Postgraduate Diploma in Digital Filtering, Artefact Detection and Signal Quality
Online 800-hour Postgraduate Diploma in Digital Filtering, Artefact Detection and Signal Quality. 8 modules covering IEC 60601-2-25 (electrocardiographs), IEC 60601-2-27 (ECG monitoring) and MATLAB (Signal Processing Toolbox). Assigned tutor and open enrolment.
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What you will be able to do
Describe the characteristics of the main biomedical signals.
Design FIR and IIR digital filters for physiological signals.
Apply adaptive filtering to remove interference.
Detect motion and electrode artefacts.
Calculate signal quality indices.
Analyse signals in the spectral and time-frequency domains.
Implement and validate algorithms against reference databases.
Who it's for
- Biomedical signal processing engineer looking to specialise in Digital Filtering, Artefact Detection and Signal Quality.
- Monitoring algorithm developer looking to specialise in Digital Filtering, Artefact Detection and Signal Quality.
- Medical device software engineer looking to specialise in Digital Filtering, Artefact Detection and Signal Quality.
- Biomedical engineering researcher looking to specialise in Digital Filtering, Artefact Detection and Signal Quality.
- Health data scientist looking to specialise in Digital Filtering, Artefact Detection and Signal Quality.
- Algorithm validation engineer looking to specialise in Digital Filtering, Artefact Detection and Signal Quality.
- Engineering or science graduates seeking to enter medical device manufacturers.
What you'll work with
- IEC 60601-2-25 (electrocardiographs)
- IEC 60601-2-27 (ECG monitoring)
- ANSI/AAMI EC57 (evaluation of arrhythmia algorithms)
- IEC 60601-2-26 (electroencephalographs)
- IEC 60601-2-47 (Holter systems)
- IEC 62304
8 modules · 800 h
1.1Origin and characteristics of ECG, EMG, EEG and PPG1.2Sampling and the Nyquist theorem1.3Analogue-to-digital conversion and quantisation1.4Signal-to-noise ratio1.5Noise sources in acquisition1.6Mains interference and harmonics1.7Baseline drift1.8Wearable versus clinical signals1.9Data formats: EDF and WFDB1.10Practice: exploring PhysioNet recordings
2.1Discrete systems and the Z-transform2.2Frequency and phase response2.3FIR filters: windows and optimal design2.4IIR filters: Butterworth, Chebyshev and elliptic2.5Zero-phase filtering2.6Notch filters2.7Stability and numerical precision2.8Real-time implementation2.9Design with SciPy and MATLAB2.10Laboratory: designing ECG filters in Python
3.1Adaptive LMS and RLS filters3.2Noise cancellation with a reference3.3Kalman filter3.4Median and non-linear filters3.5Wavelet transform and thresholding3.6Empirical mode decomposition3.7Independent component analysis3.8Spatial filtering in multichannel recordings3.9Comparison of noise removal methods3.10Practice: motion artefact cancellation in PPG
4.1Artefact types: motion, contact and saturation4.2Physiological artefacts: ocular and muscular4.3Threshold-based detection4.4Statistical indicators: kurtosis and entropy4.5Machine learning detection4.6Use of accelerometers as reference4.7Marking and segmentation4.8Interpolation and reconstruction4.9Validation against expert annotations4.10Laboratory: EEG artefact detector
5.1Concept of the signal quality index (SQI)5.2SQI for ECG: kSQI, bSQI and pSQI5.3SQI for PPG: perfusion and morphology5.4Quality in EEG and EMG5.5Fusion of quality indices5.6Acceptable or unacceptable classification5.7Real-time quality in devices5.8Impact of quality on clinical accuracy5.9Labelled quality databases5.10Practice: calculating SQI on ambulatory ECG recordings
6.1Discrete Fourier transform and FFT6.2Power spectral density: Welch's method6.3Spectral leakage and windows6.4Spectrograms and STFT6.5Continuous wavelet analysis6.6Frequency-domain heart rate variability analysis6.7EEG bands6.8Median frequency in EMG and fatigue6.9Coherence between signals6.10Laboratory: spectral analysis of HRV
7.1Filtering on microcontrollers7.2Fixed-point arithmetic7.3Latency and group delay7.4Edge versus cloud processing7.5IEC 60601-2-47 and 2-27 requirements7.6Algorithm verification7.7Reference test sets7.8Documentation for medical software7.9Continuous improvement with field data7.10Case study: validating an ECG pipeline on a patch
8.1Signal and application selection8.2Noise characterisation8.3Filter design8.4Artefact detection8.5Quality index8.6Pipeline implementation8.7Performance evaluation8.8Technical documentation8.9Report and presentation8.10Final review with the tutor
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB (Signal Processing Toolbox), Python (SciPy, MNE), PhysioNet WFDB, EEGLAB, in free, academic or trial versions. Tests are studied using real processing procedures on PhysioNet databases, recording signals with controlled artefacts, and validating algorithms against reference annotations. Assessment is continuous per module plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Describe the characteristics of the main biomedical signals.
Design FIR and IIR digital filters for physiological signals.
Apply adaptive filtering to remove interference.
Detect motion and electrode artefacts.
Calculate signal quality indices.
Where this takes you
Requirements
- Prior technical training or professional experience in the sector. No university degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow the reading of technical regulations.
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 800 hours spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You work with MATLAB (Signal Processing Toolbox), Python (SciPy, MNE), PhysioNet WFDB, EEGLAB. Free, academic or trial versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official Ministry qualification.
Yes. Modules passed in a course or postgraduate diploma in the same area are credited towards the advanced programme. Check the credit transfer policy when you enrol.
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