Blind source separation for ambulatory sleep recording

• Published in: IEEE transactions on information technology in biomedicine Vol. 10; no. 2; pp. 293 - 301
• Main Authors: Poree, F., Kachenoura, A., Gauvrit, H., Morvan, C., Carrault, G., Senhadji, L.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2006; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Ambulatory recording; Artificial Intelligence; Bioengineering; Blind source separation; Cognitive science; Computer Science; Diagnosis, Computer-Assisted; Diagnosis, Computer-Assisted - methods; electrocardiogram (ECG); Electrocardiography; electroencephalogram (EEG); Electroencephalography; electromyogram (EMG); Electromyography; electrooculogram (EOG); Electrooculography; Engineering Sciences; home recording; Humanities and Social Sciences; Humans; Independent component analysis; independent component analysis (ICA); Laboratories; Life Sciences; Mathematics; Maximum likelihood detection; Methods and statistics; Monitoring, Ambulatory; Monitoring, Ambulatory - methods; Neuroscience; Pattern Recognition, Automated; Pattern Recognition, Automated - methods; Polysomnography; Polysomnography - methods; Reproducibility of Results; Sensitivity and Specificity; Signal and Image processing; sleep analysis; Sleep apnea; sleep apnea syndrome (SAS); Sleep Apnea Syndromes; Sleep Apnea Syndromes - diagnosis; sleep disorders; Sleep Stages; sleep staging; Statistics; Statistics Theory; Synthetic aperture sonar; Blind Source Separation; Deconvolution; BBS; Sleep; ICA; EEG; ECG; EMG
• ISSN: 1089-7771; 1558-0032
• DOI: 10.1109/TITB.2005.859878

This paper deals with the conception of a new system for sleep staging in ambulatory conditions. Sleep recording is performed by means of five electrodes: two temporal, two frontal and a reference. This configuration enables to avoid the chin area to enhance the quality of the muscular signal and the hair region for patient convenience. The electroencephalopgram (EEG), eletromyogram (EMG), and elctrooculogram (EOG) signals are separated using the Independent Component Analysis approach. The system is compared to a standard sleep analysis system using polysomnographic recordings of 14 patients. The overall concordance of 67.2% is achieved between the two systems. Based on the validation results and the computational efficiency we recommend the clinical use of the proposed system in a commercial sleep analysis platform