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Postgraduate DiplomaHealth, Biomechanics, Wearables & Operational Safety Applications open

Postgraduate Diploma in Physiological Event Detection Algorithms

Online 800-hour Postgraduate Diploma in Physiological Event Detection Algorithms. 8 modules covering ANSI/AAMI EC57 (evaluation of heart rhythm algorithms), IEC 60601-2-47 (ambulatory electrocardiography systems) and Python (scikit-learn). Personal tutor assigned and enrolment open.

Duration800 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Characterise the physiological events of interest, such as arrhythmias, apnoeas or seizures.

02

Apply pre-processing and noise removal techniques.

03

Implement classical detectors based on thresholds and morphology.

04

Train machine learning models for event detection.

05

Evaluate performance using sensitivity and positive predictive value metrics.

06

Deploy real-time algorithms on resource-constrained devices.

07

Prepare the regulatory documentation for algorithms as medical devices.

Profile

Who it's for

  • Biomedical algorithm engineer wishing to specialise in Physiological Event Detection Algorithms.
  • Clinical data scientist wishing to specialise in Physiological Event Detection Algorithms.
  • Medical device software engineer wishing to specialise in Physiological Event Detection Algorithms.
  • Signal processing researcher wishing to specialise in Physiological Event Detection Algorithms.
  • Embedded systems engineer wishing to specialise in Physiological Event Detection Algorithms.
  • Healthcare AI regulation specialist wishing to specialise in Physiological Event Detection Algorithms.
  • Engineering or science graduates seeking to enter the medical device sector.
Standards & tools

What you'll work with

  • ANSI/AAMI EC57 (evaluation of heart rhythm algorithms)
  • IEC 60601-2-47 (ambulatory electrocardiography systems)
  • IEC 62304 (medical device software)
  • Regulation (EU) 2017/745 (medical devices)
  • Regulation (EU) 2024/1689 (artificial intelligence)
  • ISO 14971 (risk management)
  • IEC 82304-1 (health software)
Syllabus

8 modules · 800 h

  1. 1.1ECG events: beats, arrhythmias and ischaemia
  2. 1.2PPG events: pulse and desaturation
  3. 1.3Respiratory events: apnoeas and hypopnoeas
  4. 1.4EEG events: seizures and sleep spindles
  5. 1.5EMG events: muscle activation
  6. 1.6Inertial events: falls and tremor
  7. 1.7Clinical definitions and AASM criteria
  8. 1.8PhysioNet databases
  9. 1.9Expert event annotation
  10. 1.10Laboratory: exploring MIT-BIH on PhysioNet
  1. 2.1Filtering and resampling
  2. 2.2Segmentation and windowing
  3. 2.3Artefact detection
  4. 2.4Normalisation across subjects
  5. 2.5Interpolation of missing data
  6. 2.6Channel fusion
  7. 2.7Fourier transform and wavelets
  8. 2.8Time-frequency representations
  9. 2.9Libraries: NeuroKit2, MNE and SciPy
  10. 2.10Practice: pre-processing pipeline for an ambulatory ECG
  1. 3.1Pan-Tompkins algorithm
  2. 3.2Peak detection in PPG
  3. 3.3Adaptive thresholds
  4. 3.4Apnoea detection from airflow and SpO2
  5. 3.5Heart rate variability
  6. 3.6Atrial fibrillation detection from RR irregularity
  7. 3.7Templates and correlation
  8. 3.8State machines
  9. 3.9Advantages in low-power devices
  10. 3.10Workshop: implementing Pan-Tompkins in Python
  1. 4.1Feature extraction
  2. 4.2Classifiers: SVM, random forest and boosting
  3. 4.31D convolutional networks
  4. 4.4Recurrent networks and transformers
  5. 4.5Self-supervised learning on physiological series
  6. 4.6Class imbalance
  7. 4.7Subject-wise validation and leave-one-subject-out
  8. 4.8Interpretability with SHAP
  9. 4.9Embedded models with TinyML
  10. 4.10Practice: atrial fibrillation classifier with a CNN
  1. 5.1Sensitivity, specificity and predictive values
  2. 5.2Per-event versus per-sample metrics
  3. 5.3ROC and PR curves
  4. 5.4ANSI/AAMI EC57 for ECG algorithms
  5. 5.5False alarm rate per hour
  6. 5.6Confidence intervals
  7. 5.7Generalisation across populations
  8. 5.8Demographic biases
  9. 5.9Error analysis
  10. 5.10Laboratory: EC57 evaluation of a QRS detector
  1. 6.1On-device versus cloud processing
  2. 6.2Latency and power consumption
  3. 6.3Model quantisation
  4. 6.4Alarm management and prioritisation
  5. 6.5Integration with clinical applications
  6. 6.6Performance monitoring in production
  7. 6.7Data drift
  8. 6.8Algorithm updates
  9. 6.9Logging and traceability
  10. 6.10Case study: alert architecture for a cardiac patch
  1. 7.1Software as a medical device
  2. 7.2MDR classification rules
  3. 7.3IEC 62304 for algorithms
  4. 7.4FDA predetermined change control plan
  5. 7.5EU AI Act and medical devices
  6. 7.6Clinical validation according to MDCG
  7. 7.7Managing false-negative risk
  8. 7.8Algorithm documentation
  9. 7.9Ethics and fairness
  10. 7.10Practice: clinical validation plan for an apnoea detector
  1. 8.1Event definition and clinical use
  2. 8.2Data selection
  3. 8.3Pre-processing
  4. 8.4Algorithm development
  5. 8.5Performance evaluation
  6. 8.6Deployment plan
  7. 8.7Regulatory analysis
  8. 8.8Risk analysis
  9. 8.9Report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

Fully 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 the programme with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), PyTorch, MATLAB, WFDB (PhysioNet), in free, academic or evaluation versions. Tests are studied using real evaluation procedures with PhysioNet databases, real-time validation on microcontrollers, and retrospective clinical studies. Assessment is continuous per module plus the final integrative project.

Accreditable laboratories(EN ISO/IEC 17025) with rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-driven final project:safety case, test plan, compliance dossier and operating limits.
Industry-mentored:lecturers with experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Event definition and clinical use

Characterise the physiological events of interest, such as arrhythmias, apnoeas or seizures.

Data selection

Apply pre-processing and noise removal techniques.

Pre-processing

Implement classical detectors based on thresholds and morphology.

Algorithm development

Train machine learning models for event detection.

Performance evaluation

Evaluate performance using sensitivity and positive predictive value metrics.

Career opportunities

Where this takes you

Biomedical algorithm engineerClinical data scientistMedical device software engineerSignal processing researcherEmbedded systems engineerHealthcare AI regulation specialist
Admissions & scholarships

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.
  • A level of Spanish sufficient to follow technical regulatory texts.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

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.

The programme uses Python (scikit-learn), PyTorch, MATLAB, WFDB (PhysioNet). Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An in-house diploma from SEIUM 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 recognised towards the higher programme. Check the recognition policy when you enrol.

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