Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning.

Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.

Therapeutic maps for a sensor-based evaluation of deep brain stimulation programming.

Programming in deep brain stimulation (DBS) is a labour-intensive process for treating advanced motor symptoms. Specifically for patients with medication-refractory tremor in multiple sclerosis (MS). Wearable sensors are able to detect some manifestations of pathological signs, such as intention tremor in MS. However, methods are needed to visualise the response of tremor to DBS parameter changes in a clinical setting while patients perform the motor task finger-to-nose. To this end, we attended DBS programming sessions of a MS patient and intention tremor was effectively quantified by acceleration amplitude and frequency. A new method is introduced which results in the generation of therapeutic maps for a systematic review of the programming procedure in DBS. The maps visualise the combination of tremor acceleration power, clinical rating scores, total electrical energy delivered to the brain and possible side effects. Therapeutic maps have not yet been employed and could lead to a certain degree of standardisation for more objective decisions about DBS settings. The maps provide a base for future research on visualisation tools to assist physicians who frequently encounter patients for DBS therapy.