scholarly journals Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2047
Author(s):  
James R. Williamson ◽  
Brian Telfer ◽  
Riley Mullany ◽  
Karl E. Friedl
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Data Collection ◽  
Symptom Severity ◽  
Low Cost ◽  
Area Under The Curve ◽  
Primary Diagnosis ◽  
Data Set ◽  
Movement Data ◽  
The One

Parkinson’s disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.

scholarly journals Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Gloria Vergara-Diaz ◽  
Jean-Francois Daneault ◽  
Federico Parisi ◽  
Chen Admati ◽  
Christina Alfonso ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Wearable Sensors ◽  
Motor Fluctuations ◽  
Sensor Data ◽  
Accelerometer Data ◽  
Motor Tasks ◽  
Specific Symptom ◽  
At Home

AbstractParkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and non-motor symptoms. Dyskinesia and motor fluctuations are complications of PD medications. An objective measure of on/off time with/without dyskinesia has been sought for some time because it would facilitate the titration of medications. The objective of the dataset herein presented is to assess if wearable sensor data can be used to generate accurate estimates of limb-specific symptom severity. Nineteen subjects with PD experiencing motor fluctuations were asked to wear a total of five wearable sensors on both forearms and shanks, as well as on the lower back. Accelerometer data was collected for four days, including two laboratory visits lasting 3 to 4 hours each while the remainder of the time was spent at home and in the community. During the laboratory visits, subjects performed a battery of motor tasks while clinicians rated limb-specific symptom severity. At home, subjects were instructed to use a smartphone app that guided the periodic performance of a set of motor tasks.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Smartphones for Remote Symptom Monitoring of Parkinson’s Disease

2021 ◽  
pp. 1-5
Author(s):  
Max A. Little
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Symptom Severity ◽  
Computational Analysis ◽  
Computational Algorithms ◽  
Objective Data ◽  
Future Prospects ◽  
Symptom Monitoring ◽  
Sensor Devices ◽  
Symptoms And Signs

Parkinson’s disease is a complex and heterogeneous condition, and there are many gaps in the medical community’s scientific and practical understanding of the disease. Closing these gaps relies on objective data about symptoms and signs, collected over long durations. Smartphones contain sensor devices which can be used to remotely capture behavioral signals. From these signals, computational algorithms can distill metrics of symptom severity and progression. This brief review introduces the main concepts of the discipline, addressing the experimental, hardware and software logistics, and computational analysis. The article finishes with an exploration of future prospects for the technology.

scholarly journals Mucuna pruriens for Parkinson's disease: Low-cost preparation method, laboratory measures and pharmacokinetics profile

2016 ◽  
Vol 365 ◽  
pp. 175-180 ◽  
Author(s):  
Erica Cassani ◽  
Roberto Cilia ◽  
Janeth Laguna ◽  
Michela Barichella ◽  
Manuela Contin ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Preparation Method ◽  
Low Cost ◽  
Mucuna Pruriens ◽  
Laboratory Measures

Assessment and Rating of Movement Impairment in Parkinson’s Disease Using a Low-Cost Vision-Based System

2018 ◽  
pp. 777-788 ◽  
Author(s):  
Domenico Buongiorno ◽  
Gianpaolo Francesco Trotta ◽  
Ilaria Bortone ◽  
Nicola Di Gioia ◽  
Felice Avitto ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Low Cost

scholarly journals Stability of MDS-UPDRS Motor Subtypes Over Three Years in Early Parkinson's Disease

2021 ◽  
Vol 12 ◽  
Author(s):  
Abhijeet K. Kohat ◽  
Samuel Y. E. Ng ◽  
Aidan S. Y. Wong ◽  
Nicole S. Y. Chia ◽  
Xinyi Choi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Area Under The Curve ◽  
Baseline Factors ◽  
Prospective Cohorts ◽  
The Mean ◽  
The Stability ◽  
Early Parkinson’S Disease ◽  
Levodopa Equivalent Daily Dose

