Key Features

• Focus on Physiological Signals – Emphasizes acquisition, processing, and interpretation of biomedical signals such as Electrocardiogram (ECG) and Electroencephalogram (EEG).
• Signal Preprocessing Techniques – Covers noise removal, filtering, and artifact reduction to improve signal clarity and accuracy.
• Feature Extraction – Discusses methods to identify key characteristics like heart rate variability in ECG and brain wave patterns in EEG.
• Time and Frequency Domain Analysis – Explores analytical tools such as FFT, wavelet transforms, and spectral analysis for signal interpretation.
• Pattern Recognition and Classification – Introduces machine learning approaches for disease detection and diagnosis.
  Clinical Applications – Demonstrates real-world uses in cardiac monitoring, neurological assessment, and medical device development.
  Includes numerous coding exercises, quizzes, and mini projects for self-assessment.
  Voice to the Marginalized – Amplifies the experiences of oppressed groups—indigenous peoples, women, and minorities—long silenced in traditional literature.
  Automation and AI Integration – Highlights the role of AI and deep learning in automated biomedical diagnostics.