Biomedical Signal Processing Certification

Transform Raw Medical Data into Powerful Clinical Decisions

What you’ll learn
Understand the nature and sources of biomedical signals including ECG, EEG, EMG, and PCG, and how these reflect physiological activity.
Analyze signal characteristics such as nonstationarity, noise, and artifacts, and learn practical strategies to manage them.
Gain knowledge of biomedical signal acquisition, including electrodes, sensors, amplifiers, and analog front-end concepts.
Master core signals and systems fundamentals: continuous vs discrete signals, sampling theorem, aliasing, quantization, and digital representation.
Apply Z-transform, convolution, and difference equations for modeling and analyzing biomedical systems.
Use statistical parameters (mean, variance, correlation) and signal averaging for noise reduction and data interpretation.
Perform event detection such as QRS complexes in ECG, neural spikes in EEG, and bursts in EMG.
Analyze signals in the frequency domain using Fourier Transform, Power Spectral Density, Periodogram, and frequency band analysis (EEG rhythms and HRV).
Extract meaningful spectral features for biomedical applications.
Design and implement digital filters including FIR, IIR, adaptive filters (LMS, RLS), and notch filters for powerline interference removal.
Apply advanced denoising techniques such as EMG noise reduction in ECG and wavelet denoising.
Use wavelet transform for multiresolution analysis of nonstationary biomedical signals.
Perform dimensionality reduction and artifact removal using PCA and ICA, especially for EEG processing.
Understand nonlinear dynamics, entropy measures (Approximate and Sample Entropy), and higher-order statistics including bispectral analysis to characterize phys
Build complete pipelines for feature extraction and dimensionality reduction in biomedical datasets.
Apply machine learning methods such as SVM, k-NN, and Random Forest for biomedical signal classification.
Implement deep learning approaches (CNNs) for automated ECG and EEG interpretation.
Learn architectures for real-time signal processing, including embedded pipelines and low-latency systems.
Understand how biomedical algorithms are deployed in wearable and point-of-care signal processing systems.

Requirements
Basic concepts of signals and systems

Description
This course on Biomedical Signal Processing provides an in-depth understanding of how physiological signals are acquired, analyzed, and interpreted for clinical and research applications. The course begins by exploring the nature and sources of biomedical signals such as ECG, EEG, EMG, and PCG, followed by an examination of their key characteristics including nonstationarity, noise, and artifacts. Learners gain a strong foundation in biomedical signal acquisition, studying electrodes, sensors, and amplifiers, before moving into core principles of signals and systems, including continuous and discrete representations, sampling theorem, aliasing, quantization, and digital signal modeling using Z-transform, convolution, and difference equations.

Building on these fundamentals, the course develops analytical skills through statistical parameters such as mean, variance, and correlation, along with signal averaging and event detection techniques for identifying QRS complexes, spikes, and bursts. Frequency-domain methods are extensively covered, including Fourier Transform, power spectral density, periodogram analysis, EEG frequency bands, heart rate variability, and spectral feature extraction. Students also master digital filtering techniques such as FIR, IIR, adaptive filters (LMS, RLS), notch filters, and EMG noise reduction in ECG signals.

Advanced modules introduce wavelet transform for multiresolution analysis, PCA and ICA for dimensionality reduction and artifact removal, and wavelet denoising techniques. Nonlinear dynamics, entropy measures, higher-order statistics, and bispectral analysis deepen understanding of physiological complexity. The course concludes with feature extraction, machine learning methods (SVM, k-NN, Random Forest), deep learning approaches using CNNs for ECG/EEG classification, and real-time signal processing architectures integrated into wearable and point-of-care systems. By the end, learners will be equipped to design intelligent, real-time biomedical signal analysis systems for modern healthcare applications.

Who this course is for
Biomedical Engineering students who want a strong foundation in signal analysis for ECG, EEG, EMG, and other physiological data.
Electronics and Electrical Engineering students interested in medical instrumentation, embedded systems, and healthcare devices.
Neuroscience and Life Science researchers who work with EEG or physiological recordings and want deeper analytical understanding.
Medical device engineers and developers involved in designing ECG monitors, EEG systems, wearable health devices, or point-of-care systems.
Professionals working on wearable technology focused on health monitoring, fitness tracking, or remote patient monitoring systems.
Clinical researchers and hospital technologists who want to better understand how signal processing improves diagnosis and monitoring accuracy.
Postgraduate students and researchers preparing for advanced study or thesis work in biomedical signal analysis, neuroengineering, or digital health.