Stress Detection from Photoplethysmography Signals Using Multi-Domain Heart Rate Variability Analysis

Clicks: 1
ID: 312633
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #388 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Stress is a key contributor to cardiovascular and mental health disorders, which motivates reliable, non intrusive monitoring methods for early detection and intervention. Photoplethysmography (PPG) has emerged as a practical alternative to electrocardiography (ECG) for wearable cardiovascular monitoring because of its low cost and ease of integration into consumer devices. This study presents a stress detection framework based on multi domain pulse rate variability (PRV) analysis derived from PPG signals. Publicly available datasets, including the Wearable Stress and Affect Detection (WESAD) dataset and a Pulse Transit Time (PTT) PPG dataset, are used to validate the proposed signal processing and feature extraction pipeline. Raw PPG signals are preprocessed with bandpass filtering and robust peak detection to obtain inter beat intervals, from which time domain, frequency domain, and nonlinear PRV features, as well as the Baevsky Stress Index, are computed. On WESAD, statistically significant differences between baseline and TSST induced stress conditions are observed in key PRV features (e.g., SDNN, RMSSD, pNN50, LF/HF ratio, SD1, SD2, ApEn, SampEn; Bonferroni corrected p < 0.0029), indicating reduced variability, a shift toward sympathetic dominance, and decreased dynamical complexity during stress. A subset of features also shows strong stability across window lengths from 2 to 5 minutes (R² ≥ 0.6 when compared to 5 minute reference segments), supporting their use in near real time monitoring. These results confirm the feasibility of PPG based, multi domain PRV analysis for non invasive stress assessment and highlight its suitability for wearable and edge based healthcare applications.
Reference Key
imported_1777055584_69ebb7606ed73 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mohammed Alnaeem
Journal Journal of Computing & Biomedical Informatics
Year 2026
DOI
10.56979/1002/2026/1234
URL
https://jcbi.org/index.php/Main/article/view/1234
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.