Dynamic SVM detection of tremor and dyskinesia during unscripted and unconstrained activities

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

A novel method for assessing the severity of levodopa-induced dyskinesia using wearable sensors

Patients with Parkinson's disease often experience significant changes in the severity of dyskinesia when they undergo titration of their medications. Dyskinesia is marked by involuntary jerking movements that occur randomly in a burst-like fashion. The burst-like nature of such movements makes it difficult to estimate the clinical scores of severity of dyskinesia using wearable sensors. Clinical observations are generally made over intervals of 15-30 s. On the other hand, techniques designed to estimate the severity of dyskinesia based on the analysis of wearable sensor data typically use data segments of approximately 5 s. Consequently, some data segments might include dyskinetic movements, whereas others might not. Herein, we propose a novel method suitable to automatically select data segments from the training dataset that are marked by dyskinetic movements. The proposed method also aggregates results derived from the testing dataset using a machine learning algorithm to estimate the severity of dyskinesia from wearable sensor data. Results obtained from the analysis of sensor data collected from seven subjects with Parkinson's disease showed a marked improvement in the accuracy of the estimation of clinical scores of dyskinesia.

Levodopa-Induced Changes in Electromyographic Patterns in Patients with Advanced Parkinson's Disease

Levodopa medication is the most efficient treatment for motor symptoms of Parkinson's disease (PD). Levodopa significantly alleviates rigidity, rest tremor, and bradykinesia in PD. The severity of motor symptoms can be graded with UPDRS-III scale. Levodopa challenge test is routinely used to assess patients' eligibility to deep-brain stimulation (DBS) in PD. Feasible and objective measurements to assess motor symptoms of PD during levodopa challenge test would be helpful in unifying the treatment. Twelve patients with advanced PD who were candidates for DBS treatment were recruited to the study. Measurements were done in four phases before and after levodopa challenge test. Rest tremor and rigidity were evaluated using UPDRS-III score. Electromyographic (EMG) signals from biceps brachii and kinematic signals from forearm were recorded with wireless measurement setup. The patients performed two different tasks: arm isometric tension and arm passive flexion-extension. The electromyographic and the kinematic signals were analyzed with parametric, principal component, and spectrum-based approaches. The principal component approach for isometric tension EMG signals showed significant decline in characteristics related to PD during levodopa challenge test. The spectral approach on passive flexion-extension EMG signals showed a significant decrease on involuntary muscle activity during the levodopa challenge test. Both effects were stronger during the levodopa challenge test compared to that of patients' personal medication. There were no significant changes in the parametric approach for EMG and kinematic signals during the measurement. The results show that a wireless and wearable measurement and analysis can be used to study the effect of levodopa medication in advanced Parkinson's disease.