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Information
| Code | BMM020 |
| Name | Processing of Biopotential Signals |
| Term | 2023-2024 Academic Year |
| Semester | . Semester |
| 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 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 | ||