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Nami, Mohammad, Mehrabi, Samrad, Derman, Sabri. (1396). Employing Neural Network Methods to Label Sleep EEG Micro-Arousals in Obstructive Sleep Apnea Syndrome. سامانه مدیریت نشریات علمی, 3(4), 221-226. doi: 10.32598/jamsat.3.4.221
Mohammad Nami; Samrad Mehrabi; Sabri Derman. "Employing Neural Network Methods to Label Sleep EEG Micro-Arousals in Obstructive Sleep Apnea Syndrome". سامانه مدیریت نشریات علمی, 3, 4, 1396, 221-226. doi: 10.32598/jamsat.3.4.221
Nami, Mohammad, Mehrabi, Samrad, Derman, Sabri. (1396). 'Employing Neural Network Methods to Label Sleep EEG Micro-Arousals in Obstructive Sleep Apnea Syndrome', سامانه مدیریت نشریات علمی, 3(4), pp. 221-226. doi: 10.32598/jamsat.3.4.221
Nami, Mohammad, Mehrabi, Samrad, Derman, Sabri. Employing Neural Network Methods to Label Sleep EEG Micro-Arousals in Obstructive Sleep Apnea Syndrome. سامانه مدیریت نشریات علمی, 1396; 3(4): 221-226. doi: 10.32598/jamsat.3.4.221

Employing Neural Network Methods to Label Sleep EEG Micro-Arousals in Obstructive Sleep Apnea Syndrome

Journal of Advanced Medical Sciences and Applied Technologies
مقاله 6، دوره 3، شماره 4، اسفند 2017، صفحه 221-226    اصل مقاله (1.2 M)
نوع مقاله: Methodology Report
شناسه دیجیتال (DOI): 10.32598/jamsat.3.4.221
نویسندگان
Mohammad Nami* 1؛ Samrad Mehrabi2؛ Sabri Derman3
1Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran.
2Sleep Disorders Laboratory, Namazi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.
3Sleep Disorders Unit, American Hospital, Koc Foundation, Istanbul, Turkey.
چکیده
Well-designed studies are essential to screen suspected cases of Obstructive Sleep Apnea Syndrome (OSAS) using the widely-referenced questionnaires and then to confirm the diagnosis by means of full Polysomnography (PSG), and finally to assess various variables of treatment efficacy and safety. Defining the severity index of OSAS based on the Apnea-Hypopnea Index (AHI), sleep marco- and micro-structural features (i.e. hypnogram and cyclic alternating patterns or CAPs), and neurocognitive functions would help better explain the treatment outcome. Using the neural network models on sleep data in OSAS sufferers is potentially expected to help the above goals. Determination of neurocognitive impairments in OSAS subjects in relation with disease severity indices and subsequent changes in microstructural changes (i.e. CAPs) in sleep Electroencephalography (EEG), would therefore be useful in defining individualized care and cognitive rehabilitation plans. The present methodology paper has attempted to address the above hypothesis in a clinical population from a hospital-based sleep disorders laboratory.
کلیدواژه‌ها
Sleep apnea؛ EEG؛ Microstructure؛ Arousals؛ Neural network methods
مراجع
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