Quick computer aided differential diagnostics based on repetitive finger tapping in Parkinson's disease and atypical parkinsonisms.

Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder whose prevalence rises with age, yet clinical diagnosis is still a challenging task due to similar manifestations of other neurodegenerative movement disorders. In untreated patients or those with unclear responses to medication, correct percentages of early diagnoses go as low as 26%. Technology has been used in various forms to facilitate discerning between persons with PD and healthy individuals, but much less work has been dedicated to separating PD and atypical parkinsonisms. Methods: A wearable system was developed based on inertial sensors that capture the movements of fingers during repetitive finger tapping. A k-nearest-neighbor classifier was used on features extracted from gyroscope recordings for quick aid in differential diagnostics, discerning patients with PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and healthy controls (HC). Results: The overall classification accuracy achieved was 85.18% in the multiclass setup. MSA and HC groups were the easiest to discern (100%), while PSP was the most elusive diagnosis, as some patients were incorrectly assigned to MSA and HC groups. Conclusions: The system shows potential for use as a tool for quick diagnostic aid, and in the era of big data, offers a means of standardization of data collection that could allow scientists to aggregate multi-center data for further research.

Impact of depression on gait variability in Parkinson's disease.

OBJECTIVE: 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.

Artificial intelligence for assisting diagnostics and assessment of Parkinson's disease-A review.

Artificial intelligence, specifically machine learning, has found numerous applications in computer-aided diagnostics, monitoring and management of neurodegenerative movement disorders of parkinsonian type. These tasks are not trivial due to high inter-subject variability and similarity of clinical presentations of different neurodegenerative disorders in the early stages. This paper aims to give a comprehensive, high-level overview of applications of artificial intelligence through machine learning algorithms in kinematic analysis of movement disorders, specifically Parkinson's disease (PD). We surveyed papers published between January 2007 and January 2019, within online databases, including PubMed and Science Direct, with a focus on the most recently published studies. The search encompassed papers dealing with the implementation of machine learning algorithms for diagnosis and assessment of PD using data describing motion of upper and lower extremities. This systematic review presents an overview of 48 relevant studies published in the abovementioned period, which investigate the use of artificial intelligence for diagnostics, therapy assessment and progress prediction in PD based on body kinematics. Different machine learning algorithms showed promising results, particularly for early PD diagnostics. The investigated publications demonstrated the potentials of collecting data from affordable and globally available devices. However, to fully exploit artificial intelligence technologies in the future, more widespread collaboration is advised among medical institutions, clinicians and researchers, to facilitate aligning of data collection protocols, sharing and merging of data sets.

An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors.

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.

Challenges of Stride Segmentation and Their Implementation for Impaired Gait.

Stride segmentation represents important but challenging part of the gait analysis. Different methods and sensor systems have been proposed for detection of markers for segmentation of gait sequences. This task is often performed with wearable sensors comprising force sensors and/or inertial sensors. In this paper, we have compared four different methods for stride segmentation based on signals collected from force sensing resistors, accelerometers and gyro sensors. The results were evaluated on 15 healthy and 15 patients with Parkinson's disease, and expressed in terms of number of imprecisely, missed or wrongly detected gait events, as well as temporal absolute error. It was established that the methods using the inertial data, provide results with up to 12% higher error rate comparing to detection from force sensing resistors.