Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Chronic Stress Recognition Based on Time-Slot Analysis of Ambulatory Electrocardiogram and Tri-Axial Acceleration

Published: 23 October 2023 Publication History

Abstract

Stress, especially chronic stress, is a high risk factor of many physical and mental health problems. This work acquired 702 days of full-day ambulatory electrocardiogram (ECG) and Tri-axial acceleration (T-ACC) data from 104 healthy college students and realized chronic stress recognition through signal processing, statistical test and machine learning. We divided the 24 hours of a day into 153 time slots, and calculated 30 features from ECG and T-ACC data in each time slot. Statistical test of the above 30 features of the subjects with chronic stress and no chronic stress labels showed that chronic stress altered the autonomic nervous control of the heart not only in the daily activity time but also in the rest time at night, leading to smaller heart rate variability, faster heart rate and less complexity of the heartbeat rhythm. More specifically, the parasympathetic nervous activity at night was weakened by chronic stress. We expressed the ECG and T-ACC data of a day as a 30 × 153 data matrix, applied a four-layer fully connected neural network to classify the data of 702 days with chronic stress and no chronic stress labels, and obtained 88.17% chronic stress detection accuracy in the leave-one-subject-out cross test.

References

[1]
M. Lourens, S. M. Beram, B. B. Borah, A. P. Dube, A. Deka, and V. Tripathi, “A review of physiological signal processing via machine learning (ML) for personal stress detection,” in Proc. 2nd Int. Conf. Adv. Comput. Innov. Technol. Eng., Greater Noida, India, 2022, pp. 345–349.
[2]
R. A. Bryant, “Post-traumatic stress disorder: A state-of-the-art review of evidence and challenges,” World Psychiatry, vol. 18, no. 3, pp. 259–269, 2019.
[3]
G. Giannakakis, D. Grigoriadis, K. Giannakaki, O. Simantiraki, A. Roniotis, and M. Tsiknakis, “Review on psychological stress detection using biosignals,” IEEE Trans. Affect. Comput., vol. 13, no. 1, pp. 440–460, First Quarter 2022.
[4]
T. Chandola, E. Brunner, and M. Marmot, “Chronic stress at work and the metabolic syndrome: Prospective study,” BMJ-Brit. Med. J., vol. 332, no. 7540, pp. 521–524A, 2006.
[5]
A. E. Autry, “Neurobiology of chronic social defeat stress: Role of brain-derived neurotrophic Factor Signaling in the Nucleus Accumbens,” Biol. Psychiatry, vol. 80, no. 6, pp. E39–E40, 2016.
[6]
D. Riemann, “Sleep, insomnia and anxiety-bidirectional mechanisms and chances for intervention,” Sleep Med. Rev., vol. 614, 2022, Art. no.
[7]
C. Schubert, M. Lambertz, R. A. Nelesen, W. Bardwell, J. B. Choi, and J. E. Dimsdale, “Effects of stress on heart rate complexity-A comparison between short-term and chronic stress,” Biol. Psychol., vol. 80, no. 3, pp. 325–332, 2009.
[8]
P. Melillo, M. Bracale, and L. Pecchia, “Nonlinear heart rate variability features for real-life stress detection. Case study: Students under stress due to university examination,” Biomed. Eng. Online, vol. 10, 2011, Art. no.
[9]
J. F. Thayer, S. S. Yamamoto, and J. F. Brosschot, “The relationship of autonomic imbalance, heart rate variability and cardiovascular disease risk factors,” Int. J. Cardiol., vol. 141, no. 2, pp. 122–131, 2010.
[10]
M. Malik et al., “Heart rate variability guidelines: Standards of measurement, physiological interpretation and clinical use,” Eur. Heart J., vol. 17, no. 3, pp. 354–381, 1996.
[11]
R. Castaldo, W. Xu, P. Melillo, L. Pecchia, L. Santamaria, and C. James, “Detection of mental stress due to oral academic examination via ultra-short-term HRV analysis,” in Proc. IEEE 38th Annu. Int. Conf. Eng. Med. Biol. Soc., Orlando, FL, USA, 2016, pp. 3805–3808.
[12]
T. Li, Y. Chen, and W. Chen, “Daily stress monitoring using heart rate variability of bathtub ECG signals,” in Proc. IEEE 40th Annu. Int. Conf. Eng. Med. Biol. Soc., Honolulu, HI, USA, 2018, pp. 2699–2702.
[13]
M. Gardani et al., “A systematic review and meta-analysis of poor sleep, insomnia symptoms and stress in undergraduate students,” Sleep Med. Rev., vol. 61, 2022, Art. no.
[14]
D. Riemann, L. B. Krone, K. Wulff, and C. Nissen, “Sleep, insomnia, and depression,” Neuropsychopharmacology, vol. 45, no. 1, pp. 74–89, 2020.
[15]
Y. Yap, B. Bei, and J. F. Wiley, “Daily coping moderates the relations between stress and actigraphic sleep: A daily intensive longitudinal study with ecological momentary assessments,” Sleep Med., vol. 88, pp. 231–240, 2021.
[16]
C. A. Palmer, B. Oosterhoff, A. Massey, and H. Bawden, “Daily associations between adolescent sleep and socioemotional experiences during an ongoing stressor,” J. Adolesc. Health, vol. 70, no. 6, pp. 970–977, 2022.
[17]
J. C. Cousins et al., “The bidirectional association between daytime affect and nighttime sleep in youth with anxiety and depression,” J. Pediatr. Psychol., vol. 36, no. 9, pp. 969–979, 2011.
[18]
E. Lutin et al., “The cumulative effect of chronic stress and depressive symptoms affects heart rate in a working population,” Front. Psychiatry, vol. 13, 2022, Art. no.
[19]
J. A. Mortensen, M. E. Mollov, A. Chatterjee, D. Ghose, and F. Y. Li, “Multi-class stress detection through heart rate variability: A deep neural netwrk based study,” IEEE Access, vol. 11, pp. 57470–57480, 2023.
[20]
A. I. Siam, S. A. Gamel, and F. M. Talaat, “Automatic stress detection in car drivers based on non-invasive physiological signals using machine learning techniques,” Neural Comput. Appl., vol. 35, no. 17, pp. 12891–12904, 2023.
[21]
M. S. H. Onim, H. Thapliyal, and E. Rhodus, “A review of context-aware machine learning for stress detection,” IEEE Consum. Electron. Mag., early access, May 19, 2023.
[22]
L. Cao, H. J. Zhang, N. Y. Li, X. Wang, W. S. Ri, and L. Feng, “Category-aware chronic stress detection on microblogs,” IEEE J. Biomed. Health Inform., vol. 26, no. 2, pp. 852–864, Feb. 2022.
[23]
Y. C. Yang, A. Xie, S. Kim, J. Hair, M. Al-Garadi, and A. Sarker, “Automatic detection of twitter users who express chronic stress experiences via supervised machine learning and natural language processing,” CIN-Comput. Inform. Nurs., vol. 41, no. 9, pp. 717–724, 2023.
[24]
C. Nagpal and P. K. Upadhyay, “Adaptive neuro fuzzy inference system technique on polysomnographs for the detection of stressful conditions,” IETE J. Res., vol. 65, no. 3, pp. 298–309, 2019.
[25]
A. Hag, D. Handayani, T. Pillai, T. Mantoro, M. H. Kit, and F. Al-Shargie, “A wearable single EEG channel analysis for mental stress state detection,” in Proc. IEEE 7th Int. Conf. Comput. Eng. Des., Sukabumi, Indonesia, 2021, pp. 1–6.
[26]
D. Kim, Y. Seo, J. Cho, and C. Cho, “Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day,” in Proc. IEEE 30th Annu. Int. Conf. Eng. Med. Biol. Soc., Vancouver, BC, Canada, 2008, pp. 682–685.
[27]
R. Khosrowabadi, Q. Chai, K. A. Kai, S. W. Tung, and M. Heijnen, “A brain-computer interface for classifying EEG correlates of chronic mental stress,” in Proc. Int. Joint Conf. Neural Netw., San Jose, CA, USA, 2011, pp. 757–762.
[28]
M. Kuroha, Y. Ban, F. Rui, and S. Warisawa, “Chronic stress level estimation focused on motion pattern changes acquired from seat pressure distribution,” in Proc. Int. Conf. Cyberworlds, Kyoto, Japan, 2019, pp. 135–142.
[29]
Y. S. Can, D. Gokay, D. R. Kiliç, D. Ekiz, N. Chalabianloo, and C. Ersoy, “How laboratory experiments can be exploited for monitoring stress in the wild: A bridge between laboratory and daily life,” Sensors, vol. 20, no. 3, 2020, Art. no.
[30]
P. Sarkar et al., “Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning,” Sci. Rep., vol. 11, no. 1, 2021, Art. no.
[31]
Y. Zhang and D. Yang, “Life event scale,” Chin. J. Ment. Health, vol. 13, pp. 101–103, 1999.
[32]
S. Cohen, T. Kamarck, and R. Mermelstein, “A global measure of perceived stress,” J. Health Social Behav., vol. 24, no. 4, pp. 385–396, 1983.
[33]
W. Wen et al., “Toward constructing a real-time social anxiety evaluation system: Exploring effective heart rate features,” IEEE Transaction Affect. Comput., vol. 11, no. 1, pp. 100–110, First Quarter 2020.
[34]
H. Liu, W. Wen, J. Zhang, G. Liu, and Z. Yang, “Autonomic nervous pattern of motion interference in real-time anxiety detection,” IEEE Access, vol. 6, pp. 69763–69768, 2018.
