How many sleep stages do we need for an efficient automatic insomnia diagnosis?

Sana Tmar Ben Hamida, Martin Glos, Thomas Penzel, Beena Ahmed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Tools used by clinicians to diagnose and treat insomnia typically include sleep diaries and questionnaires. Overnight polysomnography (PSG) recordings are used when the initial diagnosis is uncertain due to the presence of other sleep disorders or when the treatment, either behavioral or pharmacologic, is unsuccessful. However, the analysis and the scoring of PSG data are time-consuming. To simplify the diagnosis process, in this paper we have proposed an efficient insomnia detection algorithm based on a central single electroencephalographic (EEG) channel (C3) using only deep sleep. We also analyzed several spectral and statistical EEG features of good sleeper controls and subjects suffering from insomnia in different sleep stages to identify the features that offered the best discrimination between the two groups. Our proposed algorithm was evaluated using EEG recordings from 19 patients diagnosed with primary insomnia (11 females, 8 males) and 16 matched control subjects (11 females, 5 males). The sensitivity of our algorithm is 92%, the specificity is 89.9%, the Cohen's kappa is 0.81 and the agreement is 91%, indicating the effectiveness of our proposed method.

Original languageEnglish
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2431-2434
Number of pages4
Volume2016-October
ISBN (Electronic)9781457702204
DOIs
Publication statusPublished - 13 Oct 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: 16 Aug 201620 Aug 2016

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
CountryUnited States
CityOrlando
Period16/8/1620/8/16

Fingerprint

Sleep Stages
Sleep Initiation and Maintenance Disorders
Polysomnography
Sleep
Therapeutics

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Hamida, S. T. B., Glos, M., Penzel, T., & Ahmed, B. (2016). How many sleep stages do we need for an efficient automatic insomnia diagnosis? In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 (Vol. 2016-October, pp. 2431-2434). [7591221] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2016.7591221

How many sleep stages do we need for an efficient automatic insomnia diagnosis? / Hamida, Sana Tmar Ben; Glos, Martin; Penzel, Thomas; Ahmed, Beena.

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. p. 2431-2434 7591221.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Hamida, STB, Glos, M, Penzel, T & Ahmed, B 2016, How many sleep stages do we need for an efficient automatic insomnia diagnosis? in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. vol. 2016-October, 7591221, Institute of Electrical and Electronics Engineers Inc., pp. 2431-2434, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Orlando, United States, 16/8/16. https://doi.org/10.1109/EMBC.2016.7591221
Hamida STB, Glos M, Penzel T, Ahmed B. How many sleep stages do we need for an efficient automatic insomnia diagnosis? In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2431-2434. 7591221 https://doi.org/10.1109/EMBC.2016.7591221
Hamida, Sana Tmar Ben ; Glos, Martin ; Penzel, Thomas ; Ahmed, Beena. / How many sleep stages do we need for an efficient automatic insomnia diagnosis?. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Vol. 2016-October Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2431-2434
@inproceedings{562070df3d8148febb76d795bac121a4,
title = "How many sleep stages do we need for an efficient automatic insomnia diagnosis?",
abstract = "Tools used by clinicians to diagnose and treat insomnia typically include sleep diaries and questionnaires. Overnight polysomnography (PSG) recordings are used when the initial diagnosis is uncertain due to the presence of other sleep disorders or when the treatment, either behavioral or pharmacologic, is unsuccessful. However, the analysis and the scoring of PSG data are time-consuming. To simplify the diagnosis process, in this paper we have proposed an efficient insomnia detection algorithm based on a central single electroencephalographic (EEG) channel (C3) using only deep sleep. We also analyzed several spectral and statistical EEG features of good sleeper controls and subjects suffering from insomnia in different sleep stages to identify the features that offered the best discrimination between the two groups. Our proposed algorithm was evaluated using EEG recordings from 19 patients diagnosed with primary insomnia (11 females, 8 males) and 16 matched control subjects (11 females, 5 males). The sensitivity of our algorithm is 92{\%}, the specificity is 89.9{\%}, the Cohen's kappa is 0.81 and the agreement is 91{\%}, indicating the effectiveness of our proposed method.",
author = "Hamida, {Sana Tmar Ben} and Martin Glos and Thomas Penzel and Beena Ahmed",
year = "2016",
month = "10",
day = "13",
doi = "10.1109/EMBC.2016.7591221",
language = "English",
volume = "2016-October",
pages = "2431--2434",
booktitle = "2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - How many sleep stages do we need for an efficient automatic insomnia diagnosis?

AU - Hamida, Sana Tmar Ben

AU - Glos, Martin

AU - Penzel, Thomas

AU - Ahmed, Beena

PY - 2016/10/13

Y1 - 2016/10/13

N2 - Tools used by clinicians to diagnose and treat insomnia typically include sleep diaries and questionnaires. Overnight polysomnography (PSG) recordings are used when the initial diagnosis is uncertain due to the presence of other sleep disorders or when the treatment, either behavioral or pharmacologic, is unsuccessful. However, the analysis and the scoring of PSG data are time-consuming. To simplify the diagnosis process, in this paper we have proposed an efficient insomnia detection algorithm based on a central single electroencephalographic (EEG) channel (C3) using only deep sleep. We also analyzed several spectral and statistical EEG features of good sleeper controls and subjects suffering from insomnia in different sleep stages to identify the features that offered the best discrimination between the two groups. Our proposed algorithm was evaluated using EEG recordings from 19 patients diagnosed with primary insomnia (11 females, 8 males) and 16 matched control subjects (11 females, 5 males). The sensitivity of our algorithm is 92%, the specificity is 89.9%, the Cohen's kappa is 0.81 and the agreement is 91%, indicating the effectiveness of our proposed method.

AB - Tools used by clinicians to diagnose and treat insomnia typically include sleep diaries and questionnaires. Overnight polysomnography (PSG) recordings are used when the initial diagnosis is uncertain due to the presence of other sleep disorders or when the treatment, either behavioral or pharmacologic, is unsuccessful. However, the analysis and the scoring of PSG data are time-consuming. To simplify the diagnosis process, in this paper we have proposed an efficient insomnia detection algorithm based on a central single electroencephalographic (EEG) channel (C3) using only deep sleep. We also analyzed several spectral and statistical EEG features of good sleeper controls and subjects suffering from insomnia in different sleep stages to identify the features that offered the best discrimination between the two groups. Our proposed algorithm was evaluated using EEG recordings from 19 patients diagnosed with primary insomnia (11 females, 8 males) and 16 matched control subjects (11 females, 5 males). The sensitivity of our algorithm is 92%, the specificity is 89.9%, the Cohen's kappa is 0.81 and the agreement is 91%, indicating the effectiveness of our proposed method.

UR - http://www.scopus.com/inward/record.url?scp=85009063709&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85009063709&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2016.7591221

DO - 10.1109/EMBC.2016.7591221

M3 - Conference contribution

VL - 2016-October

SP - 2431

EP - 2434

BT - 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -