EE652 Biomedical Signal Processing

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

Information

Code EE652
Name Biomedical Signal Processing
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Yüksek Lisans Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. SAMİ ARICA


Course Goal

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 Discuss the selection of signal processing techniques for real biomedical signals.
LO03 Evaluate ettects of different biomedical signal processing approaches using Matlab.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Being able to specialize in at least one of the branches that form the foundations of electrical-electronic engineering by increasing the level of knowledge beyond the undergraduate level. 5
PLO02 Bilgi - Kuramsal, Olgusal To comprehend the integrity of all the subjects included in the field of specialization. 5
PLO03 Bilgi - Kuramsal, Olgusal Knowing and following the current scientific literature in the field of specialization 5
PLO04 Bilgi - Kuramsal, Olgusal To be able to comprehend the interdisciplinary interaction of the field with other related branches. 5
PLO05 Bilgi - Kuramsal, Olgusal Ability to do theoretical and experimental work 4
PLO06 Bilgi - Kuramsal, Olgusal To create a complete scientific text by compiling the information obtained from the research. 4
PLO07 Bilgi - Kuramsal, Olgusal To work on the thesis topic programmatically, following the logical integrity required by the subject within the framework determined by the advisor. 4
PLO08 Bilgi - Kuramsal, Olgusal To search for literature in scientific databases, particularly the ability to correctly and accurately scan databases and evaluate and categorize listed items. 4
PLO09 Bilgi - Kuramsal, Olgusal Knowledge of English at a level that can easily read and understand a scientific text written in English in the field of specialization 3
PLO10 Bilgi - Kuramsal, Olgusal Compile information on his/her expertise in a presentation format and present it understandably and effectively. 3
PLO11 Bilgi - Kuramsal, Olgusal Ability to write a computer program in a familiar programming language, generally for a specific purpose, specifically related to the field of expertise. 4
PLO12 Bilgi - Kuramsal, Olgusal Being able to guide and take the initiative in environments that require solving problems related to the field 3
PLO13 Yetkinlikler - İletişim ve Sosyal Yetkinlik Ability to communicate with people in an appropriate language 2
PLO14 Yetkinlikler - Öğrenme Yetkinliği To be able to produce projects, policies, and processes in the field of expertise and to evaluate these elements 2
PLO15 Yetkinlikler - Öğrenme Yetkinliği Ability to research new topics based on existing research experience 4


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
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
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
5 Basic signal processing methods. Signal space methods. Frequency estimation. Textbook reading/Computer application. Öğretim Yöntemleri:
Anlatım
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. Öğretim Yöntemleri:
Problem Çözme
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