Biomedical Signal Processing
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Follow on Instagram for UpdatesBiomedical Signal Processing Syllabus — Unit-wise Content
The official GTU curriculum for Biomedical Signal Processing (subject code 2713109) as prescribed for BE · Semester 1 divides the course into 6 units, covering 38 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (2713109)Unit 3 Filtering for removal of artifacts
- Random noise
- structured noise, and physiological interference
- Stationary versus non-stationary processes
- Time-domain filters
- Frequency-domain filters
- Optimal filtering- the wiener filter
- Adaptive filters for removal of interference and applications
Unit 4 Event detection
- Detection of Events and Waves: Derivative-based methods for QRS detection
- The Pan-Tompkins algorithm for QRS detection
- Detection of the dicrotic notch
- Detection of the P wave and Applications
Unit 5 Analysis of Wave shape and waveform complexity
- Illustration of case-studies
- Analysis of Event-related potentials
- Morphological Analysis of ECG waves
- Envelope Extraction and Analysis
- Analysis of Activity: The root mean-squared value
- Zero- crossing rate
- Turns count and Form factor
Unit 6 Frequency-domain characterization of signals and systems
- Estimation of the Power Spectral Density Function: The periodogram
- The need for averaging
- The use of windows: Spectral resolution and leakage
- Estimation of the autocorrelation function
- Synchronized averaging of PCG spectra
- Measures Derived from PSDs: Moments of PSD functions
- Spectral power ratios
Unit 7 Modeling biomedical systems
- Parametric system modeling
- Autoregressive or all pole modeling
- Applications of Modeling biomedical systems
- Concept of curve fitting
Unit 8 Pattern Classification and Diagnostic Decision
- Introduction & case studies
- Pattern Classification
- Supervised Pattern Classification
- Unsupervised Pattern Classification
- Probabilistic Models and Statistical Decision
- Logistic Regression Analysis
- The training and Test Steps
- Neural Networks
- its applications
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