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