Analysis of human locomotion using analytic wavelets applied to electromyographic data from healthy controls and Parkinson's patients

Human locomotion is controlled by the dynamic interaction between the human brain and spinal cord. An understanding of the basic control strategies and paradigms of normal locomotion could provide opportunity to understand and help characterise early stages of movement disorders such as Parkinson's disease. Locomotion is a stereotyped action and highly non-stationary which needs time-varying analyses. Analytic wavelets provide a powerful time-frequency analysis framework for studying time-varying properties of non-stationary neurophysiological signals, and are used in this thesis. A unified framework, which includes coherence, phase locking value (PLV), and time-resolved phase-amplitude coupling (tPAC) using generalized Morse wavelets, is developed to analyse electromyographic (EMG) recordings obtained from leg muscles during treadmill and overground walking in healthy controls and Parkinson's disease (PD) patients. A novel technique applied to coherence and PLV for removal of low frequency components due to EMG envelope modulation is then proposed. All measures have successfully been applied to three data sets, healthy human treadmill walking and human overground walking including a comparison of control subjects and PD patients. All measures provide a clear description of data from healthy treadmill locomotion. In comparison, more variability in time and frequency values are observed in analysis of overground walking data sets. The results provide new insights into the rhythmic control of locomotion in health and disease. Significant differences in features between healthy subjects and PD patients are observed in 12-20 Hz frequency range for all measures. The results from our novel technique for removal of low frequency EMG envelope modulation confirm the expectation for separating physiological mechanisms from effects due to low frequency envelope modulation of surface EMG during walking. Our results suggest that a combination of these measures could be suitable for investigating and characterising non-stationary neurophysiological data, and might be important for understanding the basic principles of human locomotion in health and disease.