Biomedical Signal Processing

ECG and EEG are foundational biosignals that represent electrical activity of heart and brain respectively. This chapter builds interpretation skills for waveform morphology, rhythm metrics, and frequency-band meaning.

Signal filtering removes noise and interference while preserving clinically relevant waveform content. The chapter emphasizes frequency-domain reasoning, filter-type trade-offs, and biomedical-specific constraints.

Time-frequency analysis helps reveal hidden periodicity, energy distribution, and transient events in biomedical recordings. It is central for ECG variability studies, EEG rhythms, and vibration-based prosthetic diagnostics.

DSP for biomedical data focuses on implementing reliable algorithms under real-time and hardware constraints. Beyond mathematics, this topic examines computational efficiency, numerical stability, and clinical robustness.

Feature extraction and classification convert biomedical waveforms into decision-support outputs such as normal/abnormal labels. The key challenge is choosing robust features and evaluating models with clinically meaningful metrics.