Mutual information measures applied to EEG signals for sleepiness characterization. Issue 3 (March 2015)

• Record Type: Journal Article
• Authors: Melia, Umberto; Guaita, Marc; Vallverdú, Montserrat; Embid, Cristina; Vilaseca, Isabel; Salamero, Manel; Santamaria, Joan
• Is Part Of: Medical engineering & physics. Volume 37:Issue 3(2015:Mar.)
• Page Start: 297
• Page End: 308
• Publication Date: 2015-03
• Subjects: Biomedical signal processing -- Complexity theory -- Electroncephalography -- EEG -- Excessive daytime sleepiness -- Mutual information; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2015.01.002
• Languages: English
• ISSNs: 1350-4533

Abstract

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. Highlights: We tested mutual information applied to EEG for assessing daytime sleepiness. Auto-mutual information described the complexity of the EEG signal. Cross-mutual information quantified the nonlinear coupling in EEG signal. Patients with sleepiness present less complexity and more non-linear coupling. Patients with and without sleepiness were accurately discriminated.