Upper limb motor pre-clinical assessment in Parkinson's disease using machine learning

Abstract Introduction Parkinson's disease (PD) is a common neurodegenerative disorder characterized by disabling motor and non-motor symptoms. For example, idiopathic hyposmia (IH), which is a reduced olfactory sensitivity, is typical in >95% of PD patients and is a preclinical marker for the pathology. Methods In this work, a wearable inertial device, named SensHand V1, was used to acquire motion data from the upper limbs during the performance of six tasks selected by MDS-UPDRS III. Three groups of people were enrolled, including 30 healthy subjects, 30 IH people, and 30 PD patients. Forty-eight parameters per side were computed by spatiotemporal and frequency data analysis. A feature array was selected as the most significant to discriminate among the different classes both in two-group and three-group classification. Multiple analyses were performed comparing three supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes, on three different datasets. Results Excellent results were obtained for healthy vs. patients classification (F-Measure 0.95 for RF and 0.97 for SVM), and good results were achieved by including subjects with hyposmia as a separate group (0.79 accuracy, 0.80 precision with RF) within a three-group classification. Overall, RF classifiers were the best approach for this application. Conclusion The system is suitable to support an objective PD diagnosis. Further, combining motion analysis with a validated olfactory screening test, a two-step non-invasive, low-cost procedure can be defined to appropriately analyze people at risk for PD development, helping clinicians to identify also subtle changes in motor performance that characterize PD onset

Biomechanical parameter assessment for classification of Parkinson's disease on clinical scale

The primary goal of this study was to investigate computerized assessment methods to classify motor dysfunctioning of patients with Parkinsonâ\u80\u99s disease on the clinical scale. In this proposed system, machine learningâ\u80\u93based computerized assessment methods were introduced to assess the motor performance of patients with Parkinsonâ\u80\u99s disease. Biomechanical parameters were acquired from six exercises through wearable inertial sensors: SensFoot V2 and SensHand V1. All patients were evaluated via neurologist by means of the clinical scale. The average rating was calculated from all exercise ratings given by clinicians to estimate overall rating for each patient. Patients were divided in two groups: slightâ\u80\u93mild patients with Parkinsonâ\u80\u99s disease and moderateâ\u80\u93severe patients with Parkinsonâ\u80\u99s disease according to average rating (â\u80\u9c0: slight and mildâ\u80\u9d and â\u80\u9c1: moderate and severeâ\u80\u9d). Feature selection methods were used for the selection of significant features. Selected features were trained in support vector machine, logistic regression, and neural network to classify the two groups of patients. The highest classification accuracy obtained by support vector machine classifier was 79.66%, with 0.8790 area under the curve. A 76.2% classification accuracy was obtained with 0.7832 area under the curve through logistic regression. A 83.10% classification accuracy was obtained by neural network classifier, with 0.889 area under the curve. Strong distinguishability of the models between the two groups directs the high possibility of motor impairment classification through biomechanical parameters in patients with Parkinsonâ\u80\u99s disease based on the clinical scale

Hands-feet wireless devices: Test-retest reliability and discriminant validity of motor measures in Parkinson's disease telemonitoring

Background Telemonitoring, a branch of telemedicine, involves the use of technological tools to remotely detect clinical data and evaluate patients. Telemonitoring of patients with Parkinson's disease (PD) should be performed using reliable and discriminant motor measures. Furthermore, the method of data collection and transmission, and the type of subjects suitable for telemonitoring must be well defined. Objective To analyze differences in patients with PD and healthy controls (HC) with the wearable inertial device SensHands-SensFeet (SH-SF), adopting a standardized acquisition mode, to verify if motor measures provided by SH-SF have a high discriminating capacity and high intraclass correlation coefficient (ICC). Methods Altogether, 64 patients with mild-to-moderate PD and 50 HC performed 14 standardized motor activities for assessing bradykinesia, postural and resting tremors, and gait parameters. SH-SF inertial devices were used to acquire movements and calculate objective motor measures of movement (total: 75). For each motor task, five or more biomechanical parameters were measured twice. The results were compared between patients with PD and HC. Results Fifty-eight objective motor measures significantly differed between patients with PD and HC; among these, 32 demonstrated relevant discrimination power (Cohen's d > 0.8). The test-retest reliability was excellent in patients with PD (median ICC = 0.85 right limbs, 0.91 left limbs) and HC (median ICC = 0.78 right limbs, 0.82 left limbs). Conclusion In a supervised environment, the SH-SF device provides motor measures with good results in terms of reliability and discriminant ability. The reliability of SH-SF measurements should be evaluated in an unsupervised home setting in future studies

