Biomedical Signal Processing Course | IIT Kharagpur | Prof. Sudipta Mukhopadhyay
Course Details
| Exam Registration | 747 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Electrical, Electronics and Communications Engineering |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 25 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Unlock the Secrets of Life's Signals: A Guide to the Biomedical Signal Processing Course
Biomedical Signal Processing sits at the thrilling intersection of engineering, medicine, and data science. It is the discipline that empowers us to extract life-saving information from the electrical whispers of the human body—the heartbeat (ECG), brainwaves (EEG), muscle activity (EMG), and more. For engineering students and professionals looking to make a tangible impact in healthcare technology, mastering this field is paramount.
We are excited to present a detailed overview of a premier educational offering: the Biomedical Signal Processing course taught by the distinguished Prof. Sudipta Mukhopadhyay from IIT Kharagpur. This 12-week program is meticulously designed to transform complex theory into practical, problem-solving skills.
Meet Your Instructor: Prof. Sudipta Mukhopadhyay
The course is led by an authority in the field. Prof. Sudipta Mukhopadhyay brings a rare blend of deep academic rigor and rich industrial experience. An alumnus of Jadavpur University and IIT Kanpur (MTech, PhD), he served for over a decade in leading roles at Philips Medical Systems and GE Global Research, Bangalore. This practical exposure to the forefront of medical technology informs every lecture. With over 100 publications and having guided more than 90 postgraduate scholars, Prof. Mukhopadhyay is uniquely positioned to bridge the gap between textbook concepts and real-world biomedical engineering challenges.
Who Is This Course For?
This course is crafted to be both comprehensive and accessible:
- Primary Audience: Final-year Undergraduate and Postgraduate students in Electrical, Electronics, Instrumentation, and Communications Engineering.
- Also Suitable For: Students from Computer Science, Mathematics, Geophysics, or Physics with an interest in signal applications.
- Prerequisites: A solid foundation in Signals and Systems and basic probability is required. Familiarity with MATLAB is highly recommended, as it is the core tool for tutorials.
- Industry Relevance: The curriculum is directly supported by and relevant to R&D divisions of major players like Philips, GE, Siemens, TCS, Wipro, and Conduent Labs.
Course Philosophy: The Engineer as a Problem Solver
Moving away from passive learning, this course adopts a hands-on, problem-solving approach. The entire syllabus is structured as a series of problems and their engineering solutions. This method ensures that you don't just learn concepts but learn how to apply them—a critical skill for any successful engineer.
Detailed 12-Week Course Layout
Here is a week-by-week breakdown of what you will master:
| Week | Core Topics Covered |
|---|---|
| Week 1-2 | Fundamentals & Filtering: Origin of ECG, EEG, EMG. Time-domain (Synchronized Averaging, Moving Average) and Frequency-domain (Notch, Weiner) filtering for artifact removal. |
| Week 3-4 | Advanced Filtering & Event Detection: Adaptive Filtering, the iconic Pan-Tompkins Algorithm for QRS detection in ECG, and Dicrotic Notch detection. |
| Week 5-6 | Waveform Analysis: Morphological analysis, envelope extraction, and key feature calculation (RMS value, Zero-crossing rate, Turns Count). |
| Week 7 | Frequency-Domain Analysis: Power Spectral Density (PSD) estimation using Periodogram, Blackman-Tukey methods, and derived measures. |
| Week 8-9 | Biomedical System Modelling: Parametric modelling with Autoregressive (AR) and ARMA models, Levinson-Durbin algorithm, and model order selection. |
| Week 10-12 | Hands-On Tutorials with MATLAB: Applied sessions on filter design, QRS detection, correlation analysis, PSD computation, and feature extraction. Enrolled students get access to MATLAB for the course duration. |
Essential Reference Materials
The course content is complemented by a robust reading list, including seminal texts like:
- Biomedical Signal Analysis: A Case-Based Approach by R.M. Rangayyan
- Biomedical Digital Signal Processing by Willis J. Tompkins
- Discrete-time Signal Processing by Oppenheim and Schafer
- Modern Spectral Estimation by Steven M. Kay
Why Enroll in This Course?
This course is more than just a syllabus; it's a career investment. You will gain:
- Practical Skills: From designing a Weiner filter to implementing a real-time QRS detector.
- Industry-Aligned Knowledge: Learn the techniques used in cutting-edge medical device and health-tech companies.
- Expert Guidance: Learn from a professor who has been both a creator and a user of this technology in the industry.
- Strong Foundation: Whether you aim for research, product development, or data analysis in med-tech, this course provides the core toolkit.
If you are ready to decode the signals of life and build the next generation of diagnostic tools, this Biomedical Signal Processing course from IIT Kharagpur is your ideal starting point.
Enroll Now →