Adaptive Neuro Fuzzy Inference System Technique On Polysomnographs For The Detection Of Stressful Conditions

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.