Master’s in Biosensors and Biosignal Processing Engineering
Online master's programme of 1,900 hours in biosensor engineering and biosignal processing. 12 modules covering electrochemical and optical biosensors, biopotential acquisition, ECG, EEG, EMG and PPG processing, machine learning, device integration and IVDR/MDR regulation, with an assigned technical tutor.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Explain the transduction principles of biosensors.
Characterise electrochemical and optical biosensors in the laboratory.
Design safe biopotential acquisition chains.
Process ECG, EEG, EMG and PPG signals using digital techniques.
Apply validated machine learning to biosignals.
Integrate biosensors into devices with biocompatibility criteria.
Plan regulatory compliance under the MDR and IVDR.
Who it's for
- Biosensor engineers looking to deepen their knowledge of signal processing and regulation.
- Biomedical signal processing engineers moving towards sensor design.
- In vitro diagnostics researchers.
- Clinical algorithm developers.
- IVD regulatory affairs specialists.
- Biomedical hardware engineers.
- Engineering or science graduates looking to enter in vitro diagnostics.
What you'll work with
- Regulation (EU) 2017/746 (in vitro diagnostic medical devices)
- Regulation (EU) 2017/745 (medical devices)
- ISO 15197 (blood-glucose self-testing systems)
- ISO 13485 (quality management systems for medical devices)
- IEC 60601-2-47 (ambulatory electrocardiographic systems)
- ANSI/AAMI EC57 (testing of arrhythmia algorithms)
- ISO 10993-1 (biological evaluation)
- ISO 14971 (risk management)
- MATLAB
- Python with SciPy and NeuroKit2
- EEGLAB
- LabVIEW
- COMSOL Multiphysics
12 modules · 1900 h
1.1Definition and architecture of a biosensor1.2Biological recognition elements1.3Electrochemical, optical and piezoelectric transducers1.4Sensitivity, selectivity and limit of detection1.5Calibration curves and dynamic range1.6Stability, drift and shelf life1.7Glucose biosensors as a reference1.8Market and clinical applications1.9Lactate and ketone biosensors1.10Case study: analysis of a commercial glucometer
2.1Electrodes and three-electrode cells2.2Amperometry and cyclic voltammetry2.3Electrochemical impedance spectroscopy2.4Second- and third-generation enzymatic sensors2.5Immunosensors and aptasensors2.6Low-cost screen-printed electrodes2.7Nanomaterials for signal amplification2.8Sweat and tear sensors2.9Continuous interstitial glucose biosensors2.10Laboratory: cyclic voltammetry with a potentiostat
3.1Surface plasmon resonance3.2Fluorescence and chemiluminescence3.3Optical fibre and waveguides3.4Quartz crystal microbalance3.5Paper-based colorimetric sensors3.6Lateral flow tests3.7Smartphone-based detection3.8Point-of-care biosensors3.9Graphene- and transistor-based biosensors3.10Practical exercise: reading a lateral flow test
4.1Origin of ECG, EEG and EMG4.2Electrode-skin interface impedance4.3Instrumentation amplifiers for biopotentials4.4Common-mode rejection and the right-leg drive circuit4.5Isolation and electrical safety4.6Dry and textile electrodes4.7High-density acquisition4.8IEC 60601-2-47 for ambulatory ECG recording4.9Multi-frequency bioimpedance and body composition4.10Laboratory: ECG front-end with the ADS1292
5.1Nyquist theorem and aliasing5.2Quantisation and resolution5.3Fourier transform and spectrograms5.4FIR and IIR filters5.5Mains interference filtering5.6Resampling and synchronisation5.7Windowing and spectral leakage5.8EDF and WFDB formats5.9Outlier detection in signals5.10Practical exercise: designing ECG filters in Python
6.1QRS detection with Pan-Tompkins6.2Delineation of P and T waves6.3RR series and non-linear analysis6.4Atrial fibrillation detection6.5ST segment analysis6.6Signal quality and artefacts6.7PhysioNet databases6.8Validation with ANSI/AAMI EC576.9Ventricular arrhythmias and their detection6.10Laboratory: QRS detector on MIT-BIH