Background: Various classifications have been proposed to subtype Parkinson's disease (PD) based on their motor phenotypes. However, the stability of these subtypes has not been properly evaluated.Objective: The goal of this study was to understand the distribution of PD motor subtypes, their stability over time, and baseline factors that predicted subtype stability.Methods: Participants (n = 170) from two prospective cohorts were included: the Early PD Longitudinal Singapore (PALS) study and the National Neuroscience Institute Movement Disorders Database. Early PD patients were classified into tremor-dominant (TD), postural instability and gait difficulty (PIGD), and indeterminate subtypes according to the Movement Disorder Society's Unified PD Rating Scale (MDS-UPDRS) criteria and clinically evaluated for three consecutive years.Results: At baseline, 60.6% patients were TD, 12.4% patients were indeterminate, and 27.1% patients were PIGD subtypes (p < 0.05). After 3 years, only 62% of patients in TD and 50% of patients in PIGD subtypes remained stable. The mean levodopa equivalent daily dose (LEDD) was higher in the PIGD subtype (276.92 ± 232.91 mg; p = 0.01). Lower LEDD [p < 0.05, odds ratio (OR) 0.99, 95% confidence interval (CI): 0.98–0.99] and higher TD/PIGD ratios (p < 0.05, OR 1.77, 95% CI: 1.29–2.43) were independent predictors of stability of TD subtype with an area under the curve (AUC) of 0.787 (95%CI: 0.669–0.876), sensitivity = 57.8%, and specificity = 89.7%.Conclusion: Only 50–62% of PD motor subtypes as defined by MDS-UPDRS remained stable over 3 years. TD/PIGD ratio and baseline LEDD were independent predictors for TD subtype stability over 3 years.

scholarly journals Laboratory versus daily life gait characteristics in patients with multiple sclerosis, Parkinson’s disease, and matched controls

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Vrutangkumar V. Shah ◽  
James McNames ◽  
Martina Mancini ◽  
Patricia Carlson-Kuhta ◽  
Rebecca I. Spain ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurological Disease ◽  
Inertial Sensors ◽  
Daily Life ◽  
Area Under The Curve ◽  
Gait Characteristics ◽  
Discrete Moment ◽  
Healthy Control

Abstract Background and purpose  Recent findings suggest that a gait assessment at a discrete moment in a clinic or laboratory setting may not reflect functional, everyday mobility. As a step towards better understanding gait during daily life in neurological populations, we compared gait measures that best discriminated people with multiple sclerosis (MS) and people with Parkinson’s Disease (PD) from their respective, age-matched, healthy control subjects (MS-Ctl, PD-Ctl) in laboratory tests versus a week of daily life monitoring. Methods  We recruited 15 people with MS (age mean ± SD: 49 ± 10 years), 16 MS-Ctl (45 ± 11 years), 16 people with idiopathic PD (71 ± 5 years), and 15 PD-Ctl (69 ± 7 years). Subjects wore 3 inertial sensors (one each foot and lower back) in the laboratory followed by 7 days during daily life. Mann–Whitney U test and area under the curve (AUC) compared differences between PD and PD-Ctl, and between MS and MS-Ctl in the laboratory and in daily life. Results  Participants wore sensors for 60–68 h in daily life. Measures that best discriminated gait characteristics in people with MS and PD from their respective control groups were different between the laboratory gait test and a week of daily life. Specifically, the toe-off angle best discriminated MS versus MS-Ctl in the laboratory (AUC [95% CI] = 0.80 [0.63–0.96]) whereas gait speed in daily life (AUC = 0.84 [0.69–1.00]). In contrast, the lumbar coronal range of motion best discriminated PD versus PD-Ctl in the laboratory (AUC = 0.78 [0.59–0.96]) whereas foot-strike angle in daily life (AUC = 0.84 [0.70–0.98]). AUCs were larger in daily life compared to the laboratory. Conclusions Larger AUC for daily life gait measures compared to the laboratory gait measures suggest that daily life monitoring may be more sensitive to impairments from neurological disease, but each neurological disease may require different gait outcome measures.

scholarly journals Alpha-Synuclein Protofibrils in Cerebrospinal Fluid: A Potential Biomarker for Parkinson's Disease

2020 ◽  
Vol 10 (4) ◽  
pp. 1429-1442
Author(s):  
Marianne von Euler Chelpin ◽  
Linda Söderberg ◽  
Johanna Fälting ◽  
Christer Möller ◽  
Marco Giorgetti ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Cerebrospinal Fluid ◽  
Single Molecule ◽  
Area Under The Curve ◽  
Corticobasal Degeneration ◽  
Alpha Synuclein ◽  
Cross Reactivity ◽  
Potential Biomarker ◽  
Csf Levels