Item Reduction, Psychometric and Biometric Properties of the Italian Version of the Body Perception Questionnaire-Short Form (BPQ-SF): The BPQ-22

Body awareness disorders and reactivity are mentioned across a range of clinical problems. Constitutional differences in the control of the bodily state are thought to generate a vulnerability to psychological symptoms. Autonomic nervous system dysfunctions have been associated with anxiety, depression, and post-traumatic stress. Though interoception may be a transdiagnostic mechanism promoting the improvement of clinical symptomatology, few psychometrically sound, symptom-independent, self-report measures, informed by brain-body circuits, are available for research and clinical use. We validated the Italian version of the body perception questionnaire (BPQ)-short form and found that response categories could be collapsed from five to three and that the questionnaire retained a three-factor structure with items reduced from 46 to 22 (BPQ-22). The first factor was loaded by body awareness items; the second factor comprised some items from the body awareness scale and some from the subdiaphragmatic reactivity scale (but all related to bloating and digestive issues), and the third factor by supradiaphragmatic reactivity items. The BPQ-22 had sound psychometric properties, good convergent and discriminant validity and test-retest reliability and could be used in clinical and research settings in which the body perception assessment is of interest. Psychometric findings in light of the polyvagal theory are discussed

How wearable sensors can support parkinson's disease diagnosis and treatment: A systematic review

Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. Objectives: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). Data sources: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Study eligibility criteria: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Results: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease

Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review

Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy

Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches

Millions of people worldwide are affected by Parkinson’s disease (PD), which significantly worsens their quality of life. Currently, the diagnosis is based on assessment of motor symptoms, but interest toward non-motor symptoms is increasing, as well. Among them, idiopathic hyposmia (IH) is associated with an increased risk of developing PD in healthy adults. In this work, a wearable inertial device, named SensFoot V2, was used to acquire motor data from 30 healthy subjects, 30 people with IH, and 30 PD patients while performing tasks from the MDS-UPDRS III for lower limb assessment. The most significant and non-correlated extracted parameters were selected in a feature array that can identify differences between the three groups of people. A comparative classification analysis was performed by applying three supervised machine learning algorithms. The system resulted able to distinguish between healthy and patients (specificity and recall equal to 0.967), and the people with IH can be identified as a separate class within a three-group classification (accuracy equal to 0.78). Thus, the system could support the clinician in objective assessment of PD. Further, identification of IH together with changes in motor parameters could be a non-invasive two-step approach to investigate the early onset of PD

Item reduction, psychometric and biometric properties of the italian version of the body perception questionnaire—short form (Bpq-sf): The bpq-22

Body awareness disorders and reactivity are mentioned across a range of clinical prob-lems. Constitutional differences in the control of the bodily state are thought to generate a vulnerability to psychological symptoms. Autonomic nervous system dysfunctions have been associated with anxiety, depression, and post-traumatic stress. Though interoception may be a transdiagnostic mechanism promoting the improvement of clinical symptomatology, few psychometrically sound, symptom-independent, self-report measures, informed by brain–body circuits, are available for research and clinical use. We validated the Italian version of the body perception questionnaire (BPQ)—short form and found that response categories could be collapsed from five to three and that the questionnaire retained a three-factor structure with items reduced from 46 to 22 (BPQ-22). The first factor was loaded by body awareness items; the second factor comprised some items from the body awareness scale and some from the subdiaphragmatic reactivity scale (but all related to bloating and digestive issues), and the third factor by supradiaphragmatic reactivity items. The BPQ-22 had sound psychometric properties, good convergent and discriminant validity and test– retest reliability and could be used in clinical and research settings in which the body perception assessment is of interest. Psychometric findings in light of the polyvagal theory are discussed