Sleep Scan: Sleep Disorder Detection in Real Time Using Smart Machine Learning Models

Abstract

Sleep disorders such as insomnia, sleep apnea, and restless leg syndrome significantly impact an individual’s physical health, cognitive performance, and overall quality of life. However, early diagnosis remains a major challenge due to the limited availability of sleep laboratories, high costs, and lack of continuous monitoring solutions. To address these limitations, this work proposes Sleep Scan Smart, a real-time machine learning-based system designed for early identification of sleep disorders in a non-invasive and cost-effective manner. The system continuously collects physiological signals including heart rate, oxygen saturation (SpO₂), respiration rate, body movement, and electroencephalogram (EEG) data using wearable or sensor-based devices. These signals undergo real-time preprocessing and feature extraction to remove noise and enhance data quality. Advanced machine learning and deep learning models such as Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) are employed to analyze patterns and detect abnormalities associated with various sleep disorders. Sleep Scan Smart performs real-time anomaly detection and generates instant alerts, enabling timely intervention. The system also provides comprehensive sleep health insights through a user-friendly interface, helping users and healthcare professionals make informed decisions. By achieving high accuracy with reduced false alarms, the proposed solution ensures reliable monitoring. Overall, the system enables continuous, home-based sleep assessment and contributes to the advancement of smart, AI-driven preventive healthcare by facilitating early diagnosis and improving long-term health outcomes.