The course touches the following topics:
• Introduction to main bioelectric signals (ECG, EMG, EEG), considerations on their genesis (action potential, spontaneous and induced signals, etc.), properties, classification, identification of the useful information and specific clinical and not-clinical applications.
• Introduction to speech signals, considerations on their genesis, analysis, synthesis and coding of the useful information and specific applications.
• Introduction to the analysis of human movement, considerations on the type of available data (coordinates, acceleration, angular velocity, etc.), analysis and synthesis of the useful information and applications specific for mHealth and fitness/wellness.
• Considerations on deterministic signals and specific application to biomedical signals (e.g. ECG signal filtering for noise reduction, filtering for base-line and power-line removal, threshold identification for critical ventricular arrhythmias, filtering DC components in the acceleration signal, pitch detection, etc.).
• Considerations on the concept of probability and processing of signals generated by stochastic processes and specific application to biomedical signals (e.g. separation of fetal and maternal ECG signal, identification of myocardial ischemia, classification of evoked potentials in the EEG, EMG signal classification addressing human-machine interfaces, etc.)
Lectures want to cover from a theoretical point of view all the topics, which are also treated during exercises/laboratories, providing the foundation needed to understand the genesis of the data, the processing methods and the synthesis of the information required by specific applications.