Characterising insomnia

A graph spectral theory approach

Ramiro Chaparro-Vargas, Beena Ahmed, Thomas Penzel, Dean Cvetkovic

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

Abstract

This paper introduces a computational approach to characterise healthy controls and insomniacs based on graph spectral theory. Based upon expert-generated hypnograms of sleep onset periods, a network of sleep stages transitions is derived to compute four similarity distances amongst subjects' sleeping patterns. A subsequent statistical analysis is performed to differentiate the 16-subject healthy group from a 16-patient disordered cohort. Our findings demonstrated that the similarity distances based on eigenvalues determination, i.e. d1 and d4 were the most reliable and robust measures to characterise insomniacs, discriminating 93% and 87% of the affected population, respectively.

Original languageEnglish
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages366-369
Number of pages4
Volume2015-November
ISBN (Electronic)9781424492718
DOIs
Publication statusPublished - 4 Nov 2015
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: 25 Aug 201529 Aug 2015

Other

Other37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
CountryItaly
CityMilan
Period25/8/1529/8/15

Fingerprint

Sleep Stages
Sleep Initiation and Maintenance Disorders
Healthy Volunteers
Sleep
Population
Statistical methods

ASJC Scopus subject areas

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

Cite this

Chaparro-Vargas, R., Ahmed, B., Penzel, T., & Cvetkovic, D. (2015). Characterising insomnia: A graph spectral theory approach. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 (Vol. 2015-November, pp. 366-369). [7318375] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2015.7318375

Characterising insomnia : A graph spectral theory approach. / Chaparro-Vargas, Ramiro; Ahmed, Beena; Penzel, Thomas; Cvetkovic, Dean.

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. p. 366-369 7318375.

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

Chaparro-Vargas, R, Ahmed, B, Penzel, T & Cvetkovic, D 2015, Characterising insomnia: A graph spectral theory approach. in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. vol. 2015-November, 7318375, Institute of Electrical and Electronics Engineers Inc., pp. 366-369, 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015, Milan, Italy, 25/8/15. https://doi.org/10.1109/EMBC.2015.7318375
Chaparro-Vargas R, Ahmed B, Penzel T, Cvetkovic D. Characterising insomnia: A graph spectral theory approach. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November. Institute of Electrical and Electronics Engineers Inc. 2015. p. 366-369. 7318375 https://doi.org/10.1109/EMBC.2015.7318375
Chaparro-Vargas, Ramiro ; Ahmed, Beena ; Penzel, Thomas ; Cvetkovic, Dean. / Characterising insomnia : A graph spectral theory approach. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015. Vol. 2015-November Institute of Electrical and Electronics Engineers Inc., 2015. pp. 366-369
@inproceedings{01fdb4cb49844c8d8f33e243efa28386,
title = "Characterising insomnia: A graph spectral theory approach",
abstract = "This paper introduces a computational approach to characterise healthy controls and insomniacs based on graph spectral theory. Based upon expert-generated hypnograms of sleep onset periods, a network of sleep stages transitions is derived to compute four similarity distances amongst subjects' sleeping patterns. A subsequent statistical analysis is performed to differentiate the 16-subject healthy group from a 16-patient disordered cohort. Our findings demonstrated that the similarity distances based on eigenvalues determination, i.e. d1 and d4 were the most reliable and robust measures to characterise insomniacs, discriminating 93{\%} and 87{\%} of the affected population, respectively.",
author = "Ramiro Chaparro-Vargas and Beena Ahmed and Thomas Penzel and Dean Cvetkovic",
year = "2015",
month = "11",
day = "4",
doi = "10.1109/EMBC.2015.7318375",
language = "English",
volume = "2015-November",
pages = "366--369",
booktitle = "2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Characterising insomnia

T2 - A graph spectral theory approach

AU - Chaparro-Vargas, Ramiro

AU - Ahmed, Beena

AU - Penzel, Thomas

AU - Cvetkovic, Dean

PY - 2015/11/4

Y1 - 2015/11/4

N2 - This paper introduces a computational approach to characterise healthy controls and insomniacs based on graph spectral theory. Based upon expert-generated hypnograms of sleep onset periods, a network of sleep stages transitions is derived to compute four similarity distances amongst subjects' sleeping patterns. A subsequent statistical analysis is performed to differentiate the 16-subject healthy group from a 16-patient disordered cohort. Our findings demonstrated that the similarity distances based on eigenvalues determination, i.e. d1 and d4 were the most reliable and robust measures to characterise insomniacs, discriminating 93% and 87% of the affected population, respectively.

AB - This paper introduces a computational approach to characterise healthy controls and insomniacs based on graph spectral theory. Based upon expert-generated hypnograms of sleep onset periods, a network of sleep stages transitions is derived to compute four similarity distances amongst subjects' sleeping patterns. A subsequent statistical analysis is performed to differentiate the 16-subject healthy group from a 16-patient disordered cohort. Our findings demonstrated that the similarity distances based on eigenvalues determination, i.e. d1 and d4 were the most reliable and robust measures to characterise insomniacs, discriminating 93% and 87% of the affected population, respectively.

UR - http://www.scopus.com/inward/record.url?scp=84953249513&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84953249513&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2015.7318375

DO - 10.1109/EMBC.2015.7318375

M3 - Conference contribution

VL - 2015-November

SP - 366

EP - 369

BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -