Abstract
Stress is a condition that causes a specific physiologicsal response. Heart rate variability (HRV) is a critical aspect in identifying stress. It is crucial for those who want to keep track of their wellness. Currently, numerous research is being conducted on stress prediction from HRV. The existing works in this field cover different data sets to identify stress, where significantly few models can predict stress with high accuracy. This work combines two well-known stress prediction data sets comprising HRV features named WESAD and SWELL-KW to compare twelve classical machine learning models and hybrid models. Finally, it proposes a hybrid stress prediction model that combines Artificial Neural Network (ANN) with Naive Bayes (NB). The proposed model performed auspiciously, having an accuracy of 95.75% within only 0.80 s. A stress prediction framework is also suggested based on the findings.
Md. Rahat Shahriar Zawad, Chowdhury Saleh Ahmed Rony, Md. Yeaminul Haque : Equal Contributor.
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References
Ahmad Z et al (2021) Multi-level stress assessment from ECG in a virtual reality environment using multimodal fusion. arXiv 2107.04566
Ahuja R, Banga A (2019) Mental stress detection in university students using machine learning algorithms. Procedia Comput Sci 152:349–353
Al Banna MH et al (2021) Attention-based bi-directional long-short term memory network for earthquake prediction. IEEE Access 9:56589–56603
Al Nahian MJ, Ghosh T et al (2020) Towards artificial intelligence driven emotion aware fall monitoring framework suitable for elderly people with neurological disorder. In: Proceedings of Brain Informatics, pp 275–286
Al Nahian MJ et al (2021) Towards an accelerometer-based elderly fall detection system using cross-disciplinary time series features. IEEE Access 9:39413–31
Biswas M, Tania MH, Kaiser MS et al (2021) Accu3rate: a mobile health application rating scale based on user reviews. PloS One 16(12):e0258050
Biswas M et al (2021) An xai based autism detection: the context behind the detection. In: Proceedings of Brain Informatics, pp 448–459
Dalmeida KM, Masala GL (2021) HRV features as viable physiological markers for stress detection using wearable devices. Sensors 21(8):2873
Deepa B et al (2022) Pattern descriptors orientation and map firefly algorithm based brain pathology classification using hybridized machine learning algorithm. IEEE Access 10:3848–3863
Fabietti M, Mahmud M, Lotfi A (2021) Anomaly detection in invasively recorded neuronal signals using deep neural network: effect of sampling frequency. In: Proceedings of AII, pp 79–91
Fabietti M, Mahmud M, Lotfi A (2022) Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning. Brain Inform 9(1):1–17
Fabietti M et al (2020) Artifact detection in chronically recorded local field potentials using long-short term memory neural network. In: Proceedings of AICT 2020, pp 1–6
Faria TH et al (2021) Smart city technologies for next generation healthcare. In: Data-driven mining, learning & analytics for secured smart cities, pp 253–274
Ghosh T et al (2021) Artificial intelligence and internet of things in screening and management of autism spectrum disorder. Sustain Cities Soc 74:103189
Ghosh T et al (2021) An attention-based mood controlling framework for social media users. In: Proceedings of Brain Informatics, pp 245–256
Ghosh T et al (2021) A hybrid deep learning model to predict the impact of Covid-19 on mental health form social media big data. Preprints 2021 (2021060654)
Koldijk S, Neerincx MA, Kraaij W (2016) Detecting work stress in offices by combining unobtrusive sensors. IEEE Trans Affect Comput 9(2):227–239
Koldijk S, Sappelli M et al (2014) The swell knowledge work dataset for stress and user modeling research. In: Proceedings of ICMI, pp 291–298
Kumar I et al (2022) Dense tissue pattern characterization using deep neural network. Cogn Comput, pp 1–24 (ePub ahead of print)
Lalotra GS, Kumar V, Bhatt A, Chen T, Mahmud M (2022) iReTADS: an intelligent real-time anomaly detection system for cloud communications using temporal data summarization and neural network. Secur Commun Netw 2022:9149164
Mahmud M, Kaiser MS, McGinnity TM, Hussain A (2021) Deep learning in mining biological data. Cogn Comput 13(1):1–33
Mahmud M et al (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063–2079
Mammoottil MJ, Kulangara LJ, Cherian AS, Mohandas P, Hasikin K, Mahmud M (2022) Detection of breast cancer from five-view thermal images using convolutional neural networks. J Healthc Eng 2022:4295221
McDonald AD, Sasangohar F, Jatav A, Rao AH (2019) Continuous monitoring and detection of post-traumatic stress disorder triggers among veterans: a supervised machine learning approach. IISE Trans Healthc Syst Eng 9(3):201–211
Nawar A, Toma NT, Al Mamun S et al (2021) Cross-content recommendation between movie and book using machine learning. In: Proceedings of AICT, pp 1–6
Nkurikiyeyezu K, Yokokubo A, Lopez G (2019) The effect of person-specific biometrics in improving generic stress predictive models. arXiv 1910.01770
Paul A et al (2022) Inverted bell-curve-based ensemble of deep learning models for detection of Covid-19 from chest x-rays. Neural Compu Appl, pp 1–15
Prakash N et al (2021) Deep transfer learning Covid-19 detection and infection localization with superpixel based segmentation. Sustain Cities Soc 75:103252
Satu M et al (2020) Towards improved detection of cognitive performance using bidirectional multilayer long-short term memory neural network. In: Proceedings of brain informatics, pp 297–306
Satu MS et al (2021) TClustVID: a novel machine learning classification model to investigate topics and sentiment in Covid-19 tweets. Knowl-Based Syst 226:107126
Schmidt P, Reiss A, Duerichen R, Marberger C, Van Laerhoven K (2018) Introducing wesad, a multimodal dataset for wearable stress and affect detection. In: Proceedings of ICMI, pp 400–408
Siirtola P (2019) Continuous stress detection using the sensors of commercial smartwatch. In: Proceedings of ubiquitous computing, pp 1198–1201
Sriramprakash S, Prasanna VD, Murthy OR (2017) Stress detection in working people. Procedia Comput Sci 115:359–366
Walambe R, Nayak P, Bhardwaj A, Kotecha K (2021) Employing multimodal machine learning for stress detection. J Healthc Eng 2021
Watkins J, Fabietti M, Mahmud M (2020) Sense: a student performance quantifier using sentiment analysis. In: Proceedings of IJCNN, pp 1–6
Acknowledgements
MM and MSK are supported by the DIVERSASIA project (618615-EPP-1-2020-1-UKEPPKA2-CBHEJP) funded by the European Commission under the Erasmus+ programme.
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Zawad, M.R.S., Rony, C.S.A., Haque, M.Y., Banna, M.H.A., Mahmud, M., Kaiser, M.S. (2023). A Hybrid Approach for Stress Prediction from Heart Rate Variability. In: Mandal, J.K., De, D. (eds) Frontiers of ICT in Healthcare . Lecture Notes in Networks and Systems, vol 519. Springer, Singapore. https://doi.org/10.1007/978-981-19-5191-6_10
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DOI: https://doi.org/10.1007/978-981-19-5191-6_10
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