Real-time detection of FOG episodes in patients with Parkinson's Disease

Parkinson’s Disease is a progressive neurodegenerative disorder that affects 1% of the worldwide population over the age of 65. Being a disease without a known cure, the pharmacological treatment tries to mitigate the effects of associated symptoms. The most common and disturbing ones belong to the motor domain. A typical impairment, in the advanced stages, is the Freezing of Gait. It represents a sudden blocking of movement described by the patients as their feet are stuck to the ground. Hence, this is the main cause of falls and significantly affects patients quality of life. Currently symptoms evaluation is performed through the application of questionnaires and rating scales, which usually lead to erroneous assessment of the patient state and disease evolution. Objective quantification and monitoring of freezing episodes, through the use of wearable technologies, would have a key role, with advantages for both patients and clinicians. Combining freezing events monitoring to auditory stimulation strategies, one is able to build a system capable of helping clinicians to manage disease treatments and, on the other side, of helping patients to deal and overcome these events. Having this is mind, a methodology for monitoring and detecting Freezing of Gait episodes was developed and is presented in this dissertation. It is a supervised machine learning algorithm, where acceleration signals acquired from inertial sensors placed in the patient’s ankle are used as the input of a supervised machine learning classifier Support Vector Machines. Due to the patient dependent nature of the freezing events, a personalized methodology is proposed. In addition to only using data from the concerned patient, the patient-dependent approach includes the segmentation of the signal based on the gait cycle of each individual and also the extraction of similarity measures between a gait template and portions of the recorded acceleration signal, through the calculation of the Dynamic Time Warping. Frequency-based features are also proposed. The methodology was evaluated with acceleration data of 8 patients from the DAPHNet dataset, achieving a specificity of 82,0%, a sensitivity of 85,1% and a F1-score of 58,9%. The proposed features were compared with a set of features proposed in the literature and the performances were very similar, with an improvement in the results when the two sets are used together. Additionally, a preliminary analysis towards prediction of freezing events before their beginning was also performed by comparing the behavior of the proposed features during normal gait and prior to the freezing episodes. The results suggest that the pre freezing phase is also highly patient dependent and that there are features able to capture gait degradation prior to Freezing of Gait. The main limitation of the results is the high number of false positives detected. However, the results were promising and suggest that the proposed methodology can be used to detect freezing events in patients with Parkinson’s Disease. Further developments to this project are required to better validate and also improve the results.