Amro Khasawneh, Sergio A. Alvarez, Carolina Ruiz, Shivin Misra, Majaz Moonis


Unsupervised clustering of staged human polysomnographic recordings reveals a hierarchy of sleep composition types described primarily by sleep efficiency and slow wave sleep content. Associations are found between these sleep clusters and health-related variables including BMI, smoking habits, and heart disease, showing that sleep types correspond to objective and medically relevant groupings. The present work describes the sleep type hierarchy, and studies the EEG and ECG correlates of sleep composition type. It is found that measures of EEG variation such as δ, θ, and α spectral content, as well as average heart rate, and measures of heart rate variability, including the standard deviation of the sequence of RR intervals, and Hjörth activity and mobility of the ECG signal, differ significantly among sleep composition type clusters. EEG analysis is shown to allow approximate reconstruction of sleep type without the need for ECG data, while ECG alone is found to be insufficient for accurate sleep type classification.


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Paper Citation

in Harvard Style

Khasawneh A., A. Alvarez S., Ruiz C., Misra S. and Moonis M. (2011). EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011) ISBN 978-989-8425-34-8, pages 97-106. DOI: 10.5220/0003173900970106

in Bibtex Style

author={Amro Khasawneh and Sergio A. Alvarez and Carolina Ruiz and Shivin Misra and Majaz Moonis},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)},

in EndNote Style

JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)
SN - 978-989-8425-34-8
AU - Khasawneh A.
AU - A. Alvarez S.
AU - Ruiz C.
AU - Misra S.
AU - Moonis M.
PY - 2011
SP - 97
EP - 106
DO - 10.5220/0003173900970106