Authors:
Amro Khasawneh
1
;
Sergio A. Alvarez
2
;
Carolina Ruiz
1
;
Shivin Misra
1
and
Majaz Moonis
3
Affiliations:
1
Worcester Polytech. Inst., United States
;
2
Boston College, United States
;
3
U. Massachusetts Medical School, United States
Keyword(s):
Clustering, EEG, ECG, HRV, Sleep.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Medical and Nursing Informatics
;
Pattern Recognition and Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
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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