Biomedical Signal Processing: Advanced Techniques, Applications, and Emerging Trends

Biomedical Signal Processing (BSP) plays a critical role in modern healthcare by extracting clinically relevant information from physiological signals such as ECG, EEG, EMG, and PPG. This paper presents an in-depth review of the mathematical foundations, advanced processing techniques, and machine learning applications in BSP. It explores methods such as adaptive filtering, wavelet transforms, empirical mode decomposition, and independent component analysis, highlighting their clinical relevance. Furthermore, the paper discusses the integration of deep learning models, including CNNs and LSTMs, for tasks like arrhythmia detection and seizure prediction. Future directions involving the Internet of Medical Things (IoMT), edge computing, and explainable AI are also explored. This review aims to bridge the gap between theoretical advancements and practical applications in biomedical signal analysis, offering insight into emerging trends and ongoing challenges in the field.