| Abstract: |  This diploma thesis deals with development of a suitable method for detection of tremor in patients with Parkinson's disease. The detection is based on recorded local field potentials of the Subthalamic nucleus (STN), captured through an electrode of a deep brain stimulation device.
The main part of this work is dedicated to research of various properties of the STN signal, compared on tremor and non-tremor recordings in different patients. Features based on temporal, spectral, statistical, fractal and autocorrelation-related discovered properties of the signals are developed and implemented. Evaluation and comparison
of the features is done on the available data during a classification process, using short sections of preprocessed signals. The results show great importance of spectral properties of the signal, which corresponds with previous research in this field. In particular, the frequency bands 0-1 and 3.5-5.5 Hz proved great significance to the problem, the latter corresponding with the tremor frequency found in the simultaneous EMG recordings. Classification based on multiple features showed very good results for 3 out of 5 patients, reaching up to 94% sensitivity with specificity equalling one. On the contrary, the method did not work with the two remaining patients, possible reason being substantial lack of training data.
|
|---|