Deep Learning Architectures for Biomedical Signal Intelligence and Early Disease Prediction

Abstract

The integration of deep learning (DL) into biomedical signal processing has catalyzed a paradigm shift in healthcare, enabling the development of intelligent systems for early disease prediction and diagnosis. This chapter provides a comprehensive exploration of advanced DL architectures tailored for biomedical signal intelligence. We introduce a novel hybrid model, the CNN-LSTM-SE, which synergistically combines Convolutional Neural Networks (CNNs) for spatial feature extraction, Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, and a Squeeze-andExcitation (SE) module for adaptive channel-wise feature recalibration. Using the MITBIH Arrhythmia Database as a case study, we demonstrate the model’s exceptional performance in classifying cardiac arrhythmias, achieving an accuracy of 98.5%. The chapter details the complete workflow, from signal preprocessing and data augmentation to model architecture, training, and evaluation. A significant portion is dedicated to the in-depth analysis of the results, including performance metrics, confusion matrices, and comparative assessments against other DL models. We conclude by discussing the implications of these findings for the future of predictive medicine and outlining potential avenues for further research. This work serves as a practical guide for researchers and practitioners seeking to leverage the power of deep learning for building robust and accurate biomedical prediction systems.