Sleep monitoring classification strategy for an unobtrusive EEG system

The advances in the wearable devices and Artificial Intelligence domains highlight the need for ICT systems that aim in the improvement of human's quality of life. In this paper we present the sleeping tracking component of an activity and sleeping tracking system. We present the sleep quality assessment based on EEG processing and support vector machines with sequential minimal optimization classifiers (SVM-SMO). The performance of the system demonstrated by respective experiments (accuracy: 83% and kappa coeff: 72%) exhibits significant prospects for the assessment of sleep quality and the further validation through an evaluation study.

[1]  P. Welch.  The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms , 1967 .

[2]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[3]  B. Saletu,et al.  Sleep and quality of life in the Austrian population , 2000, Acta neurologica Scandinavica.

[4]  Dae-Jin Kim,et al.  Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data , 2002, Comput. Biol. Medicine.

[5]  Marina Ronzhina,et al.  Sleep scoring using artificial neural networks. , 2012, Sleep medicine reviews.

[6]  R. Rosenberg,et al.  The American Academy of Sleep Medicine inter-scorer reliability program: sleep stage scoring. , 2013, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[7]  Fanglin Chen,et al.  Unobtrusive sleep monitoring using smartphones , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[8]  Evangelia I. Zacharaki,et al.  Online Seizure Detection from EEG and ECG Signals for Monitoring of Epileptic Patients , 2014, SETN.

[9]  Joseph Wei,et al.  How Wearables Intersect with the Cloud and the Internet of Things : Considerations for the developers of wearables. , 2014, IEEE Consumer Electronics Magazine.

[10]  S. Youngstedt,et al.  Sleep quality improved following a single session of moderate-intensity aerobic exercise in older women: Results from a pilot study. , 2014, Journal of sport and health science.

[11]  Chin-Teng Lin,et al.  Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels , 2014, Front. Neurosci..

[12]  Y. Bhagat,et al.  Clinical validation of a wrist actigraphy mobile health device for sleep efficiency analysis , 2014, 2014 IEEE Healthcare Innovation Conference (HIC).

[13]  Christoph Kalkbrenner,et al.  Sleep monitoring using body sounds and motion tracking , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Mannes Poel,et al.  Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.