Open Access

REVIEW

A Review of Deep Learning for Biomedical Signals: Current Applications, Advancements, Future Prospects, Interpretation, and Challenges

Ali Mohammad Alqudah¹, Zahra Moussavi^1,2,*

1 Biomedical Engineering Program, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
2 Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, Canada

* Corresponding Author: Zahra Moussavi. Email:

Computers, Materials & Continua 2025, 83(3), 3753-3841. https://doi.org/10.32604/cmc.2025.063643

Received 20 January 2025; Accepted 17 April 2025; Issue published 19 May 2025

Abstract

This review presents a comprehensive technical analysis of deep learning (DL) methodologies in biomedical signal processing, focusing on architectural innovations, experimental validation, and evaluation frameworks. We systematically evaluate key deep learning architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformer-based models, and hybrid systems across critical tasks such as arrhythmia classification, seizure detection, and anomaly segmentation. The study dissects preprocessing techniques (e.g., wavelet denoising, spectral normalization) and feature extraction strategies (time-frequency analysis, attention mechanisms), demonstrating their impact on model accuracy, noise robustness, and computational efficiency. Experimental results underscore the superiority of deep learning over traditional methods, particularly in automated feature extraction, real-time processing, cross-modal generalization, and achieving up to a 15% increase in classification accuracy and enhanced noise resilience across electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals. Performance is rigorously benchmarked using precision, recall, F1-scores, area under the receiver operating characteristic curve (AUC-ROC), and computational complexity metrics, providing a unified framework for comparing model efficacy. The survey addresses persistent challenges: synthetic data generation mitigates limited training samples, interpretability tools (e.g., Gradient-weighted Class Activation Mapping (Grad-CAM), Shapley values) resolve model opacity, and federated learning ensures privacy-compliant deployments. Distinguished from prior reviews, this work offers a structured taxonomy of deep learning architectures, integrates emerging paradigms like transformers and domain-specific attention mechanisms, and evaluates preprocessing pipelines for spectral-temporal trade-offs. It advances the field by bridging technical advancements with clinical needs, such as scalability in real-world settings (e.g., wearable devices) and regulatory alignment with the Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR). By synthesizing technical rigor, ethical considerations, and actionable guidelines for model selection, this survey establishes a holistic reference for developing robust, interpretable biomedical artificial intelligence (AI) systems, accelerating their translation into personalized and equitable healthcare solutions.

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Keywords

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