A Hybrid Approach for Stress Prediction from Heart Rate Variability

Md Rahat Shahriar Zawad; Chowdhury Saleh Ahmed Rony; Md Yeaminul Haque; Md Hasan Al Banna; Mufti Mahmud; M. Shamim Kaiser

doi:10.1007/978-981-19-5191-6_10

A Hybrid Approach for Stress Prediction from Heart Rate Variability

Md Rahat Shahriar Zawad
, Chowdhury Saleh Ahmed Rony
, Md Yeaminul Haque
, Md Hasan Al Banna
, Mufti Mahmud^*
, M. Shamim Kaiser

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

Stress is a condition that causes a specific physiologicsal response. Heart rate variability (HRV) is a critical aspect in identifying stress. It is crucial for those who want to keep track of their wellness. Currently, numerous research is being conducted on stress prediction from HRV. The existing works in this field cover different data sets to identify stress, where significantly few models can predict stress with high accuracy. This work combines two well-known stress prediction data sets comprising HRV features named WESAD and SWELL-KW to compare twelve classical machine learning models and hybrid models. Finally, it proposes a hybrid stress prediction model that combines Artificial Neural Network (ANN) with Naive Bayes (NB). The proposed model performed auspiciously, having an accuracy of 95.75% within only 0.80 s. A stress prediction framework is also suggested based on the findings.

Original language	English
Title of host publication	Frontiers of ICT in Healthcare - Proceedings of EAIT 2022
Editors	Jyotsna Kumar Mandal, Debashis De
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	111-121
Number of pages	11
ISBN (Print)	9789811951909
DOIs	https://doi.org/10.1007/978-981-19-5191-6_10
State	Published - 2023
Externally published	Yes
Event	7th International Conference on Emerging Applications of Information Technology, EAIT 2022 - kolkata, India Duration: 27 Mar 2022 → 28 Mar 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	519 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	7th International Conference on Emerging Applications of Information Technology, EAIT 2022
Country/Territory	India
City	kolkata
Period	27/03/22 → 28/03/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

HRV
Hybrid method
Machine learning
Stress

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-19-5191-6_10

Cite this

Zawad, M. R. S., Rony, C. S. A., Haque, M. Y., Banna, M. H. A., Mahmud, M., & Kaiser, M. S. (2023). A Hybrid Approach for Stress Prediction from Heart Rate Variability. In J. K. Mandal, & D. De (Eds.), Frontiers of ICT in Healthcare - Proceedings of EAIT 2022 (pp. 111-121). (Lecture Notes in Networks and Systems; Vol. 519 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-5191-6_10

Zawad, Md Rahat Shahriar ; Rony, Chowdhury Saleh Ahmed ; Haque, Md Yeaminul et al. / A Hybrid Approach for Stress Prediction from Heart Rate Variability. Frontiers of ICT in Healthcare - Proceedings of EAIT 2022. editor / Jyotsna Kumar Mandal ; Debashis De. Springer Science and Business Media Deutschland GmbH, 2023. pp. 111-121 (Lecture Notes in Networks and Systems).

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title = "A Hybrid Approach for Stress Prediction from Heart Rate Variability",

abstract = "Stress is a condition that causes a specific physiologicsal response. Heart rate variability (HRV) is a critical aspect in identifying stress. It is crucial for those who want to keep track of their wellness. Currently, numerous research is being conducted on stress prediction from HRV. The existing works in this field cover different data sets to identify stress, where significantly few models can predict stress with high accuracy. This work combines two well-known stress prediction data sets comprising HRV features named WESAD and SWELL-KW to compare twelve classical machine learning models and hybrid models. Finally, it proposes a hybrid stress prediction model that combines Artificial Neural Network (ANN) with Naive Bayes (NB). The proposed model performed auspiciously, having an accuracy of 95.75\% within only 0.80 s. A stress prediction framework is also suggested based on the findings.",

keywords = "HRV, Hybrid method, Machine learning, Stress",

author = "Zawad, \{Md Rahat Shahriar\} and Rony, \{Chowdhury Saleh Ahmed\} and Haque, \{Md Yeaminul\} and Banna, \{Md Hasan Al\} and Mufti Mahmud and Kaiser, \{M. Shamim\}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 7th International Conference on Emerging Applications of Information Technology, EAIT 2022 ; Conference date: 27-03-2022 Through 28-03-2022",

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Zawad, MRS, Rony, CSA, Haque, MY, Banna, MHA, Mahmud, M & Kaiser, MS 2023, A Hybrid Approach for Stress Prediction from Heart Rate Variability. in JK Mandal & D De (eds), Frontiers of ICT in Healthcare - Proceedings of EAIT 2022. Lecture Notes in Networks and Systems, vol. 519 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 111-121, 7th International Conference on Emerging Applications of Information Technology, EAIT 2022, kolkata, India, 27/03/22. https://doi.org/10.1007/978-981-19-5191-6_10

A Hybrid Approach for Stress Prediction from Heart Rate Variability. / Zawad, Md Rahat Shahriar; Rony, Chowdhury Saleh Ahmed; Haque, Md Yeaminul et al.
Frontiers of ICT in Healthcare - Proceedings of EAIT 2022. ed. / Jyotsna Kumar Mandal; Debashis De. Springer Science and Business Media Deutschland GmbH, 2023. p. 111-121 (Lecture Notes in Networks and Systems; Vol. 519 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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