Speech-Based Blood Pressure Estimation with Enhanced Optimization and
  Incremental Clustering
Vaishali Rajput Preeti Mulay Rajeev Raje
Blood Pressure (BP) estimation plays a pivotal role in diagnosing various
health conditions, highlighting the need for innovative approaches to overcome
conventional measurement challenges. Leveraging machine learning and speech
signals, this study investigates accurate BP estimation with a focus on
preprocessing, feature extraction, and real-time applications. An advanced
clustering-based strategy, incorporating the k-means algorithm and the proposed
Fact-Finding Instructor optimization algorithm, is introduced to enhance
accuracy. The combined outcome of these clustering techniques enables robust BP
estimation. Moreover, extending beyond these insights, this study delves into
the dynamic realm of contemporary digital content consumption. Platforms like
YouTube have emerged as influential spaces, presenting an array of videos that
evoke diverse emotions. From heartwarming and amusing content to intense
narratives, YouTube captures a spectrum of human experiences, influencing
information access and emotional engagement. Within this context, this research
investigates the interplay between YouTube videos and physiological responses,
particularly Blood Pressure (BP) levels. By integrating advanced BP estimation
techniques with the emotional dimensions of YouTube videos, this study enriches
our understanding of how modern media environments intersect with health
implications.