Conference on Information and Control Systems 2025
Classification of Cognitive Task Performance using HRV Features: A Convolutional Neural Network Approach
- Venue and date
- Conference on Information and Control Systems 2025 · October 22–25, 2025
- Location
- Delpino Resort, Goseong, Gangwon-do, Republic of Korea
- Publication type
- Conference Proceedings Paper
- Pages
- 291–292
- Publication date
- 2025.10.22
- Presentation date
- 2025.10.23
- My role
- First author · Poster preparation · Presentation participation
Abstract
This study classifies cognitive task performance from PPG-derived HRV features and evaluates a convolutional neural network approach.
Research Problem
The work examines whether physiological HRV features can distinguish cognitive task performance states.
System Architecture
The pipeline proceeds from PPG acquisition to IBI extraction, HRV feature construction, CNN classification, and metric reporting.
Methodology
PPG acquisition, IBI/HRV preprocessing, and CNN/Transformer Encoder classification with five-fold cross-validation and an 80/20 holdout evaluation.
Results
Paper abstract/conclusion: AUC 0.99 and F1-score 0.992. Figure 6: 140 validation samples in Fold 5, approximately 99.3% accuracy.
- Paper AUC
- 0.99
- Paper F1-Score
- 0.992
Study scope
The abstract/conclusion metrics and body Fold 5 results are labeled separately. Independent code runs retain their own split, preprocessing, and aggregation conditions.
Paper and related project
BibTeX
@inproceedings{ryu2025cics25cnnhrv,
title={ Classification of Cognitive Task Performance using HRV Features: A Convolutional Neural Network Approach },
author={ Hyeong-Rok Ryu and Woo-seok Kang and Kyung-Ho Kim },
booktitle={ Conference on Information and Control Systems 2025 },
year={ 2025 },
pages={ 291–292 }
}CICS’25 proceedings, pp. 291–292. First author: Hyeong-Rok Ryu. Sources include the public PDF, DBpia record, and PPG acquisition/classification code.