[35]
C. R. Rebello, P. B. Kallingappa, and P. G. Hegde, “Assessment of perceived stress and association with sleep quality and attributed stressors among 1st-year medical students: A cross-sectional study from Karwar, Karnataka, India,” Tzu Chi Med. J., vol. 30, no. 4, pp. 221–226, 2018.
[36]
H. Otzenberger, C. Simon, C. Gronfier, and G. Brandenberger, “Temporal relationship between dynamic heart rate variability and electroencephalographic activity during sleep in man,” Neurosci. Lett., vol. 229, no. 3, pp. 173–176, 1997.
[37]
R. Xiong, F. Kong, X. Yang, G. Liu, and W. Wen, “Pattern recognition of cognitive load using EEG and ECG signals,” Sensors, vol. 20, no. 18, 2020, Art. no.
[38]
H. G. Kim, E. J. Cheon, D. S. Bai, Y. H. Lee, and B. H. Koo, “Stress and heart rate variability: A meta-analysis and review of the literature,” Psychiatry Investigation, vol. 15, no. 3, pp. 235–245, 2018.
[39]
Y. R. Xia, L. C. Yang, L. Zunino, H. Y. Shi, Y. Zhuang, and C. Y. Liu, “Application of permutation entropy and permutation min-entropy in multiple emotional states analysis of RRI time series,” Entropy, vol. 20, no. 3, 2018, Art. no.
[40]
S. Byun et al., “Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol,” Comput. Biol. Med., vol. 112, 2019, Art. no.
[41]
Y. Chen and H. Yang, “Multiscale recurrence analysis of long-term nonlinear and nonstationary time series,” Chaos Solitons Fractals, vol. 45, no. 7, pp. 978–987, 2012.
[42]
S. K. Berkaya, A. K. Uysal, E. S. Gunal, S. Ergin, S. Gunal, and M. B. Gulmezoglu, “A survey on ECG analysis,” Biomed. Signal Process. Control, vol. 43, pp. 216–235, 2018.
[43]
M. N. Jarczok et al., “Heart rate variability in the prediction of mortality: A systematic review and meta-analysis of healthy and patient populations,” Neurosci. Biobehavioral Rev., vol. 143, 2022, Art. no.
[44]
A. L. Goldberger et al., “PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,” Circulation, vol. 101, no. 23, pp. E215–E220, 2000.
[45]
X. Fan, C. Zhao, X. Zhang, H. Luo, and W. Zhang, “Assessment of mental workload based on multi-physiological signals,” Techonol. Health Care, vol. 28, pp. S67–S80, 2020.
[46]
Z. Hua, C. Chen, R. Zhang, G. Liu, and W. Wen, “Diagnosing various severity levels of congestive heart failure based on long-term HRV signal,” Appl. Sci., vol. 9, no. 12, 2019, Art. no.
[47]
J. Zhu, L. Ji, and C. Liu, “Heart rate variability monitoring for emotion and disorders of emotion,” Physiol. Meas., vol. 40, 2019, Art. no.
[48]
D. Razanskaite-Virbickiene, E. Danyte, G. Mockeviciene, R. Dobrovolskiene, R. Verkauskiene, and R. Zalinkevicius, “Can coefficient of variation of time-domain analysis be valuable for detecting cardiovascular autonomic neuropathy in young patients with type 1 diabetes: A case control study,” BMC Cardiovasc. Disord., vol. 17, 2017, Art. no.
[49]
J. Xie, W. Wen, G. Liu, and Y. Li, “Intelligent biological alarm clock for monitoring autonomic nervous recovery during nap,” Int. J. Comput. Intell. Syst., vol. 12, no. 2, pp. 453–459, 2019.
[50]
S. Gedam and S. Paul, “A review on mental stress detection using wearable sensors and machine learning techniques,” IEEE Access, vol. 9, pp. 84045–84066, 2021.
[51]
A. Almadhor et al., “Wrist-based electrodermal activity monitoring for stress detection using federated learning,” Sensors, vol. 23, no. 8, 2023, Art. no.
[52]
S. Akter et al., “Comprehensive performance assessment of deep learning models in early prediction and risk identification of chronic kidney disease,” IEEE Access, vol. 9, pp. 165184–165206, 2021.
[53]
A. Onan, “Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach,” Comput. Appl. Eng. Educ., vol. 29, no. 3, pp. 572–589, 2021.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE Transactions on Affective Computing
IEEE Transactions on Affective Computing  Volume 15, Issue 3
July-Sept. 2024
1087 pages

Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 23 October 2023

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 21 Dec 2024

Other Metrics

Citations

View Options

View options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media