BMM020 Processing of Biopotential Signals

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code BMM020
Name Processing of Biopotential Signals
Term 2023-2024 Academic Year
Term Spring
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language Türkçe
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator
Course Instructor
1


Course Goal / Objective

To introduce students to the signal processing techniques when applied specifically to biomedical signals: ECG, EEG and EMG. To teach methods for extracting Information from these signals and demonstrate practical implementations of signal processing techniques to biomedical signals.

Course Content

Biomedical signals and their origins: Electrocardiogram (ECG), Electroencephalography (EEG), Electromyogram (EMG). Basic signal processing methods: noise suppression, trend removal, power spectrum, partitioned into blocks/windows, time-frequency map, modelling, frequency estimation. ECG analysis: detection of R peaks and computation of heart-rate. EEG analysis: derivations/references, physiologic frequency bands and decomposition of these bands. EMG analysis: detection of bursts, power and frequency estimation, computation of envelope of an EMG signal.

Course Precondition

There are no prerequisites for the course.

Resources

There is no textbook that covers the topics of the course entirety. The course must be followed from the lecture and lecture notes.

Notes

No suggested additional course notes.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Integrate application-oriented signal processing techniquea for biomedical signal analysis.
LO02 Selects appropriate signal processing methods to apply to real biomedical signals.
LO03 Applies different biomedical signal processing approaches using Matlab and interprets their results and effects.
LO04 Investigate Alternative Methods for Time and Frequency Domain Analysis of Biomedical Signals.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal To be able to solve scientific problems encountered in the field of medicine and medical technologies by applying current and advanced technical approaches of mathematics, science and engineering sciences. 5
PLO02 Yetkinlikler - Öğrenme Yetkinliği To have a knowledge of the literature related to a sub-discipline of biomedical engineering, to define and model current problems. 5
PLO03 Beceriler - Bilişsel, Uygulamalı Ability to analyze data, design and conduct experiments, and interpret results 4
PLO04 Beceriler - Bilişsel, Uygulamalı Developing researched contemporary techniques and computational tools for engineering applications 4
PLO05 Beceriler - Bilişsel, Uygulamalı To be able to analyze and design a process in line with a defined target 3
PLO06 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Conducting scientific studies with a medical doctor from an engineering perspective. 4
PLO07 Yetkinlikler - İletişim ve Sosyal Yetkinlik Expressing own findings orally and in writing, clearly and concisely.
PLO08 Yetkinlikler - Öğrenme Yetkinliği To be able to improve oneself by embracing the importance of lifelong learning and by following the developments in science-technology and contemporary issues.
PLO09 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Ability to act independently, set priorities and creativity.
PLO10 Yetkinlikler - Alana Özgü Yetkinlik Being aware of national and international contemporary scientific and social problems in the field of Biomedical Engineering.
PLO11 Yetkinlikler - Alana Özgü Yetkinlik To be able to evaluate the contribution of engineering solutions to problems in medicine, medical technologies and health in a global and social context. 3


Week Plan

Week Topic Preparation Methods
1 Introduction. Biomedical signals and their sources. Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG). Textbook reading. Öğretim Yöntemleri:
Anlatım
2 Basic signal processing methods. Sampling and quantization. Digital signals. Linear time invariant systems. Filtering. Noise and trend removal. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
3 Basic signal processing methods. Fourier transform. Power spectrum. Parametric modelling and power spectrum computation. Partitioning into blocks/windows. Short-time Fourier trasnsform. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
4 Basic signal processing methods. Random signals. Probability distributions. Stationary and non-stationary signals. Specification of probability distribution. Expected value. Relation between correlation and power spectrum. Wigner-Ville distribution. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
5 Basic signal processing methods. Signal space methods. Frequency estimation. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
6 ECG Analysis. ECG electrodes and measurement. QRS complex. R-peak detection. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
7 ECG Analysis. Tachogram. Obtaining Heart-rate variability. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
8 Mid-Term Exam Textbook/course notes reading. Ölçme Yöntemleri:
Yazılı Sınav
9 EEG Analysis. Electrode placement. Derivations/references, physiologic frequency bands and decomposition of these bands. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
10 EEG Analysis. EEG as a multichannel signal. Generating multi-input multi-output models. Computation of coherence. Connectivity between channels. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
11 EEG Analysis. Phase synchronized signals: Evoked potentials. Detection and analysis of evoked potentials. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
12 EMG analysis. Detection of bursts, power and frequency estimation, computation of envelope of an EMG signal. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
13 Classification. Linear classifiers: Bayes and Fisher discriminant analysis. Support vector machines. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
14 Feature extraction and selection. Feature specification and extraction of ECG, EEG the EMG signals. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
15 Sample applications. Brain-computer interface. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım, Alıştırma ve Uygulama
16 Term Exams Textbook/course notes reading. Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Textbook/course notes reading. Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 7 4 28
Mid-term Exams (Written, Oral, etc.) 1 2 2
Final Exam 1 2 2
Total Workload (Hour) 144
Total Workload / 25 (h) 5,76
ECTS 6 ECTS

Update Time: 11.05.2023 11:19