Acta Informatica Pragensia 2023, 12(1), 123-140 | DOI: 10.18267/j.aip.2093292

Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model

Vaishali Rajput ORCID...1,2, Preeti Mulay ORCID...1, Sharnil Pandya ORCID...1,3, Chandrashekhar Mahajan ORCID...2, Rupali Deshpande ORCID...2
1 Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
2 Vishwakarma Institute of Technology, Pune, India
3 Computer Science and Media Technology Department, Faculty of Technology, Linnaeus University, Sweden

The features of human speech signals and emotional states are used to estimate the blood pressure (BP) using a clustering-based model. The audio-emotion-dependent discriminative features are identified to distinguish individuals based on their speech to form emotional groups. We propose a bio-inspired Enhanced grey wolf spotted hyena optimization (EWHO) technique for emotion clustering, which adds significance to this research. The model derives the most informative and judicial features from the audio signal, along with the person’s emotional states to estimate the BP using the multi-class support vector machine (SVM) classifier. The EWHO-based clustering method gives better accuracy (95.59%), precision (97.08%), recall (95.16%) and F1 measure (96.20%), as compared to other methods used for BP estimation. Additionally, the proposed EWHO algorithm gives superior results in terms of parameters such as the silhouette score, Davies-Bouldin score, homogeneity score, completeness score, Dunn index, and Jaccard similarity score.

Keywords: Audio signals; Emotion recognition; Enhanced grey wolf spotted hyena optimization; Clustering; SVM; Optimization algorithm.

Received: December 19, 2022; Revised: February 22, 2023; Accepted: February 22, 2023; Prepublished online: March 1, 2023; Published: April 19, 2023  Show citation

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Rajput, V., Mulay, P., Pandya, S., Mahajan, C., & Deshpande, R. (2023). Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model. Acta Informatica Pragensia, 12(1), 123-140. doi: 10.18267/j.aip.209
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    References