Background: Currently, there is no established biomarker for Parkinson's disease (PD) and easily accessible biomarkers are crucial for developing disease-modifying treatments. Objective: To develop a novel method to quantify cerebrospinal fluid (CSF) levels of α-synuclein protofibrils (α-syn PF) and apply it to clinical cohorts of patients with PD and atypical parkinsonian disorders. Methods: A cohort composed of 49 patients with PD, 12 with corticobasal degeneration (CBD), 22 with progressive supranuclear palsy, and 33 controls, that visited the memory clinic but had no biomarker signs of Alzheimer’s disease (AD, tau<350 pg/mL, amyloid-beta 42 (Aβ42)>530 pg/mL, and phosphorylated tau (p-tau)<60 pg/mL) was used in this study. The CSF samples were analyzed with the Single molecule array (Simoa) technology. Total α-synuclein (α-syn) levels were analyzed with a commercial ELISA-kit. Results: The assay is specific to α-syn PF, with no cross-reactivity to monomeric α-syn, or the β- and γ-synuclein variants. CSF α-syn PF levels were increased in PD compared with controls (62.1 and 40.4 pg/mL, respectively, p = 0.03), and CBD (62.1 and 34.2 pg/mL, respectively, p = 0.02). The accuracy of predicting PD using α-syn PF is significantly different from controls (area under the curve 0.68, p = 0.0097) with a sensitivity of 62.8% and specificity of 67.7%. Levels of total α-syn were significantly different between the PD and CBD groups (p = 0.04). Conclusion: The developed method specifically quantifies α-syn PF in human CSF with increased concentrations in PD, but with an overlap with asymptomatic elderly controls.

scholarly journals Synergy of pandemics-social isolation is associated with worsened Parkinson severity and quality of life

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Indu Subramanian ◽  
Joshua Farahnik ◽  
Laurie K. Mischley
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Social Isolation ◽  
Support Groups ◽  
Symptom Severity ◽  
Social Performance ◽  
Social Distancing ◽  
Patient Reported

Abstract Social isolation and its deleterious effects on health increases with age in the general population. People with Parkinson’s Disease (PWP) are no exception. Social isolation is a risk factor for worsened health outcomes and increased mortality. Symptoms such as depression and sleep dysfunction are adversely affected by loneliness. There is a paucity of research on social isolation in Parkinson’s disease (PD), which is all the more critical now in the setting of social distancing due to COVID-19. The goal of this study was to survey individuals with PD to evaluate whether social isolation is associated with PD symptom severity and quality of life. Only individuals reporting a diagnosis of idiopathic PD were included in this analysis. The primary outcome measures were the Patient-Reported Outcomes in PD (PRO-PD) and questions from PROMIS Global related to social health. PRO-PD scores increased as social performance and social satisfaction scores diminished. Individuals who reported being lonely experienced a 55% greater symptom severity than those who were not lonely (P < 0.01). Individuals who documented having a lot of friends had 21% fewer symptoms than those with few or no friends (P < 0.01). Social isolation was associated with greater patient-reported PD severity and lower quality of life, although it is unclear whether this is the cause and/or a consequence of the disease. In essence, the Parkinson pandemic and the pandemic of social isolation have been further compounded by the recent COVID-19 pandemic. The results emphasize the need to keep PWP socially connected and prevent loneliness in this time of social distancing. Proactive use of virtual modalities for support groups and social prescribing should be explored.

scholarly journals Role of Serotonin Neurons in L-DOPA- and Graft-Induced Dyskinesia in a Rat Model of Parkinson's Disease

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Eunju Shin ◽  
Elisabetta Tronci ◽  
Manolo Carta
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Work ◽  
Serotonin System ◽  
Serotonin Neurons ◽  
Advanced Stages ◽  
Ventral Mesencephalic ◽  
The One ◽  
Patients Experience

L-DOPA, the most effective drug to treat motor symptoms of Parkinson's disease, causes abnormal involuntary movements, limiting its use in advanced stages of the disease. An increasing body of evidence points to the serotonin system as a key player in the appearance of L-DOPA-induced dyskinesia (LID). In fact, exogenously administered L-DOPA can be taken up by serotonin neurons, converted to dopamine and released as a false transmitter, contributing to pulsatile stimulation of striatal dopamine receptors. Accordingly, destruction of serotonin fibers or silencing serotonin neurons by serotonin agonists could counteract LID in animal models. Recent clinical work has also shown that serotonin neurons are present in the caudate/putamen of patients grafted with embryonic ventral mesencephalic cells, producing intense serotonin hyperinnervation. These patients experience graft-induced dyskinesia (GID), a type of dyskinesia phenotypically similar to the one induced by L-DOPA but independent from its administration. Interestingly, the 5-HT1Areceptor agonist buspirone has been shown to suppress GID in these patients, suggesting that serotonin neurons might be involved in the etiology of GID as for LID. In this paper we will discuss the experimental and clinical evidence supporting the involvement of the serotonin system in both LID and GID.

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