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Multiple physiological signal-based human stress identification using non-linear classifiers

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Main Authors: Karthikeyan, Palanisamy, Murugappan, Muthusamy, Dr., Sazali, Yaacob, Prof. Dr.
Other Authors: karthi_209170@yahoo.com
Format: Article
Language:English
Published: 2014
Subjects:
Human stress
Mental arithmetic task
Multiple physiological signals
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/33942
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spelling my.unimap-339422014-04-23T04:35:20Z Multiple physiological signal-based human stress identification using non-linear classifiers Karthikeyan, Palanisamy Murugappan, Muthusamy, Dr. Sazali, Yaacob, Prof. Dr. karthi_209170@yahoo.com murugappan@unimap.edu.my s.yaacob@unimap.edu.my Human stress Mental arithmetic task Multiple physiological signals Link to publisher's homepage at This paper describes the human stress identification using multiple physiological signals. The Electrocardiogram (ECG), Electromyogram (EMG), Heart Rate Variability (HRV), Galvanic Skin Response (GSR), and Skin Temperature (ST) are the multiple physiological signals acquired and derived from the 40 subjects using mental arithmetic task -based stress-inducing stimuli. To compute the stress induced in the participated subjects, the wavelet denoising, digital elliptic filtering, ectopic beat removal algorithm, Lomb-Scargle (LS) periodogram, Fast Fourier Transform (FFT), and startle detection algorithms are the signal processing methods used to extract the various features of five physiological signals. K Nearest Neighbour (KNN) and Probabilistic Neural Network (PNN) are the nonlinear classifiers used to discriminate the normal and stress states of of the subjects. In order to strengthen the multiple evidence-based stress identification system, we investigated the Higher-Order Statistical (HOS) features in HRV signals that successfully in various applications in cardiac fault detection. Similarly, to evaluate the efficacy of the electromyogram (EMG), galvanic skin response (GSR), and skin temperature (ST), the existing statistical features are considered with a large number of data samples in stress research. The results indicate that the proposed HOS of HRV performed well, with accuracy up to 93.75 %. In other extreme, 76.25 %, 71.25 %, 70.32 %, and 75.32 % were obtained in ECG, EMG, GSR, and ST, respectively. Finally, this study concludes that multiple physiological signal-based subject-independent analyses incorporated and its algorithm gives the reasonably improved detection rate. 2014-04-23T04:35:20Z 2014-04-23T04:35:20Z 2013 Article Elektronika ir Elektrotechnika, vol. 19(7), 2013, pages 80-85 1392-1215 (P) 2029-5731 (O) http://www.eejournal.ktu.lt/index.php/elt/article/view/2232 http://dspace.unimap.edu.my:80/dspace/handle/123456789/33942 en
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Human stress
Mental arithmetic task
Multiple physiological signals
spellingShingle Human stress
Mental arithmetic task
Multiple physiological signals
Karthikeyan, Palanisamy
Murugappan, Muthusamy, Dr.
Sazali, Yaacob, Prof. Dr.
Multiple physiological signal-based human stress identification using non-linear classifiers
description Link to publisher's homepage at
author2 karthi_209170@yahoo.com
author_facet karthi_209170@yahoo.com
Karthikeyan, Palanisamy
Murugappan, Muthusamy, Dr.
Sazali, Yaacob, Prof. Dr.
format Article
author Karthikeyan, Palanisamy
Murugappan, Muthusamy, Dr.
Sazali, Yaacob, Prof. Dr.
author_sort Karthikeyan, Palanisamy
title Multiple physiological signal-based human stress identification using non-linear classifiers
title_short Multiple physiological signal-based human stress identification using non-linear classifiers
title_full Multiple physiological signal-based human stress identification using non-linear classifiers
title_fullStr Multiple physiological signal-based human stress identification using non-linear classifiers
title_full_unstemmed Multiple physiological signal-based human stress identification using non-linear classifiers
title_sort multiple physiological signal-based human stress identification using non-linear classifiers
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/33942
_version_ 1643797353919938560
score 13.222552

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