    1. Alkan, A., & Günay, M. (2012). Identification of EMG signals using discriminant analysis and SVM classifier. Expert Systems with Applications, 39(1), 44-47. https://doi.org/10.1016/j.eswa.2011.06.043 Go to original source...
    2. Argha, A., Celler, B. G., & Lovell, N. H. (2021). A Novel Automated Blood Pressure Estimation Algorithm Using Sequences of Korotkoff Sounds. IEEE Journal of Biomedical and Health Informatics, 25(4), 1257-1264. https://doi.org/10.1109/JBHI.2020.3012567 Go to original source...
    3. Badshah, A. M., Rahim, N., Ullah, N., Ahmad, J., Muhammad, K., Lee, M. Y., Kwon, S., & Baik, S. W. (2019). Deep features-based speech emotion recognition for smart affective services. Multimedia Tools and Applications, 78(5), 5571-5589. https://doi.org/10.1007/s11042-017-5292-7 Go to original source...
    4. Dhiman, G., Garg, M., Nagar, A., Kumar, V., & Dehghani, M. (2021). A novel algorithm for global optimization: Rat Swarm Optimizer. Journal of Ambient Intelligence and Humanized Computing, 12(8), 8457-8482. https://doi.org/10.1007/s12652-020-02580-0 Go to original source...
    5. Dhiman, G., & Kaur, A. (2019). STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence, 82, 148-174. https://doi.org/10.1016/j.engappai.2019.03.021 Go to original source...
    6. Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48-70. https://doi.org/10.1016/j.advengsoft.2017.05.014 Go to original source...
    7. Dhiman, G., & Kumar, V. (2019a). Spotted Hyena Optimizer for Solving Complex and Non-linear Constrained Engineering Problems. In Harmony Search and Nature Inspired Optimization Algorithms, (pp. 857-867). Springer. https://doi.org/10.1007/978-981-13-0761-4_81 Go to original source...
    8. Dhiman, G., & Kumar, V. (2019b). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169-196. https://doi.org/10.1016/j.knosys.2018.11.024 Go to original source...
    9. Farki, A., Kazemzadeh, R. B., & Noughabi, E. A. (2021). A Novel Clustering-Based Algorithm for Continuous and Non-invasive Cuff-Less Blood Pressure Estimation. arXiv [physics.med-ph]. http://arxiv.org/abs/2110.06996
    10. Grewe, L., & Hu, C. (2019). ULearn: understanding and reacting to student frustration using deep learning, mobile vision and NLP. In Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII. SPIE. https://doi.org/10.1117/12.2518262 Go to original source...
    11. Guizzo, E., Weyde, T., & Leveson, J. B. (2020). Multi-Time-Scale Convolution for Emotion Recognition from Speech Audio Signals. In 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (pp. 6489-6493). IEEE. https://doi.org/10.1109/ICASSP40776.2020.9053727 Go to original source...
    12. Harfiya, L. N., Chang, C. S., & Li, Y. H. (2021). Continuous Blood Pressure Estimation Using Exclusively Photopletysmography by LSTM-Based Signal-to-Signal Translation. Sensors, 21(9), Article no. 2952. https://doi.org/10.3390/s21092952 Go to original source...
    13. Issa, D., Fatih Demirci, M., & Yazici, A. (2020). Speech emotion recognition with deep convolutional neural networks. Biomedical Signal Processing and Control, 59, Article no. 101894. https://doi.org/10.1016/j.bspc.2020.101894 Go to original source...
    14. Kumar, R., & Dhiman, G. (2021). A Comparative Study of Fuzzy Optimization through Fuzzy Number. International Journal of Modern Research, 1(1), 1-14. https://www.ijmore.co.in/index.php/ijmore/article/view/1
    15. Lee, D., Kwon, H. B., Son, D., Eom, H., Park, C., Lim, Y., Seo, C., & Park, K. S. (2020). Beat-to-Beat Continuous Blood Pressure Estimation Using Bidirectional Long Short-Term Memory Network. Sensors, 21(1), 96. https://doi.org/10.3390/s21010096 Go to original source...
    16. Lee, S., Rajan, S., Jeon, G., Chang, J.-H., Dajani, H. R., & Groza, V. Z. (2017). Oscillometric blood pressure estimation by combining nonparametric bootstrap with Gaussian mixture model. Computers in Biology and Medicine, 85, 112-124. https://doi.org/10.1016/j.compbiomed.2015.11.008 Go to original source...
    17. Li, X., Li, S., Fang, Z., & Zhou, Q. (2019). Noninvasive Continuous Blood Pressure Estimation Algorithm Based on Features of Pulse Waves. In 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE. https://doi.org/10.1109/CISP-BMEI48845.2019.8965820 Go to original source...
    18. Martínez, G., Howard, N., Abbott, D., Lim, K., Ward, R., & Elgendi, M. (2018). Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure? Journal of Clinical Medicine Research, 7(10). https://doi.org/10.3390/jcm7100316 Go to original source...
    19. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007 Go to original source...
    20. Moghadam, M., & Moradi, M. H. (2014). Model based Blood Pressure estimation during exercise test using modified fuzzy function. In 2014 21th Iranian Conference on Biomedical Engineering (ICBME), (pp. 69-73). IEEE. https://doi.org/10.1109/ICBME.2014.7043896 Go to original source...
    21. Mottaghi, S., Moradi, M. H., Moghavvemi, M., Roohisefat, L., & Sagar, E. C. V. (2014). Neuro-fuzzy Indirect Blood Pressure Estimation during Bruce Stress Test. In International Conference on Bio-Inspired Systems and Signal Processing (pp. 257-263). ACM. https://doi.org/10.5220/0004862402570263 Go to original source...
    22. Mustaqeem., & Kwon, S. (2019). A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition. Sensors, 20(1), Article no. 183. https://doi.org/10.3390/s20010183 Go to original source...
    23. Mustaqeem., & Kwon, S. (2020). CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network. Mathematics, 8(12), Article no. 2133. https://doi.org/10.3390/math8122133 Go to original source...
    24. Mustaqeem., & Kwon, S. (2021). MLT-DNet: Speech emotion recognition using 1D dilated CNN based on multi-learning trick approach. Expert Systems with Applications, 167, Article no. 114177. https://doi.org/10.1016/j.eswa.2020.114177 Go to original source...
    25. Mustaqeem., Sajjad, M., & Kwon, S. (2020). Clustering-Based Speech Emotion Recognition by Incorporating Learned Features and Deep BiLSTM. IEEE Access, 8, 79861-79875. https://doi.org/10.1109/access.2020.2990405 Go to original source...
    26. Nie, W., Ren, M., Nie, J., & Zhao, S. (2021). C-GCN: Correlation Based Graph Convolutional Network for Audio-Video Emotion Recognition. IEEE Transactions on Multimedia, 23, 3793-3804. https://doi.org/10.1109/TMM.2020.3032037 Go to original source...
    27. Song, K., Park, T. G., & Chang, J. (2021). Novel Data Augmentation Employing Multivariate Gaussian Distribution for Neural Network-Based Blood Pressure Estimation. Applied Sciences, 11(9), Article no. 3923. https://doi.org/10.3390/app11093923 Go to original source...
    28. Tian, Y., Kanade, T., & Cohn, J. N. (2001). Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 97-115. https://doi.org/10.1109/34.908962 Go to original source...
    29. Wang, J., Xue, M., Culhane, R., Diao, E., Ding, J., & Tarokh, V. (2020). Speech Emotion Recognition with Dual-Sequence LSTM Architecture. In 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (pp. 6474-6478). IEEE. https://doi.org/10.1109/ICASSP40776.2020.9054629 Go to original source...
    30. Yang, S., Zhang, Y., Cho, S., Correia, R. A., & Morgan, S. P. (2021). Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model. Optical and Quantum Electronics, 53(2), Article number 93. https://doi.org/10.1007/s11082-020-02667-0 Go to original source...

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