A study of gaits in Parkinson's patients using autoregressive model

Background: Parkinson's disease (PD) is a degenerative disease of the brain that often do harm to the motor skills of patients. A disturbed gait is very common among PD patients, who are easy to fall down and may lose their functional independence. Purpose: In this paper, we extract features from the gait signals in PD patients and control subjects for the purpose of comparison and realization of automatic recognition in computer. Also, we want to extract the pattern of walking in PD and help explain the disturbed gait through the perspective of bioinformatics. Method: In this paper, autoregressive (AR) model is applied to analysis of foot-pressure based on Yule-Walker equation for the calculation of power spectra. In the comparison process, we also give our own definition to Associated Discrete Index (ADI) in order to measure the discrete degree at a same frequency. Results: We analyze the data from three research groups: Ga, Ju and Si. The database includes 18, 25, 29 control subjects and 29, 29, 35 PD patients respectively. In the framework of 31-50 Hz in the power spectra of Ga; 7-12 Hz, 25-38 Hz, 43-50 Hz in Ju; and 2-10 Hz in Si, we can see obvious difference between PD patient and control subject through t-test. Conclusions: The AR model can be used as a novel and useful tool to study the time series of foot-pressure. Associated Discrete Index could be an indicator in classification the gait signal in PD patient.