Exploration of Smart Medical Systems for Efficient Recognition of Potential Sleep Disruption Cases Relying on Health Metrics

This study explores a state-of-the-art methodology for the efficient recognition of potential sleep disruption cases relying on health metrics, comparing it with six traditional methods. The proposed method combines wearable technology, advanced data analysis, and machine learning to continuously monitor and analyze a wide range of health metrics during sleep. The traditional methods include Polysomnography (PSG), Actigraphy, Electroencephalography (EEG) analysis, Sleep Questionnaires, Sleep Diary, and Actigraph Monitoring. The comparison is made based on performance parameters, accessibility, and cost efficiency. Results indicate that the Proposed Method outperforms the traditional methods in all key performance parameters - sensitivity, specificity, accuracy, precision, recall, and F1 score. The Proposed Method achieves a high sensitivity of 0.96, ensuring it correctly identifies a vast majority of sleep disruption cases, while maintaining a specificity of 0.93 to minimize false alarms. The accuracy, precision, recall, and F1 score are consistently higher for the Proposed Method, affirming its superior accuracy and reliability. Furthermore, the Proposed Method exhibits notable advantages in terms of accessibility and cost efficiency. It is highly accessible, leveraging widely available wearable devices and mobile applications, enabling real-time monitoring and continuous tracking of health metrics. This accessibility, coupled with its cost-effectiveness, offers a user-friendly and economical solution for individuals and healthcare providers. In conclusion, the proposed methodology, with its emphasis on smart medical systems and advanced data analytics, presents a significant leap forward in the field of sleep disruption recognition. Its performance and accessibility surpass traditional methods, making it a valuable tool for early detection and personalized intervention in sleep-related disorders. The methodology is poised to enhance the overall management of sleep disruption cases, ultimately contributing to improved well-being.