Reinforcement Learning Improves SVM-Driven Algorithms for Classifying Multi-Sensor Data for Medical Monitoring

Abstract

Multi-sensor data in medical monitoring includes waveform changes in physiological signals and time-series characteristics of disease progression. These features typically exhibit high-dimensionality, large-scale, and time-varying characteristics. Nonlinear relationships exist between these features, increasing the difficulty of data processing and feature extraction, thereby reducing the classification capabilities of related algorithms. This study proposes a multi-sensor data classification processing method in medical monitoring based on reinforcement learning improved SVM. The algorithm employs the DBSCAN algorithm combined with Euclidean distance for clustering and data collection of multi-sensor data. Discrete wavelet transform is used to remove interference noise from the data, followed by convolutional neural networks for signal feature extraction from the denoised data. The Q-learning algorithm in reinforcement learning is used to improve the traditional SVM, with the extracted signal features input into the improved SVM. The classification results of medical monitoring multi-sensor data are output via a regression function. The experimental results show that the denoising results of medical monitoring data of the method are high, the signal-to-noise ratio is high, and the Kappa coefficient reaches up to 0.98. Therefore, it shows that the method can accurately classify medical monitoring multi-sensor data.