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Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net
Li, Fenghua1,2; Xu, Peida3; Zheng, Shichun1,2; Chen, Wenfeng4; Yan, Yang1,2; Lu, Suo1,2; Liu, Zhengkui1
2018-09-27
Source PublicationINTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
ISSN1550-1477
Volume14Issue:9Pages:14
Abstract

Detecting psychological stress in daily life is useful to stress management. However, existing stress-detection models with only heartbeat/pulse input are limited in prediction output granularity, and models with multiple prediction levels output usually require additional bio-signal other than heartbeat, which may increase the number of sensors and be wearable unfriendly. In this study, we took a novel approach of incremental pulse rate variability and elastic-net regression in predicting mental stress. Mental arithmetic task paradigm was used during the experiments. A total of 178 participants involved in the model building, and the model was verified with a group of 29 participants in the laboratory and 40 participants in a 14-day follow-up field test. The result showed significant median correlations between self-report and model-prediction stress levels (cross-validation: r=0.72 (p<0.0001), laboratory verification: r=0.70 (p<0.0001), field test r=0.56 (p<0.0001)) with fine granularity ratings of 0-7 float numbers. The correct prediction took 86%-91% of the testing samples with error standard deviation of 0.68-0.81 in the label space of 14. By simplifying the process of prediction with a perspective of stress difference and handling the collinearity among pulse rate variability features with elastic net, we successfully built a stress prediction model with only pulse rate variability input source, fine granularity output and portable friendly sensor.

KeywordHeart Rate Variability Stress Detection Regression Field Test Photoplethysmography
DOI10.1177/1550147718803298
Language英语
Funding OrganizationEvaluation and Intervention Technology Research for Post-traumatic Stress Patients Population project ; Shenzhen Science and Technology Innovation Commission
Funding ProjectEvaluation and Intervention Technology Research for Post-traumatic Stress Patients Population project[JCYJ20170413170301569] ; Shenzhen Science and Technology Innovation Commission
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Telecommunications
WOS IDWOS:000446031400001
PublisherSAGE PUBLICATIONS INC
WOS KeywordHeart-rate-variability ; Term Hrv Analysis ; Psychosocial Stress ; Physiological Signals ; Perceived Stress ; Rating-scale ; Classifiers ; Responses ; System
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/27124
Collection中国科学院心理健康重点实验室
Corresponding AuthorLiu, Zhengkui
Affiliation1.Chinese Acad Sci, Inst Psychol, Key Lab Mental Hlth, 218 South Block,16 Lincui Rd, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Huawei Device Dongguan Co Ltd, Shenzhen, Peoples R China
4.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Li, Fenghua,Xu, Peida,Zheng, Shichun,et al. Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net[J]. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,2018,14(9):14.
APA Li, Fenghua.,Xu, Peida.,Zheng, Shichun.,Chen, Wenfeng.,Yan, Yang.,...&Liu, Zhengkui.(2018).Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net.INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS,14(9),14.
MLA Li, Fenghua,et al."Photoplethysmography based psychological stress detection with pulse rate variability feature differences and elastic net".INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 14.9(2018):14.
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