Biomedical Signal Processing, Spring 2018 • MINDS

Jacobs University Bremen, Spring 2018, Fatemeh Hadaeghi

Course Description:
Biomedical signal processing (BSP) is about algorithms for processing a particular class of digital signals which are acquired in biomedical research and clinical medicine. Biomedical signals are recordings of physiological activities of organisms, ranging from gene and protein sequences, to neural and cardiac rhythms, to tissue and organ images. Electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG) and various sensory evoked potentials are a few examples of such bioelectric signals. In spite of slight differences in origins and data acquisition procedures, all recorded vital signals share common characteristic features (in different space and time scales) and suffer from common disturbances and artifacts. Biomedical signal processing aims at extracting significant information from such signals. A large number of processing algorithms have been particularly proposed to suppress disturbances in physiological recordings and facilitate diagnostic feature extraction. With the aid of biomedical signal processing, biologists and neuroscientists can develop hypotheses to explain physiological functions and physicians can monitor distinct states of malfunctions/disorders. This lecture briefly introduces bio-electrical phenomena, data acquisition procedures, filtering fundamentals, spectrum estimation and feature extraction. In addition, it provides a few examples of elementary linear and non-linear modeling formalisms. Although the topics covered by this lecture are specialized for processing of vital signals, the introduced method are generally described to be applicable in problems of other fields of science and technology.

Assignments:
There will be four series of assignments: 1- theoretical questions on electrophysiology and bioelectric phenomena, 2- theoretical problems on the basics of digital signal processing, 3- programming homework on EEG processing and 4- programming homework on ECG processing.

Exercise sheet 1
Exercise sheet 1: Solutions

Exercise sheet 2
Exercise sheet 2: Solutions

Exercise sheet 3, EEG data

Grading:
The final course grade will be determined as follows: 1- Class attendance (presence sheets) will count for 10% of the final grade, 2- three mini-quizzes which each will account to 10% of the final grade, 3- homework will count for 20% of the final grade and 4- a take home final exam will count for 40% of the final grade.

Quiz 1 (20.03.2018)

Quiz 2 (24.04.2018)

Quiz 3 (15.05.2018)

Evaluation Report

Topics:

Chapter 1: Introduction
Chapter 2: The origin of bioelectric signals
Chapter 3: Digital Signal Processing
Chapter 4: EEG- A Brief Introduction
Chapter 5: EEG Signal Processing
Chapter 6: ECG- A Brief Introduction
Chapter 7: ECG Signal Processing

Literature

Primary text:

self-contained lecture notes (will be uploaded weekly)
Course Content
Chapter 1: An Introduction to Biomedical Signal Processing
Chapter 2: The Origin of Bioelectric Signals
Chapter 3: Principles of Digital Signal Processing, Part 1
Chapter 3: Principles of Digital Signal Processing, Part 2
Chapter 3: Principles of Digital Signal Processing, Part 3
Chapter 4: EEG- A Brief Introduction (supplementary: Action potential, EEG recording)
Chapter 5: EEG- Signal Processing, Part 1
Chapter 5: EEG- Signal Processing, Part 2
Chapter 5: EEG- Signal Processing, Part 3

Required reading:

Alan, V. Oppenheim, W. Schafer Ronald, and R. B. John. “Discrete-time signal processing.” New Jersey, Printice Hall Inc (1989).
Cohen, Arnon. Biomedical Signal Processing: Time and frequency domains analysis, Volume I. CRC-Press, 1986.