AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Nano Biomedicine and Engineering Article
PDF (2.9 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Probabilistic Neural Network Based Fatigue Level Classification Using Electrocardiogram High Frequency Band and Average Heart Beat

Department of Computer Engineering, College of Technical Electrical Engineering, Middle Technical University, Baghdad, Iraq
Show Author Information

Abstract

The detection of fatigue level is important because it is the main reason of sudden death. This research depended on the average heartbeat of the electrocardiogram signal, and the features were extracted from its high frequency components. Therefore, there is great need to transform signal into frequency domain by discrete wavelet transform. In this research, 6 features were supplied to probabilistic neural network which gave accuracy of 60.56% of detecting high level among other levels of medium and low fatigue. This research studied the fatigue on 40 students due to mathematical exercises in a noisy environment with different stimuli.

Keywords

ElectrocardiogramAverage heartbeatDiscrete wavelet transformProbabilistic neural network

References

[1]

S. Hosseini, A. Mohammad, N. Mohammad, et al., Emotional, fatigue recognition using a new fusion link between electroencephalogram and peripheral signals. Iran J Neurol. 2015, 14(3): 142-151.
Google Scholar
[2]

M. Wali, A. Nabil, and A. Qais, Fatigue detection based on ECG using discrete wavelet transform. Almamoon J Col.; 2014, 12(2): 290-306.
Google Scholar
[3]

M. Appelhans, J. Linda. Heart rate variability as an index of regulated emotional responding. Review of General Psychology. 2006, 10(3): 229-240.
Crossref Google Scholar
[4]

K. Kalliopi, R. Bernd, S. Günther, et al., Detecting moments of fatigue from measurements of wearable physiological. Sensors. 2019, 19(17): 3805.
Crossref Google Scholar
[5]

B. Zeeshan, K. Manolya, A survey on psycho-physiological analysis & measurement methods in multimodal systems, Technologies Interact. 2019, 3(2), 37.
Crossref Google Scholar
[6]
U. Harunobu, N. Yusuke, The very low-frequency band of heart rate variability represents the slow recovery component after a mental fatigue task, PLOS ONE, 2017 open access.
[7]

L. Terryc, B. Nicola, and A. Tanit, Distraction from multiple in-vehicle secondary tasks: Vehicle performance and mental workload implications. Ergonomics, 2004, 47(1): 91-104.
Crossref Google Scholar
[8]

M. Hamza, A. Abdulaziz, S. Ebrahim, et al., Fatigue and its effects on medical students, J Health Popul Nutr. 2011, 29(5): 516-522.
Google Scholar
[9]

K. Aristotelis, G. Vasiliki, and D. Vasiliki, The Impact of healthcare workers job environment on their mental-emotional health. Health Psychol Res. 2015, 3(1): 1984.
Crossref Google Scholar
[10]
K. Hemang, K.P.S. Jayant, V. Kumar, et al., Efficient IIR notch filter design using minimax optimization for 50Hz noise suppression in ECG. Proceedings of the International Conference on Signal Processing, Computing and Control, 2015.
[11]
M. Wali, M. Murugappan, and B. Ahmmad, wavelet packet transform based driver distraction level classification using EEG. Mathematical Problems in Engineering, 2013: Article ID 297587, 10 pages.
Crossref
[12]

E.S. El-Dahshan, Genetic algorithm and wavelet hybrid scheme for ECG signal denoising. Telecommunication Systems, 2010; 46; 209-215.
Crossref Google Scholar
[13]

M. Alfouri, K. Dagrouq, ECG signal de noising by wavelet transform thresholding. American Journal of Applied Sciences, 2008; 5; 276-281.
Crossref Google Scholar
[14]

D.F. Specht, Probabilistic neural networks. Neural Networks, 1990, 3(1): 109-118.
Crossref Google Scholar
[15]

I. Daubechies, Orthonormal bases of compactly supported wavelets. Comm. Pure Appl. Math, 1988, 41: 909-996.
Crossref Google Scholar
[16]
P. Karthikeyan, M. Murugappan, and S. Yaacob, A review on fatigue inducement stimuli for assessing human fatigue using physiological signals. Proceedings of IEEE 7th Int Colloquium on Signal Processing and Its Applications, 2011: 420-425.
Crossref
[17]

P. Karthikeyan, M. Murugappan, and S. Yaacob, Detection of human stress using short term ECG and HRV signals. JMMB, 2013, 13(2).
Crossref Google Scholar
[18]

M. Athira, N. Sindhu, and S. Jerritta, Human stress detection using ECG and HRV signals. IJETMAS, 2017, 5(5).
Google Scholar
[19]

D. Shon, K. Im, J. Park, et al., Emotional fatigue state detection using genetic algorithm-based feature selection on EEG signals. Int. J. Environ. Res. Public Health, 2018, 15: 2461.
Crossref Google Scholar
[20]

S. Wonju, K. Namho, K. Sehyeon, et al., Deep ECG-respiration network (deeper net) for recognizing mental fatigue. Sensors, 2019, 19: 3021.
Crossref Google Scholar
[21]

A. Tlija, D. Istrate, A. Badii, et al., Fatigue level classification using heart rate variability. ASETS, 2019; 4(3): 38-46.
Crossref Google Scholar
[22]
B. Mazhar, E. Mohamed, ECG images classification using feature extraction based on wavelet transformation and neural network. Proceedings of ICGST, International Conference on AIML. Jun., 2006.
Nano Biomedicine and Engineering
Volume 12 Issue 2,
June 2020
Pages 132-138
DOI: 10.5101/nbe.v12i2.p132-138

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Cite this Report
Close
Close
Cite this article:
Wali MK. Probabilistic Neural Network Based Fatigue Level Classification Using Electrocardiogram High Frequency Band and Average Heart Beat. Nano Biomedicine and Engineering, 2020, 12(2): 132-138. https://doi.org/10.5101/nbe.v12i2.p132-138

620

Views

14

Downloads

1

Crossref

2

Scopus

Altmetrics
Received: 16 January 2020
Accepted: 13 April 2020
Published: 14 April 2020
© Mousa Kadhim Wali.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.