Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | MAT101 | Mathematics I | 3-1-0-4 | - |
| 1 | PHY101 | Physics I | 3-1-0-4 | - |
| 1 | CSE101 | Introduction to Computer Science | 2-1-0-3 | - |
| 1 | BIO101 | Introduction to Biology | 3-1-0-4 | - |
| 2 | MAT102 | Mathematics II | 3-1-0-4 | MAT101 |
| 2 | PHY102 | Physics II | 3-1-0-4 | PHY101 |
| 2 | ECE101 | Electrical Circuits and Networks | 3-1-0-4 | - |
| 2 | BIO102 | Human Anatomy and Physiology | 3-1-0-4 | BIO101 |
| 3 | MAT201 | Statistics and Probability | 3-1-0-4 | MAT102 |
| 3 | ECE201 | Analog Electronics | 3-1-0-4 | ECE101 |
| 3 | CSE201 | Data Structures and Algorithms | 3-1-0-4 | CSE101 |
| 3 | BIO201 | Biochemistry | 3-1-0-4 | BIO102 |
| 4 | MAT202 | Differential Equations | 3-1-0-4 | MAT201 |
| 4 | ECE202 | Digital Electronics | 3-1-0-4 | ECE201 |
| 4 | CSE202 | Database Management Systems | 3-1-0-4 | CSE201 |
| 4 | BIO202 | Molecular Biology | 3-1-0-4 | BIO201 |
| 5 | ECE301 | Signals and Systems | 3-1-0-4 | MAT202 |
| 5 | EE301 | Control Systems | 3-1-0-4 | ECE202 |
| 5 | ME301 | Applied Mechanics | 3-1-0-4 | - |
| 5 | BIO301 | Cellular and Molecular Biology | 3-1-0-4 | BIO202 |
| 6 | ECE302 | Microprocessors and Microcontrollers | 3-1-0-4 | ECE202 |
| 6 | EE302 | Power Electronics | 3-1-0-4 | EE301 |
| 6 | CSE301 | Software Engineering | 3-1-0-4 | CSE202 |
| 6 | BIO302 | Immunology | 3-1-0-4 | BIO301 |
| 7 | ECE401 | Biomedical Instrumentation | 3-1-0-4 | ECE301 |
| 7 | EE401 | Electromagnetic Fields and Waves | 3-1-0-4 | EE302 |
| 7 | CSE401 | Artificial Intelligence | 3-1-0-4 | CSE301 |
| 7 | BIO401 | Genetics and Genomics | 3-1-0-4 | BIO302 |
| 8 | ECE402 | Medical Device Design | 3-1-0-4 | ECE401 |
| 8 | EE402 | Advanced Control Systems | 3-1-0-4 | EE401 |
| 8 | CSE402 | Machine Learning | 3-1-0-4 | CSE401 |
| 8 | BIO402 | Pathology and Diagnostic Techniques | 3-1-0-4 | BIO401 |
Advanced Departmental Electives
These courses are designed to provide students with in-depth knowledge in specialized areas of medical instrumentation. Each course is offered in the respective semester and has specific prerequisites that ensure a smooth progression of learning.
Biomedical Instrumentation Lab (ECE401)
This lab course focuses on hands-on experience with various biomedical instruments, including ECG machines, blood pressure monitors, and pulse oximeters. Students learn to design, construct, calibrate, and troubleshoot these devices while gaining exposure to real-world clinical environments.
Medical Device Design (ECE402)
This course covers the entire process of designing medical devices from conceptualization to prototyping and testing. It includes topics such as regulatory compliance, risk assessment, user interface design, and product lifecycle management.
Advanced Control Systems (EE402)
Building upon earlier control systems courses, this course explores advanced topics such as nonlinear control, robust control, and adaptive control. Students apply these concepts to design control systems for medical devices that must operate reliably under varying conditions.
Machine Learning in Healthcare (CSE402)
This elective introduces students to machine learning techniques specifically tailored for healthcare applications. Topics include deep learning architectures, natural language processing for clinical notes, and predictive analytics for patient outcomes.
Immunology and Immunotherapy (BIO402)
This course explores the mechanisms of immune response and emerging therapies in immunotherapy. Students gain insights into how immunological principles can be applied to develop novel diagnostic tools and treatment modalities.
Genetics and Genomics (BIO401)
Students learn about genetic disorders, genomic technologies, and personalized medicine approaches. The course includes laboratory sessions on DNA sequencing, gene expression analysis, and bioinformatics tools for data interpretation.
Electromagnetic Fields and Waves (EE401)
This advanced course covers the theoretical and practical aspects of electromagnetic fields and their applications in medical imaging technologies such as MRI and ultrasound. Students also explore antenna design and wireless communication systems used in portable medical devices.
Biomedical Signal Processing (ECE301)
This course focuses on analyzing and processing physiological signals using digital signal processing techniques. Students learn to filter noise, extract features, and detect anomalies in signals such as ECG, EEG, and EMG.
Artificial Intelligence (CSE401)
This course introduces students to AI concepts and applications in healthcare, including neural networks, decision trees, clustering algorithms, and reinforcement learning. Emphasis is placed on building intelligent systems that can assist clinicians in diagnosis and treatment planning.
Software Engineering for Healthcare (CSE301)
This elective covers software development practices tailored for healthcare environments. Topics include agile methodologies, software testing standards, data privacy regulations, and integration with medical systems.
Power Electronics in Medical Devices (EE302)
Students study power conversion techniques used in medical equipment such as defibrillators, infusion pumps, and ventilators. The course includes practical sessions on designing efficient power supplies and ensuring safety standards.
Regulatory Compliance in Medical Devices (ECE402)
This course provides an overview of regulatory frameworks governing medical device development, including FDA guidelines, ISO standards, and international regulations. Students learn how to navigate the approval process and ensure compliance throughout the product lifecycle.
Embedded Systems for Medical Devices (ECE302)
This elective focuses on designing embedded systems for medical applications using microcontrollers, FPGAs, and real-time operating systems. Students gain experience in developing robust, reliable systems that meet stringent performance and safety requirements.
Digital Signal Processing (ECE202)
Students explore digital signal processing techniques used in biomedical applications. The course includes topics such as sampling theory, discrete Fourier transforms, and filter design methods for analyzing physiological signals.
Biomedical Sensors and Instrumentation (ECE301)
This course introduces students to various sensors used in medical diagnostics and monitoring systems. It covers sensor characteristics, calibration procedures, and integration into larger instrumentation systems.
Medical Imaging Systems (ECE401)
Students study the principles behind different imaging modalities including X-ray, CT, MRI, and ultrasound. The course includes theoretical foundations as well as practical aspects of image acquisition, reconstruction, and interpretation.
Project-Based Learning Philosophy
The department strongly believes in project-based learning as a means to enhance student engagement and apply theoretical knowledge to real-world challenges. Mini-projects are integrated throughout the curriculum starting from the second year, allowing students to explore their interests while building foundational skills.
Mini-projects typically span 2-3 months and involve working in teams of 4-6 students. Each project must address a specific problem or opportunity within the field of medical instrumentation. Projects are evaluated based on innovation, feasibility, technical execution, and presentation quality.
The final-year thesis/capstone project is a major component of the program that spans the entire semester. Students select projects under the guidance of faculty mentors who provide mentorship, resources, and feedback throughout the process. The project culminates in a formal presentation and written report that demonstrates mastery of core competencies.
Faculty mentors are selected based on their expertise and availability to guide students effectively. Students are encouraged to propose their own ideas but are also supported in exploring existing research areas or industry challenges. The selection process involves a proposal submission, followed by a brief interview with the faculty member.