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Sleep is an essential biological process, and monitoring different stages of sleep (such as wakefulness, light sleep, deep sleep, and REM) plays a key role in diagnosing sleep disorders and improving health. Manual scoring of sleep stages from polysomnography (EEG, ECG, and movement signals) is time-consuming and requires medical expertise. This project, Bed Stage Prediction Using ML Classification in Python, focuses on building a machine learning–based system to automatically classify sleep stages using physiological and movement-related data. The system uses datasets containing features such as brain activity, heart rate variability, and body movements. By applying machine learning classifiers (Random Forest, SVM, Gradient Boosting, and deep learning models like LSTM), the system predicts sleep stages accurately. Implemented in Python with libraries such as Scikit-learn, TensorFlow/Keras, Pandas, NumPy, and Matplotlib, the project provides an efficient, scalable, and automated solution for healthcare and sleep research.
Currently, sleep stage classification is performed manually by sleep technicians or medical experts, who analyze EEG, ECG, and other biosignals to label different stages. This approach is highly accurate but also time-intensive, costly, and subjective, leading to variability between scorers. Some existing automated systems rely on traditional statistical methods or rule-based algorithms, which are limited in handling complex, nonlinear patterns in physiological signals. Moreover, many existing solutions are designed for research purposes only and are not scalable or real-time, reducing their applicability in large-scale or personal health monitoring systems.
The proposed system introduces a machine learning–based automated classifier for bed stage prediction. Data preprocessing steps include noise filtering, normalization, and feature extraction from physiological signals. The extracted features are then fed into ML models such as Random Forest, Support Vector Machine, or LSTM-based deep learning models for stage classification. The system is trained and evaluated on labeled datasets to achieve high accuracy. Predictions are visualized using graphs, confusion matrices, and performance metrics (accuracy, precision, recall, and F1-score). Compared to existing methods, this system offers automation, higher accuracy, scalability, and the potential for real-time monitoring. It can be integrated into wearable devices or hospital monitoring systems for improved patient care and early detection of sleep disorders.
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