7.1Brain rhythms and frequency bands7.2Artefact removal with ICA7.3Evoked potentials and averaging7.4Non-invasive brain-computer interfaces7.5EMG decomposition into motor units7.6Muscle onset detection7.7EMG fatigue indices7.8Time-frequency analysis with wavelets7.9EMG spectral bands and fatigue7.10Practical exercise: state classification with EEG
8.1Photoplethysmography and SpO28.2Cuffless blood pressure8.3Pulse transit time8.4Capnography and respiratory signals8.5Thoracic bioimpedance and cardiac output8.6Phonocardiography and heart sounds8.7Multimodal signal fusion8.8Continuous monitoring in the ICU8.9Reflective oximetry in wrist and ring devices8.10Case study: respiratory estimation from PPG
9.1Classical feature extraction9.21D convolutional networks9.3Recurrent networks and transformers9.4Learning with limited data9.5Physiological data augmentation9.6Subject-wise validation9.7Explainability in clinical decisions9.8Deployment on embedded devices9.9Self-supervised learning on physiological signals9.10Laboratory: 1D network for arrhythmias
10.1Embedded system architectures10.2Real-time processing10.3Power consumption and autonomy10.4Microfluidics and lab-on-chip10.5Biocompatible encapsulation and ISO 1099310.6Sterilisation of biosensors10.7Production and batch control10.8Reliability of implantable sensors10.9Wireless communication of implanted sensors10.10Workshop: specification of a multisensor patch
11.1Regulation (EU) 2017/746 on in vitro diagnostics11.2Regulation (EU) 2017/745 on medical devices11.3ISO 13485 at biosensor manufacturers11.4ISO 15197 for glucometers11.5Analytical performance evaluation11.6Risk management with ISO 1497111.7Analysis software and IEC 6230411.8Clinical performance studies11.9Algorithm validation against annotated databases11.10Case study: regulatory pathway for a POC test
12.1Clinical need and target biomarker12.2Transduction selection and design12.3Prototype fabrication or acquisition12.4Analytical characterisation of the sensor12.5Signal processing chain12.6Interpretation algorithm12.7Evaluation with samples or volunteers12.8Regulatory and quality fit12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
Training is 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with full-time work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB, Python with SciPy and NeuroKit2, EEGLAB, LabVIEW and COMSOL Multiphysics, in free, academic or evaluation versions. Testing is studied through real procedures: electrochemical characterisation with a potentiostat, processing of recordings from PhysioNet databases and design of analogue front-ends for biopotentials. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2017/746, Regulation (EU) 2017/745, ISO 15197, ISO 13485, IEC 60601-2-47, ANSI/AAMI EC57, ISO 10993-1, ISO 14971.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MATLAB, Python with SciPy and NeuroKit2, EEGLAB, LabVIEW and COMSOL Multiphysics, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme software and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
Related programmes
Master’s in Movement Neuromechanics and Musculoskeletal Modelling Engineering
Online master's programme of 1,900 hours in movement neuromechanics and musculoskeletal modelling engineering: 12 modules covering motor control neurophysiology, musculotendon mechanics, joint kinematics, neuromechanical data acquisition,…
Master’s in Human Vibration, Shock and Fatigue Engineering
Online master's programme of 1,900 hours in vibration, shock and human fatigue engineering: 12 modules covering vibrating system dynamics, human response to vibration, whole-body and hand–arm…
Master’s in Emergency, Business Continuity and Resilience Engineering
Online master's programme of 1,900 hours in Emergency, Operational Continuity and Resilience Engineering, with 12 modules covering risk and resilience, civil protection regulations, threat analysis, emergency…
More in Health, Biomechanics, Wearables & Operational Safety
All Health, Biomechanics, Wearables & Operational Safety programmes → · How to apply · Fees & financing · Scholarships