Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model
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-inspir...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | ces |
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Prague University of Economics and Business
2023-04-01
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| Series: | Acta Informatica Pragensia |
| Subjects: | |
| Online Access: | https://aip.vse.cz/artkey/aip-202301-0009_blood-pressure-estimation-using-emotion-based-optimization-clustering-model.php |
| _version_ | 1797840664663162880 |
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| author | Vaishali Rajput Preeti Mulay Sharnil Pandya Chandrashekhar Mahajan Rupali Deshpande |
| author_facet | Vaishali Rajput Preeti Mulay Sharnil Pandya Chandrashekhar Mahajan Rupali Deshpande |
| author_sort | Vaishali Rajput |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T16:18:27Z |
| format | Article |
| id | doaj.art-a5622b83494f4549a9e8a53ed2d4e883 |
| institution | Directory Open Access Journal |
| issn | 1805-4951 |
| language | ces |
| last_indexed | 2024-04-09T16:18:27Z |
| publishDate | 2023-04-01 |
| publisher | Prague University of Economics and Business |
| record_format | Article |
| series | Acta Informatica Pragensia |
| spelling | doaj.art-a5622b83494f4549a9e8a53ed2d4e8832023-04-23T21:32:45ZcesPrague University of Economics and BusinessActa Informatica Pragensia1805-49512023-04-0112112314010.18267/j.aip.209aip-202301-0009Blood Pressure Estimation Using Emotion-Based Optimization Clustering ModelVaishali Rajput0Preeti Mulay1Sharnil Pandya2Chandrashekhar Mahajan3Rupali Deshpande4Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, IndiaVishwakarma Institute of Technology, Pune, IndiaVishwakarma Institute of Technology, Pune, IndiaThe 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.https://aip.vse.cz/artkey/aip-202301-0009_blood-pressure-estimation-using-emotion-based-optimization-clustering-model.phpaudio signalsemotion recognitionenhanced grey wolf spotted hyena optimizationclusteringsvmoptimization algorithm |
| spellingShingle | Vaishali Rajput Preeti Mulay Sharnil Pandya Chandrashekhar Mahajan Rupali Deshpande Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model Acta Informatica Pragensia audio signals emotion recognition enhanced grey wolf spotted hyena optimization clustering svm optimization algorithm |
| title | Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model |
| title_full | Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model |
| title_fullStr | Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model |
| title_full_unstemmed | Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model |
| title_short | Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model |
| title_sort | blood pressure estimation using emotion based optimization clustering model |
| topic | audio signals emotion recognition enhanced grey wolf spotted hyena optimization clustering svm optimization algorithm |
| url | https://aip.vse.cz/artkey/aip-202301-0009_blood-pressure-estimation-using-emotion-based-optimization-clustering-model.php |
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