Signal Processing-Driven AI for Healthcare

Signal Processing-Driven AI for Healthcare examines how AI techniques can be applied across four major biosignals-EEG, EMG, EOG, and ECG-to derive clinically meaningful insights. As biomedical data becomes increasingly multimodal, there is a rising need for integrated methodologies that unite these signals within robust, explainable AI pipelines suitable for healthcare environments. This book provides a unified framework that spans data acquisition, preprocessing, feature extraction, modeling, evaluation, and deployment, with an emphasis on reproducibility, practical Python-based implementations, and real-world translation to clinical workflows.

• Integrates AI-driven signal processing across EEG, EMG, EOG, and ECG
• Presents end-to-end workflows from data acquisition to deployment
• Demonstrates multimodal fusion and clinical decision support
• Emphasizes interpretability, validation, and regulatory considerations
• Features supplementary website to host the dataset
• Offers ready-to-use Python notebooks and scripts optimized for Google Collaboration