Biomedical Instrumentation¶

Course Overview¶

• Institution: IIT Madras / NPTEL
• Course code: 102106669
• Track: Biomedical Electronics and Signals
• Tier: A
• Role: Mainline
• Level: Intermediate
• Last reviewed: 2026-07-28

IIT Madras / NPTEL's Biomedical Instrumentation provides a broad entry to biomedical instruments through its video sequence, while lacking a reproducible instrumentation project and providing no authorization for diagnosis or human experimentation.

Why choose this course

Mainline course. A reliable option that can serve as a main course or strong alternative. Review note: A-

Before you start

• Recommended foundation: Sensors and Instrumentation
• Recommended foundation: Digital Signal Processing
• Recommended foundation: Physics Foundations

Verifiable learning outcomes

• Explain the core models in Biomedical Electronics and Signals, including their assumptions and limits
• Solve representative derivations and problems, checking units, limiting cases, or numerical results

Workload and pacing

12 weeks at 8 hours/week. The provider publishes 12 weeks; the weekly effort above is a maintainer planning estimate derived from course role and public practice/lab density, not a provider promise. Pilot two weeks and adjust when actual effort differs by more than 25%.

Safety level

Standard study. No physical lab is recorded; follow ordinary electrical, ergonomic, data, and equipment-use precautions.

Course Resources¶

Software, hardware, and cost

Software

• Maintainer-suggested open-source/free verification path: Python 3, Jupyter, MNE-Python, NeuroKit2, and WFDB
• The resource inventory does not list public code coverage; the tools above are only a maintainer-suggested independent check, not a provider requirement

Hardware

• The resource inventory does not list public human-subject lab coverage; default to de-identified public data or a signal simulator, with no equipment purchase or independent connection to a person. Any human measurement must occur only in a compliant institution with approved isolated equipment and completed ethics and safety review; before extending the course, verify provider scope and reassess safety

Cost note

Prefer open-source/free software and de-identified data; this is not a provider equipment requirement. Home purchases cannot substitute for isolation, ethics approval, data access, or institutional supervision, and any costs vary by provider, region, and institution.

Public resource coverage

Resources and access

“Listed by official page” means the link was discovered on a successfully fetched official source on the verification date; it does not guarantee that every region or account can open the target directly. Access does not grant redistribution rights. Re-check the provider page, target link, and third-party notices before downloading, adapting, or publishing material.

Practice and Verification¶

Practice loop

Biomedical Instrumentation · IIT Madras / NPTEL 102106669: Public/Synthetic Biomedical Signal Algorithm Audit

This is a maintainer-suggested self-study project for Biomedical Instrumentation · IIT Madras / NPTEL 102106669, not an official course assignment. Implement a signal or image algorithm for Biomedical Electronics and Signals using only public de-identified or synthetic data, auditing performance, subgroup slices, artifacts, and out-of-distribution failure; collect no human data.

Origin: Maintainer-suggested project

Deliverables

• A preregistration of task, nonclinical purpose, data provenance and license, de-identification status, and metrics
• Data validation, preprocessing, baseline, improved algorithm, and automated test sources
• Raw predictions, labels or synthetic truth, artifacts, and slice metrics on fixed splits
• A model-card-style report covering uncertainty, error cases, fairness limitations, and prohibited uses

Verification

• Beat a simple baseline on a locked test set and report 95% bootstrap intervals for primary metrics
• Cover all-zero or missing channels, amplitude saturation, line-noise artifacts, and synthetic out-of-distribution samples
• Cross-check preprocessing and metrics on at least 20 samples with an independent implementation
• Report the worst non-sensitive technical slice available in public metadata without claiming clinical validity

Reproducibility

• Commit data-acquisition manifest, validation, preprocessing, model, evaluation, and report sources
• Pin public-data version or hashes, splits, environment, dependencies, seeds, and model parameters
• Preserve license-permitted raw or derived data indexes, predictions, logs, and the generated report

Safety boundary: Simulation only — Use only public de-identified or synthetic data; do not instruct or perform human data collection, connect electrodes or sensors to people, or use results for diagnosis, treatment, or clinical decisions.

Risks, gaps, and boundaries

The lecture sequence is broad, but it does not provide a reproducible instrumentation project and must not be treated as authorization for diagnosis or human experimentation; human data or body-connected measurement requires ethics approval and qualified supervision.

Completion evidence

• Weekly learning log with time, questions, corrected errors, decisions, next steps, and links to that week's reproducible artifacts
• Theory dossier with explicit assumptions, notation, derivation, units, and boundary conditions, checked by at least one independent method
• Code repository with pinned dependencies and toolchain, a minimal run command, tests or waveform/benchmark checks, expected output, and license notes