Signal Processing-Driven AI for Healthcare

Dr. Ganesh R. Naik is a leading researcher in biomedical engineering and signal processing, ranked among the top 2% of scientists globally (Stanford University). He earned his PhD in Electronics Engineering from RMIT University, Melbourne (2009), and is currently a Senior Academic and Researcher in Computer Science and IT at Torrens University Australia. Dr. Naik has edited 16 books and published over 150 peer-reviewed papers. He serves as Associate Editor for IEEE Access, Frontiers in Neurorobotics, and two Springer journals. His career is distinguished by fellowships from Baden–Württemberg (Germany), ISSI (Australia), the BridgeTech Program, and the Royal Academy of Engineering (UK). Previously, he held research roles at Flinders University, Western Sydney University, and UTS, contributing to major projects in sleep technology, wearable sensors, and AI-driven biomedical signal processing. Dr. Ildar Rakhmatulin is a scientist and the creator of several popular open-source brain-computer interface (BCI) projects. He is the founder of PiEEG, a low-cost BCI solution. His experience includes working as a BCI developer at Imperial College London, a machine learning researcher at Heriot-Watt University, and a researcher in the neurotechnology group at the University of Edinburgh, UK. Additionally, he is the author of neuroscience courses on Udemy.

• Part I — Foundations1. Introduction: AI, biosignals, and healthcare workflows2. Sensors, acquisition, and data characteristicsPart II — Signal Processing for Biomedical Time Series3. Preprocessing & cleaning4. Time-frequency and feature transforms5. Spatial and multichannel processingPart III — Machine Learning & Deep Learning Methods6. Classical ML for biosignals7. Deep learning approaches8. Explainability, uncertainty, and interpretabilityPart IV — Modality-Focused Chapters (EEG, EMG, EOG, ECG)9. EEG: brain signals, pipelines, and applications10. ECG: cardiac signal analytics and arrhythmia detection11. EMG: muscle activation, prosthetics, and fatigue monitoring12. EOG: eye movement, drowsiness, and human factorsPart V — Multimodal Fusion, Deployment & Systems13. Multimodal learning and sensor fusion14. Real-time systems, edge AI, and hardware considerations15. Data engineering, annotation, and labelling strategies