Deep learning (DL) has emerged as a transformative paradigm in biomedical signal analysis, offering unprecedented capabilities for automatic feature extraction, efficient classification, and complex pattern recognition. The integration of DL techniques has significantly enhanced the interpretation of physiological signals such as Electrocardiograms (ECG) and Electroencephalograms (EEG), leading to more accurate and timely diagnosis of cardiovascular and neurological disorders. Unlike traditional machine learning methods that depend on handcrafted features, DL models autonomously learn hierarchical representations from raw signal data, improving generalization across diverse patient populations. This paper presents a comprehensive survey of state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Autoencoders, and Transformer-based models, applied to ECG and EEG signal classification. Additionally, it discusses preprocessing techniques, benchmark datasets, evaluation metrics, and hybrid model developments.
 

Deep Learning, ECG, EEG, CNN, LSTM, Transformer, Biomedical Signal Processing, Cardiovascular Disorders, Neurological Disorders.