Meet Our Workshop Instructor

Dr. Mohammed Imamul Hassan Bhuiyan


Department of Electrical and Electronic Engineering

Bangladesh University of Engineering and Technology (BUET).

Workshop Title: Detection of cyclic alternating pattern in sleep with deep neural network

Date : 31st December 2022
Time: 11.30 AM – 01.00 PM (Indian Standard Time)
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Meeting Link:

Meeting ID: 846 636 6580

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Click here to read the Abstract

Sleep is an essential part of our health, physical and mental, and overall well-being. There are a variety of sleep related disorders such as insomnia, sleep apnea, narcolepsy and hypersomnia that affect a significant percentage of the populace. Moreover, sleep disorders are often related to other ailments that include diabetes, stroke, obesity and cardiovascular diseases. About 50 to 70 million people is USA suffer from sleep disorders and undiagnosed sleep apnea alone costs 150 billion dollars annually. In addition to being a serious health concern, it reduces productivity and may cause accidents in workplace. 

Electroencephalography (EEG) signals are effective descriptors of sleep stages, a cyclic repetition of several patterns that can broadly categorized into AWAKE, non-rapid eye movement (NREM) and rapid eye movement (REM). NREM stage includes the slow wave sleep (SWS) stages. An important micro-structure in NREM is cyclic alternating patterns (CAP) which are basically periodic EEG activity lasting between 2 to 60 seconds, and closely associated with sleep stability and disruptions. These patterns are of two types: A –phase and B-phase while the A-phase has three subtypes. Detection of these patterns is important for diagnosing and monitoring of various sleep disorders and improving sleep health. It may also be helpful for various applications e.g. driver drowsiness detection, sleep tracking and study of food cravings etc.

 In this tutorial, first the sleep stages and subsequently CAP will be introduced. The need for the detection of CAP will be briefly explained. Subsequently, the state-of-the art in the identification of CAP will be discussed including the ones employing machine learning. Consequently, a deep-learning neural network model exploiting attention mechanism for detecting CAP will be described. Experimental results on publicly available CAP database from Physionet will be presented and compared with those of other recent methods. Limitations and future prospects will be outlined afterwards.

Dr. Mohammed Imamul Hassan Bhuiyan is a Professor in the Department of Electrical and Electronic Engineering of Bangladesh University of Engineering and Technology (BUET). He obtained his B.Sc. and M.Sc.  degree from BUET in 1998 and 2001, respectively, and later Ph.D. from Concordia University, Montreal, Canada in 2007. He has more than 100 publications in peer reviewed journals and international conference proceedings. His major research interests are biomedical signal and image processing, machine learning, statistical modeling of signal, image and video, applications of signal processing in genomics, telecommunications and power systems etc. He has got several awards including Best Paper Award from Japan Signal Processing Society and IEEE Lance Stafford Larson Student Scholarship for Best Student Paper from IEEE Computer Society. He is a Senior Member of IEEE.