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학술지 Multilevel Mental Stress Detection using Ultra-short Pulse Rate Variability Series
Cited 0 time in scopus
저자
주바이르, 윤장우
발행일
202003
출처
Biomedical Signal Processing and Control, v.57, pp.1-11
ISSN
1746-8094
출판사
Elsevier
DOI
https://dx.doi.org/10.1016/j.bspc.2019.101736
협약과제
18AH2800, 생체 신호를 이용한 감정인식을 위한 전달학습 딥러닝 연구, 윤장우
초록
© 2019 Elsevier Ltd Prolonged exposure to mental stress reduces human work efficiency in daily life and may increase the risk of diabetes and cardiovascular diseases. However, identification of the true degree of stress in its initial stage can reduce the risk of life threatening diseases. In this paper, we proposed a multilevel stress detection system using ultra-short term recordings of a low cost wearable sensor. We designed an experimental paradigm based on Mental Arithmetic Tasks (MAT) to properly stimulate different levels of stress. During the experiment, Photoplethysmogram (PPG) signals were recorded along with subjective feedback for validation of stress induction. The beat-to-beat interval series, estimated from sixty seconds long segments of PPG signals, were used to extract different features based on their reliability. In order to capture the temporal information in the ultra-short term segments of PPG, we introduced a new set of features which have the potential to quantify the temporal information at point-to-point level in the Poincare plot. We also used a Sequential Forward Floating Selection (SFFS) algorithm to mitigate the issues of irrelevancy and redundancy among features. We investigated two classifiers based on quadratic discriminant analysis (QDA) and Support Vector Machine (SVM). The results of the proposed method produced 94.33% accuracy with SVM for five-level identification of mental stress. Moreover, we validated the generalizability of the system by evaluating its performance on a dataset recorded with a different stressor (Stroop). In conclusion, we found that the proposed multilevel stress detection system in conjunction with new parameters of the Poincare plot has the potential to detect five different mental stress states using ultra-short term recordings of a low-cost PPG sensor.
키워드
Mental stress detection, Poincare plot, PPG signals, Pulse rate variability, Quadratic discriminant analysis, Support Vector Machine, Wearable sensors
KSP 제안 키워드
Cardiovascular diseases(CVD), Experimental paradigm, Initial stage, Intrusion detection system(IDS), Low-cost, Mental Stress, Mental arithmetic tasks(MAT), PPG Sensor, PPG signal, Point-to-Point, Pulse rate variability