Biomedical signals analysis and neural modeling of motor coordination in Parkinson’s disease (PhD project)
The aim is to propose a plausible model of a part of the neuronal circuits involved in motor coordination in humans and which are the site of deficiencies observed in Parkinson’s disease. The model will take into account structures that are involved in automatic movements, such as central pattern generators, their links to higher-level structures affected by the disease (basal ganglia, dopaminergic neurons) and known mechanisms associated with the disease.
The ultimate goal is to reproduce the anomalies observed in the motor coordination of the patients, particularly episodes of sudden inhibition of the rhythmic movements of the lower limbs during walking or upper limbs during oscillations of the wrist or fingers. The model will be constructed from biological signals (cerebral, muscular, motor) and will be validated by simulating the movements of the limbs considered from a usual musculoskeletal model.
Analysis of structure of biomedical signals in diagnostic systems of patients (Master project)
The aim of the work is improving the quality of patients’ diagnoses by developing tools for analyzing biomedical signals and characteristics for diagnostic systems based on the implementation of approaches for pattern recognition in multidimensional vector spaces.
In Chapter 1, the goals and objectives of structure analyzing of biomedical signals and characteristics in patients diagnostics are discussed. Section 2 presents the development and research of pattern recognition procedures of biomedical signals and characteristics with their mutually orthogonal decomposition. In Chapter 3, the option of forming standards in signal recognition systems based on their decomposition on periodic and orthogonal to them components of not given in advance shape is presented. In Chapter 4, with specific example of the QRS-complexes types’ recognition of patient’s electrocardiogram, the content of the procedures for obtaining waveforms characteristics is revealed in more detail. In Chapter 5, the studies on feature selection in various ways are carried out.