UNIT – I: Fundamentals of AI in Healthcare
Role of AI in Healthcare, Data acquisition concepts: Sampling, quantization and interpolation, Data filtering: FIR and IIR filters, Image filtering techniques, Data analysis: Time-domain, frequency-domain and time-frequency-domain analysis,

UNIT – II: AI for Cardiovascular signals
Electrocardiogram (ECG), Cardiac physiology, Electrophysiology, ECG introduction, Cardiovascular diseases and ECG, Processing and feature extraction of ECG, AI/ML models for ECG, Phonocardiogram(PCG), PCG introduction and overview, Analysis(Acquisition, Filtering, and feature extraction) of PCG, AI/ML models for PCG

UNIT – III: AI for Electroencephalogram
EEG: signal of the brain, Brain and its function, EEG introduction and diagnostic purpose, Processing and Feature extraction of EEG
AI/ML models for EEG

UNIT – IV: AI for Biomedical images
Computed Tomography, X-ray imaging and computed tomography, Introduction and Principle of X-ray, AI/ML for classifying diseases on x-rays, Magnetic resonance imaging, Physics and Physiological principles of MRI, AI/ML to process MRI images, Ultrasound imaging, Physical and physiological principles of ultrasound

Understand the foundations of the physiological system and then study cutting-edge AI/ML models in the context of various healthcare data, including cardiovascular signals, brain signals, and medical imaging modalities.

Exploring the potential of AI methods, emphasizing machine learning and applying them to specific areas in medicine and healthcare.

Understand the challenges of regulation of AI applications and which model components can be regulated.

As a project-based course, applying cutting-edge machine learning techniques to concrete problems in modern medicine.

On completion of this course, the students will have the ability to: CO1: Understand the principle of various biomedical signals and images. CO2: Understand the physiological characteristics provided by the biomedical signals and images, such as the reading of an ECG signal. CO3: Analyze the performance of specific AI/ML models as applied to biomedical problems and justify their use and limitations. CO4: Identify and apply appropriate AI/ML models and computational tools to specific problems in biomedicine and healthcare. CO5: Comprehend a collection of AI/ML models and their applications in medicine and healthcare.

Kayvan Najarian, Robert Splinter, Biomedical Signal and Image Processing, CRC Press, Taylor and Francis Group , 2nd Edition, 2012

Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, Handbook of Artificial Intelligence in Biomedical Engineering, CRC Press, Taylor and Francis Group , 1st edition, 2021

DC Reddy, Biomedical Signal Processing – Principles and Techniques, Tata McGraw Hill Publishing company Ltd. , 2005

Donna L. Hudson, Maurice E. Cohen, 4. Neural Networks and Artificial Intelligence for Biomedical Engineering, IEEE Press Series on Biomedical Engineering , 1999

IEEE transactions on Biomedical Engineering

Biomedical Signal Processing and Control