Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students

doi:10.5498/wjp.v16.i6.116013

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World Journal of Psychiatry

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2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Jun 19, 2026; 16(6): 116013
Published online Jun 19, 2026. doi: 10.5498/wjp.v16.i6.116013

Table 1 Demographic variables and heart rate variability parameters of control and stress groups among Chinese university students, mean ± SD

Variables	Control group (n = 142)	Stress group (n = 65)	t/χ²	P value
Demographic variables
Gender (female/male)	111/31	49/16	0.197	0.721
Age	20.73 ± 1.69	20.34 ± 1.78	1.532	0.127
BMI (kg/m²)	21.64 ± 3.09	21.97 ± 3.12	-0.716	0.475
Education level (year)	14.71 ± 1.41	14.50 ± 1.55	0.937	0.350
Only child (yes/no)	27/115	15/50	0.455	0.577
Single-parent family (yes/no)	12/130	8/57	0.76	0.448
Psychological scales
PSS	16.03 ± 5.83	32.15 ± 5.11	-20.146	< 0.001^b
PHQ-9	3.50 ± 2.86	8.22 ± 4.35	-7.979	< 0.001^b
GAD-7	1.77 ± 2.05	6.55 ± 4.14	-8.826	< 0.001^b
ISI	3.94 ± 3.40	7.49 ± 4.72	-5.456	< 0.001^b
HRV parameters
TDI	45.5 ± 10.89	50.41 ± 14.93	-2.377	0.019^a
SDNN (ms)	51.09 ± 12.32	56.34 ± 17.3	-2.205	0.03^a
SDNN5 (ms)	39.87 ± 10.95	44.94 ± 16.18	-2.3	0.024^a
FDI	1653.94 ± 414.45	1808.47 ± 536.16	-2.059	0.042^a
TSP (ms²)	2657.62 ± 1279.47	3358.65 ± 2132.21	-2.456	0.016^a
TP (ms²)	2657.64 ± 1279.45	3358.89 ± 2133.07	-2.456	0.016^a
VLF (ms²)	809.2 ± 488.06	1100.02 ± 859.48	-2.547	0.013^a
DPTI/SPTI	0.4 ± 0.13	0.46 ± 0.17	-2.381	0.019^a
C1 (mL/mmHg)	12.2 ± 1.41	11.71 ± 1.66	2.197	0.029^a
AI	-0.22 ± 0.14	-0.17 ± 0.17	-2.18	0.03^a
EEI	0.61 ± 0.1	0.57 ± 0.12	2.212	0.028^a

^aP < 0.05.

^bP < 0.01.

BMI: Body mass index; PSS: Perceived Stress Scale; PHQ-9: Patient Health Questionnaire-9; GAD-7: Generalized Anxiety Disorder-7; ISI: Insomnia Severity Index; HRV: Heart rate variability; TDI: Time Domain Index; SDNN: Standard Deviation of NN Intervals; SDNN5: 5-Minute Mean of Standard Deviation of NN Intervals; FDI: Frequency Domain Index; TSP: Total Power Spectrum; TP: Total Power; VLF: Very Low Frequency; DPTI/SPTI: Diastolic/Systolic Pressure-Time Index of the Heart; C1: Compliance of arterial vascular volume; AI: Augmentation Index; EEI: Ejection Elasticity Index.

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Table 2 Performance of six machine learning classification models using heart rate variability parameters

Classifier	AUC (95%CI)	AUC	Accuracy	Precision	Recall	F1 score	Specificity
RF	0.733 (0.655-0.811)	0.733	0.689	0.705	0.665	0.675	0.712
XGBoost	0.722 (0.635-0.809)	0.722	0.670	0.676	0.678	0.667	0.662
KNN	0.711 (0.639-0.783)	0.711	0.657	0.655	0.683	0.663	0.630
LightGBM	0.700 (0.623-0.777)	0.700	0.652	0.662	0.656	0.649	0.649
SVC	0.693 (0.607-0.779)	0.693	0.659	0.658	0.677	0.660	0.640
NB	0.648 (0.566-0.730)	0.648	0.583	0.630	0.411	0.486	0.755

HRV: Heart rate variability; AUC: Area under the curve; 95%CI: 95% confidence interval; RF: Random Forest; XGBoost: Extreme Gradient Boosting; KNN: K-Nearest Neighbors algorithm; LightGBM: Light Gradient Boosting Machine; SVC: Support Vector Machine; NB: Naive Bayes.

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Citation: Wei YG, Yang LH, Qin SS, Chen YL, Yan JN, Liu RX, Ma YM, Wang C, Song ZJ, Wang F, Ji GJ. Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students. World J Psychiatry 2026; 16(6): 116013
URL: https://www.wjgnet.com/2220-3206/full/v16/i6/116013.htm
DOI: https://dx.doi.org/10.5498/wjp.v16.i6.116013

Wei YG, Yang LH, Qin SS, Chen YL, Yan JN, Liu RX, Ma YM, Wang C, Song ZJ, Wang F, Ji GJ. Mental stress recognition using interpretable machine learning models with heart rate variability among Chinese university students. World J Psychiatry 2026; 16(6): 116013 [DOI: 10.5498/wjp.v16.i6.116013]

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