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Med Eng Phys. 2015 Mar;37(3):297-308. doi: 10.1016/j.medengphy.2015.01.002. Epub 2015 Jan 29.

Mutual information measures applied to EEG signals for sleepiness characterization.

Author information

1
Department of ESAII, Centre for Biomedical Engineering Research, Universitat Politècnica de Catalunya, CIBER-BBN, Barcelona, Spain. Electronic address: umberto.melia@upc.edu.
2
Multidisciplinary Sleep Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain; Institut d' Investigació Biomèdica August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
3
Department of ESAII, Centre for Biomedical Engineering Research, Universitat Politècnica de Catalunya, CIBER-BBN, Barcelona, Spain.
4
Multidisciplinary Sleep Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Pneumology, Hospital Clinic, Barcelona, Spain; Ciber Enfermedades Respiratorias (CIBERES), Madrid, Spain; Medical School, University of Barcelona, Spain.
5
Multidisciplinary Sleep Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Otorhinolaryngology, Hospital Clinic, Barcelona, Spain; Ciber Enfermedades Respiratorias (CIBERES), Madrid, Spain; Medical School, University of Barcelona, Spain.
6
Multidisciplinary Sleep Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Psychiatry, Hospital Clinic, Barcelona, Spain; Institut d' Investigació Biomèdica August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Medical School, University of Barcelona, Spain.
7
Multidisciplinary Sleep Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain; Department of Neurology, Hospital Clinic, Barcelona, Spain; Institut d' Investigació Biomèdica August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Ciber Enfermedades Neurológicas (CIBERNED), Barcelona, Spain; Medical School, University of Barcelona, Spain.

Abstract

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders with a great impact on the patient lives. While many studies have been carried out in order to assess daytime sleepiness, the automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on non-linear dynamical analysis of EEG signal was proposed. Multichannel EEG signals were recorded during five maintenance of wakefulness (MWT) and multiple sleep latency (MSLT) tests alternated throughout the day from patients suffering from sleep disordered breathing. A group of 20 patients with excessive daytime sleepiness (EDS) was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60-s EEG windows in waking state. Measures obtained from cross-mutual information function (CMIF) and auto-mutual-information function (AMIF) were calculated in the EEG. These functions permitted a quantification of the complexity properties of the EEG signal and the non-linear couplings between different zones of the scalp. Statistical differences between EDS and WDS groups were found in β band during MSLT events (p-value < 0.0001). WDS group presented more complexity than EDS in the occipital zone, while a stronger nonlinear coupling between occipital and frontal zones was detected in EDS patients than in WDS. The AMIF and CMIF measures yielded sensitivity and specificity above 80% and AUC of ROC above 0.85 in classifying EDS and WDS patients.

KEYWORDS:

Biomedical signal processing; Complexity theory; EEG; Electroncephalography; Excessive daytime sleepiness; Mutual information

PMID:
25638417
DOI:
10.1016/j.medengphy.2015.01.002
[Indexed for MEDLINE]
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