Hybrid Learning Models for Biomedical Signal Interpretation and Health Monitoring

Abstract

Biomedical signal interpretation is a critical component of modern healthcare, enabling the diagnosis and monitoring of various physiological conditions. This chapter explores the application of hybrid learning models for the automated interpretation of biomedical signals, focusing on their potential to enhance diagnostic accuracy and efficiency. We present a novel framework that integrates traditional machine learning classifiers with deep learning architectures to leverage the strengths of both paradigms. The proposed model combines a Random Forest, a Support Vector Machine (SVM), and a Multi-Layer Perceptron (MLP) neural network in an ensemble structure. This hybrid approach is designed to effectively process and classify complex biomedical signals, such as electrocardiograms (ECGs), for health monitoring applications. A synthetic dataset of ECG signals, simulating both normal and arrhythmic patterns, is used to evaluate the model’s performance. The experimental results demonstrate that the hybrid model achieves a high classification accuracy of 92.5%, with a sensitivity of 95% and a specificity of 96.67%. These findings underscore the potential of hybrid learning models as a robust and reliable tool for biomedical signal interpretation, paving the way for more intelligent and proactive health monitoring systems.