Investigating the Variation of Mental Fatigue and Attention Control of Obstructive Sleep Apnea Patients

Obstructive sleep apnea (OSA) is the most common respiratory sleep disorder. Mental fatigue and attention control difficulties during the day have been reported in several studies for patients who are diagnosed with OSA. At this study we recorded EEG signals during resting-state and selective attention ( Simon - Flanker ). Statistical analysis of time domain features of EEG signals showed that mobility and complexity are features that changed with respect to both Epworth score and AHI in patients with OSA.

[1]  N. Atalay,et al.  Attentional control is partially impaired in obstructive sleep apnea syndrome , 2013, Journal of sleep research.

[2]  Yu-Ri Lee,et al.  A Novel EEG Feature Extraction Method Using Hjorth Parameter , 2014 .

[3]  Faramarz GHARAGOZLOU,et al.  Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving , 2015, Iranian journal of public health.

[4]  Rui Chen,et al.  Elevated Serum Liver Enzymes in Patients with Obstructive Sleep Apnea-hypopnea Syndrome , 2015, Chinese medical journal.

[5]  R. Midilli,et al.  A Clinical Prediction Formula for Apnea-Hypopnea Index , 2014, International journal of otolaryngology.

[6]  M. Bayram,et al.  Investigation of obstructive sleep apnoea syndrome prevalence among long-distance drivers from Zonguldak, Turkey , 2013, Multidisciplinary Respiratory Medicine.

[7]  Masaaki Tanaka,et al.  Two types of mental fatigue affect spontaneous oscillatory brain activities in different ways , 2013, Behavioral and Brain Functions.

[8]  I. Karacan,et al.  Reliability and validity studies of the Turkish version of the Epworth Sleepiness Scale , 2008, Sleep and Breathing.

[9]  J. Pépin,et al.  Most obstructive sleep apnoea patients exhibit vigilance and attention deficits on an extended battery of tests , 2005, European Respiratory Journal.

[10]  Arun Kumar,et al.  Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network , 2017 .