Blood Pressure Estimation Using Emotion-Based Optimization Clustering Model

Abstract

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