Adaptive Neuro Fuzzy Inference System Technique on Polysomnographs for the detection of Stressful Conditions

ABSTRACT

A method for computerized detection of heat stress is presented in this paper and tested on pre-recorded data from a range of subjects. The physiological changes that happen in the subjects are incorporated as fuzzy logic to distinguish the stress level of the subjects as chronic or acute stress. The sleep stage classification is done initially with the help of pre-established rules governed by American Academy of Sleep Medicine. After the sleep stage classification is done, data are further classified as chronic or acute stress with respect to their controlled states. The proposed algorithm employs adaptive neuro-fuzzy inference system and Mamdani fuzzy model and achieves an average classification accuracy of 89% in detecting stress levels.

KEYWORDS:

• AASM
• ANFIS
• Fuzzy system
• Heat stress
• Polysomnographs
• Signal processing
• Sleep stages

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Chetna Nagpal

Chetna Nagpal has received an MTech degree in Electronics and Communication from Maharishi Dayananad University, India in 2008 and BE degree in Instrumentation and Controls from Kurukshetra University, India in 2005. She is pursuing her PhD degree from Banasthali University. She has 7 years of teaching experience at various renowned universities in the UAE and India. Her research area includes signal processing with soft computing techniques. She has published five research papers in international journals and six research papers in international and national conferences.

Prabhat Kumar Upadhyay

Prabhat Kumar Upadhyay obtained his PhD (Technology) degree from Birla Institute of Technology, Mesra, India. He has been working as a faculty in the department of Electrical and Electronics Engineering of BIT Mesra campus and its offshore campuses in the UAE and Oman for the last 16 years. He has published more than 20 research papers in various journals and conferences. His current research interests include brain signals, signal processing, and soft computing.

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