NDLI: Hybrid SVM classification technique to detect mental stress in human beings using ECG signals

Content Provider	IEEE Xplore Digital Library
Author	Vanitha, L. Suresh, G.R.
Copyright Year	2013
Description	Author affiliation: ECE Loyola Inst. of Technol., Chennai, India (Vanitha, L.) \|\| ECE Easwari Eng. Coll., Chennai, India (Suresh, G.R.)
Abstract	In recent years, stress has become ingrained part of our life, being stressed by our financial worries, our job, etc. Stress causes physical illnesses, such as heart attacks, arthritis, and chronic headaches or psychological diseases like mental illness, anger, anxiety, and depression. There are several research works coming up to resolve the limitations on measuring, analyzing and identifying the human stress levels Amongst the many stress monitoring methods the more reliable method to determine the human stress level is to use physiological signals. In this work, Heart Rate Variability (HRV) determined from ECG signal, an efficient parameter to detect the stress level is used. The features extracted from HRV are given as input, to the two stage classifier, to classify the stress into one of the four levels as no stress, low stress, medium stress and high stress. In the first stage of the classifier, Self Organizing Map is used to classify the stress into two classes as `stress level 1'(no stress & low stress) and `stress level 2' (medium stress & high stress). In the second stage Support Vector Machine is used with RBF kernel to subdivide the `stress level 1' into two classes `No Stress' and `Low Stress'. The stress level 2 is subdivided into twoclasses `Medium Stress' and `High Stress'. The performance of this hybrid structure is better and the efficiency of classification is 91%.
Starting Page	1
Ending Page	6
File Size	343245
Page Count	6
File Format	PDF
e-ISBN	9781479935062
DOI	10.1109/ICACCS.2013.6938735
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-19
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Stress ECG HRV Frequency-domain analysis Electrocardiography Feature extraction Heart rate variability Stress Stress measurement
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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