Enhancing Vital Sign Monitoring with Reinforcement Learning and Wavelet Analysis in Sleep Disorders

Sleep disorders have a significant impact on individuals’ health and overall quality of life. Among the most prevalent sleep disorders, obstructive sleep apnea and snoring necessitate effective monitoring and assessment methods. This paper introduces a novel approach to extract vital sign data, including heart rate, respiration rate, and body motion, from electronic signal graphs using wavelet analysis. In order to enhance the accuracy and efficiency of vital sign prediction, we employ reinforcement learning techniques to acquire an optimal policy for processing electronic signals. By identifying distinct features that characterize subjects with similar conditions, we enable personalized treatment approaches, ultimately leading to improvements in the overall health of individuals affected by sleep disorders.