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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21305
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
Total Countries : 156
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| Paper Title: | Multi-Class Stress Detection Through Heart Rate Variability Using Deep Learning |
| Authors Name: | D. Jayasoniya , KOTHAMASU DIVYA VENKATA SAI LAKSHMISNEHA , METIKALA JYOTHSNA , JAHNAVI SAI MUTHYALA , KADAVAKUDURU SWATHI |
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IJRTI_205996
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| Published Paper Id: | IJRTI2509012 |
| Published In: | Volume 10 Issue 9, September-2025 |
| DOI: | |
| Abstract: | Stress, a prevalent psychological and physiological condition, significantly impacts mental well-being and physical health. Detecting stress accurately in its early stages can help prevent chronic disorders and improve quality of life. This study presents a deep learning-based approach for multi-class stress detection using Heart Rate Variability (HRV) data. By leveraging the SWELL-KW dataset, a 1D Convolutional Neural Network (CNN) is implemented to classify stress levels into three categories: No Stress, Interruption Stress, and Time Pressure Stress. The model integrates robust preprocessing, ANOVA-based feature selection, and optimized hyperparameters to enhance performance. Experimental results demonstrate a classification accuracy of 99.9%, significantly outperforming traditional models like SVM and Random Forest. The proposed system shows promise in providing a real-time, non-invasive solution for stress monitoring, thereby contributing to advancements in mental health technology and workplace wellness. |
| Keywords: | Heart Rate Variability (HRV); Stress Detection; 1D CNN; Deep Learning; SWELL-KW Dataset; ANOVA Feature Selection; Time-Domain Features; Frequency-Domain Features; Multi-Class Classification. |
| Cite Article: | "Multi-Class Stress Detection Through Heart Rate Variability Using Deep Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a108-a112, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509012.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2509012
Registration ID:205996
Published In: Volume 10 Issue 9, September-2025
DOI (Digital Object Identifier):
Page No: a108-a112 Country: HYDERABAD, Telangana, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509012 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509012 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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