Biomedical Signals
| Title | Βιοϊατρικά Σήματα / Biomedical Signals |
| Code | MEI004 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Anthony Aletras |
| Common | No |
| Status | Active |
| Course ID | 600021930 |
Programme of Study: PMS "Iatrikī Mīchanikī kai Plīroforikī" (2022-sīmera)
Registered students: 9
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 6 |
| Academic Year | 2024 – 2025 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600265084
|
Type Of Offer
- Disciplinary Course
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600265084
- eLearning: https://elearning.auth.gr/course/view.php?id=17085
Language of Instruction
- English (Instruction, Examination)
Learning Outcomes
With successful completion of the course the graduate students will know the types of biomedical signals, the mathematical representation of complex signals, basic principles of signal processing as well as newer methods based on neural networks.
General Competences
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Work autonomously
Course Content (Syllabus)
The purpose of the course is to present biomedical signals as well as processing techniques aimed at diagnosis.
The course material includes:
Introduction to signals and their importance in medical research and health services, Complex & Real signals, Sampling, Fourier transform, wavelet analysis, electrocardiogram (ECG) acquisition and processing, heart rate generation (theory and hands-on), analysis ECG and heart rate variability (theories and hands-on), types of signals personalized healthcare-social signals and others, neurophysiological signal preprocessing, neurophysiological signal processing/Deep Learning, neurophysiological signal pre/processing for functional imaging and networks, personality pattern recognition. Lectures include the following topics:
Introduction to biosignals. Complex and Real biosignals
Signals and their importance in medical research and health services
ECG acquisition and processing - The heartbeat concept - Theory & hands on
Social signals
Pre-processing neurophysiological signals
Digitization, Fourier transform
Analysis of ECG and heartbeab variation - Theory & hands on
Neurophysiological signal processing/deep learning
Wavelet analysis
Neurophysiological signal processing - functional neuroimaging - connectivity networks
Neurophysiological signal processing - functional neuroimaging - connectivity networks
Keywords
Biomedical signals
Educational Material Types
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 78 | 3.1 | |||
| Project | 72 | 2.9 | |||
| Total | 150 | 6 |
Student Assessment
Description
Examination on the content of the lectures via multiple choice quiz (60%) and examination of semester project presentation (40%).
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Performance / Staging (Summative)
- Report (Summative)
Last Update
05-12-2023
