Biomedical Signal Processing

Aim

The course gives in-depth knowledge about bioelectricity and analysis of bioelectrical signals in humans. The analysis is related to differentiating between healthy and pathological conditions in relevant clinical situations. After completing the course, the students should be able to independently:

• Describe and explain physical, electrical and mathematical models for the origin of bioelectrical signals in the cell, and how bioelectrical signals are conducted in nerves and muscle tissue.
• Describe the bioelectrical functions of the heart and of the central and peripheral nervous systems.
• Describe and use the most important bioelectrical measurement methods, ECG, EEG, and EMG, and explain their relations to normal and pathological conditions.
• Describe and implement different methods for signal processing of the ECG, the EEG, and the EMG, using both time and frequency domain analysis.

Content

Signal analysis: time and frequency, sampling, digital signals, Fouriertransform, power spectrum, spectrogram, sampling windows, leakage, aliasing, autocorrelation, noise reduction with digital filtering and ensemble averaging

Physiological and mathematical models of bioelectricity: cell membrane physiology, resting and action potentials, Nernst equation, Goldman-Katz equation, the Hodgkin-Huxley model Measurement of bioelectrical signals: Measurement systemsElectrocardiography (ECG): origin of the ECG, ECG leads, ECG analysis methods and diagnosisNeurophysiology: function of nervous system, muscle control, electroencephalography (EEG), evoked potentials (EP), electromyography (EMG), electrooculography (EOG), signal analysis and diagnosis