A Dual Path Hybrid Convolutional Neural Network and Bidirectional Long-Short Term Memory Approach for PPG-Based Stress Monitoring

A Dual Path Hybrid Convolutional Neural Network and Bidirectional Long-Short Term Memory Approach for PPG-Based Stress Monitoring | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 July 2025 V1 Latest version Share on A Dual Path Hybrid Convolutional Neural Network and Bidirectional Long-Short Term Memory Approach for PPG-Based Stress Monitoring Authors : Md Santo Ali 0009-0007-1831-5843 [email protected] , Mohammod Abdul Motin , and Mahmud Mufti Authors Info & Affiliations https://doi.org/10.22541/au.175329643.35949288/v1 184 views 126 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Mental stress adversely impacts both physical and mental health, with chronic stress leading to serious health concerns. Photoplethysmography (PPG) sensors, widely available in wearable devices, offer a convenient, cost-effective, and non-invasive method for stress monitoring. This study proposes a dual path hybrid convolutional neural network-bidirectional long shortterm memory (CNN-BiLSTM) hybrid architecture for real-time stress detection using only PPG signals. Trained and validated on the publicly available WESAD dataset, the model achieves exceptional performance metrics: 97.90% accuracy, 98.30% specificity, 97.20% sensitivity, 97.06% F1-score, 99.12% AUC, and 95.42% Cohen's kappa. The lightweight model exhibits high accuracy in stress detection while maintaining computational efficiency, making it particularly suitable for wearable devices. These results highlight the potential of this approach for practical real-time stress monitoring and management applications. Supplementary Material File (a_dual_path_hybrid_convolutional.pdf) Download 658.62 KB Information & Authors Information Version history V1 Version 1 23 July 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords deep learning dual path hybrid photoplethysmography (ppg) signal processing stress monitoring Authors Affiliations Md Santo Ali 0009-0007-1831-5843 [email protected] Department of Electrical & Electronic Engineering, Ra-jshahi University of Engineering & Technology View all articles by this author Mohammod Abdul Motin Department of Electrical & Electronic Engineering, Ra-jshahi University of Engineering & Technology View all articles by this author Mahmud Mufti Department of Information and Computer Sci-ence, Inter-disciplinary Research Center for Bio Systems and Machines, SDAIA-KFUPM Joint Research Center for AI, King Fahd University of Petroleum and Minerals View all articles by this author Metrics & Citations Metrics Article Usage 184 views 126 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Md Santo Ali, Mohammod Abdul Motin, Mahmud Mufti. A Dual Path Hybrid Convolutional Neural Network and Bidirectional Long-Short Term Memory Approach for PPG-Based Stress Monitoring. Authorea . 23 July 2025. DOI: https://doi.org/10.22541/au.175329643.35949288/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); Cited by Md Santo Ali, Mohammod Abdul Motin, Sapnil Sarker Bipro, Sumaiya Kabir, Md Khalid Syfullah, Dinesh K. Kumar, Detection of Mental Stress From Photoplethysmography for Under- Resourced Healthcare Systems, IEEE Sensors Journal, 26 , 4, (6293-6301), (2026). https://doi.org/10.1109/JSEN.2025.3649586 Crossref Md Rokonuzzaman Mim, Md Santo Ali, Mohammod Abdul Motin, A Convolutional Neural Network with Bidirectional Gated Recurrent Units Architecture for Enhanced Stress Monitoring from Wearable Signals, 2025 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON), (249-252), (2025). https://doi.org/10.1109/SPICSCON69221.2025.11504136 Crossref Md Santo Ali, Mohammod Abdul Motin, Md Rokonuzzaman Mim, Sumaiya Kabir, A Shallow Deep Learning Model for Stress Monitoring from Photoplethysmography Signals, 2025 IEEE International Conference on Signal Processing, Information, Communication and Systems (SPICSCON), (744-747), (2025). https://doi.org/10.1109/SPICSCON69221.2025.11504104 Crossref Md Santo Ali, Mohammod Abdul Motin, El-Sayed M. El-Alfy, Mufti Mahmud, A Hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory Approach for PPG-Based Stress Monitoring from Wrist Worn Wearables, Neural Information Processing, (474-489), (2025). https://doi.org/10.1007/978-981-95-4384-7_33 Crossref Loading... View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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