The results suggest that the sleep quality assessment out of sleep centers could be possible and as consequence more people could be beneficiated.

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KW - sleep

KW - indexes

KW - heart rate variability

KW - accuracy

KW - monitoring

KW - pathology

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DO - 10.1109/IEMBS.2010.5628005

M3 - Conference article in proceedings

SN - 978-1-4244-4123-5

SP - 3994

EP - 3997

BT - 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

PB - IEEE Institute of Electrical and Electronic Engineers

ER -

Mendez MO, Migliorini M, Kortelainen JM, Nistico D, Arce-Santana E, Cerutti S et al. Evaluation of the Sleep Quality based on bed sensor signals: Time-Variant Analysis. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10. IEEE Institute of Electrical and Electronic Engineers . 2010. p. 3994-3997. 5628005 https://doi.org/10.1109/IEMBS.2010.5628005