Wearable sensors and advanced algorithms can provide significant decision support for clinical practice. Currently, the motor symptoms of patients with neurological disorders are often visually observed and evaluated, which may result in rough and subjective quantification. Using small inertial wearable sensors, fine repetitive and clinically important movements can be captured and objectively evaluated. In this paper, a new methodology is designed for objective evaluation and automatic scoring of bradykinesia in repetitive finger-tapping movements for patients with idiopathic Parkinson’s disease and atypical parkinsonism. The methodology comprises several simple and repeatable signal-processing techniques that are applied for the extraction of important movement features. The decision support system consists of simple rules designed to match universally defined criteria that are evaluated in clinical practice. The accuracy of the system is calculated based on the reference scores provided by two neurologists. The proposed expert system achieved an accuracy of 88.16% for files on which neurologists agreed with their scores. The introduced system is simple, repeatable, easy to implement, and can provide good assistance in clinical practice, providing a detailed analysis of finger-tapping performance and decision support for symptom evaluation.
The goal of this study was to analyze how depression associated with Parkinson's disease (PD) affected gait variability in these patients using a dual-task paradigm. Additionally, the dependency of the executive functions and the impact of depression on gait variability were analyzed. Patients and Methods: Three subject groups were included: patients with PD, but no depression (PD-NonDep; 14 patients), patients with both PD and depression (PD-Dep; 16 patients) and healthy controls (HC; 15 subjects). Gait was recorded using the wireless sensors. The participants walked under four conditions: single-task, motor dual-task, cognitive dual-task, and combined dual-task. Variability of stride length, stride duration, and swing time was calculated and analyzed using the statistical methods. Results: Variability of stride duration and stride length were not significantly different between PD-Dep and PD-NonDep patients. The linear mixed model showed that swing time variability was statistically significantly higher in PD-Dep patients compared to controls (p = 0.001). Hamilton Disease Rating Scale scores were significantly correlated with the swing time variability (p = 0.01). Variability of all three parameters of gait was significantly higher while performing combined or cognitive task and this effect was more pronounced in PD-Dep group of patients. Conclusions: Depression in PD was associated with swing time variability, and this effect was more prominent while performing a dual-task. Significance: Diagnosing and treating depression might be important for gait improvement and fall reduction in PD patients.
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