Automatic and Objective Assessment of Alternating Tapping Performance in Parkinson’s Disease

Objective assessment of the motor evaluation test for Parkinson’s disease (PD) diagnosis is an open issue both for clinical and technical experts since it could improve current clinical practice with benefits both for patients and healthcare systems. In this work, a wearable system composed of four inertial devices (two SensHand and two SensFoot), and related processing algorithms for extracting parameters from limbs motion was tested on 40 healthy subjects and 40 PD patients. Seventy-eight and 96 kinematic parameters were measured from lower and upper limbs, respectively. Statistical and correlation analysis allowed to define four datasets that were used to train and test five supervised learning classifiers. Excellent discrimination between the two groups was obtained with all the classifiers (average accuracy ranging from 0.936 to 0.960) and all the datasets (average accuracy ranging from 0.953 to 0.966), over three conditions that included parameters derived from lower, upper or all limbs. The best performances (accuracy = 1.00) were obtained when classifying all the limbs with linear support vector machine (SVM) or gaussian SVM. Even if further studies should be done, the current results are strongly promising to improve this system as a support tool for clinicians in objectifying PD diagnosis and monitoring.

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Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson’s Disease

Sensors ◽

10.3390/s150923727 ◽

2015 ◽

Vol 15 (9) ◽

pp. 23727-23744 ◽

Cited By ~ 21

Author(s):

Mevludin Memedi ◽

Aleksander Sadikov ◽

Vida Groznik ◽

Jure Žabkar ◽

Martin Možina ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Objective Assessment ◽

Motor Symptoms

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Quantitative assessment of movements with inertial sensors in Parkinson’s disease

10.7287/peerj.preprints.1031 ◽

2015 ◽

Author(s):

Markus A Hobert

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Daily Activity ◽

Assessment Tool ◽

Inertial Sensors ◽

Small Body ◽

Objective Assessment ◽

Clinical Scales ◽

Functional Assessments ◽

The Moment

A major problem in the field of Parkinson's disease (PD) is that there is no objective assessment tool for PD symptoms to date. At the moment data are mostly collected with questionaires, interviews, or clinical scales. This makes the assessment of changes in the course of the disease, due to training or due to medication very difficult for patients and medical staff. A way to solve this issue is the objective measurement of movements (in patients with PD) with (small) body-worn sensor units containing accelerometers, gyroscopes and magnetometers. There are four main fields of applications of these sensor units in PD: 1) Measuring symptoms and instrumented clinical scales; 2) Instrumented functional assessments; 3) Quantification of daily activity; 4) Technology-assisted neurorehabilitation; In the talk examples of these four fields of applications have been discussed.

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Objective Assessment of Impulse Control Disorder in Patients with Parkinson’s Disease Using a Low-Cost LEGO-Like EEG Headset

10.21203/rs.3.rs-125029/v1 ◽

2020 ◽

Author(s):

Yuan-Pin Lin ◽

Hsing-Yi Liang ◽

Yueh-Sheng Chen ◽

Cheng-Hsien Lu ◽

Yih-Ru Wu ◽

...

Keyword(s):

Parkinson’S Disease ◽

Adverse Effect ◽

Parkinson's Disease ◽

Impulse Control ◽

Early Stage ◽

Low Cost ◽

Statistical Significance ◽

Objective Assessment ◽

Event Related Potential ◽

Eeg Abnormalities

Abstract BackgroundPatients with Parkinson’s disease (PD) can develop the cognitive adverse effect of impulse control disorders (ICDs) while undergoing a pharmacological treatment for motor control dysfunctions with a dopamine agonist (DA). Conventional clinical interviews or questionnaires can be biased and may not provide an accurate diagnosis in the early stage. A wearable electroencephalogram (EEG)-sensing headset paired with an examination procedure can be a potential user-friendly method to explore ICD-related biomarkers that can reflect brain activity abnormalities and detect its early signs and progression.MethodsA stereotypical Go/NoGo test that targets impulse inhibition was performed with 59 individuals, including heathy controls, patients with PD, and patients with PD diagnosed with ICD. A low-cost LEGO-like EEG headset was used to record concurrent EEG signals. The event-related potential (ERP) analytical framework was then used to explore ICD-related EEG abnormalities after DA treatment.ResultsOnly PD patients with ICD exhibited a tendency for N2 and P3 amplitude deterioration at the fronto-central regions (i.e., Fz, FCz, and Cz); in particular, the P3 counterpart reached statistical significance (p<0.05). Neither PD patients nor healthy controls (without DA) replicated such findings. Furthermore, N2 amplitude deterioration was found to be related to ICD severity at Fz (r=-0.28, p=0.04).ConclusionsA low-cost LEGO-like EEG headset successfully captured ERP neuromarkers for the objective assessment of ICD in PD patients undergoing DA treatment. The present objective neuro-evidence could provide complementary information to conventional clinical scales used to diagnose the ICD adverse effect.

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The Use of Event-Related Potentials and Machine Learning to Improve Diagnostic Testing and Prediction of Disease Progression in Parkinson’s Disease

10.3233/shti210737 ◽

2021 ◽

Author(s):

Robin Vlieger ◽

Elena Daskalaki ◽

Deborah Apthorp ◽

Christian J Lueck ◽

Hanna Suominen

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Disease Progression ◽

Objective Assessment ◽

Diagnostic Testing ◽

Event Related Potentials ◽

Disease Status ◽

Related Potentials ◽

Machine Learning Models

Current tests of disease status in Parkinson’s disease suffer from high variability, limiting their ability to determine disease severity and prognosis. Event-related potentials, in conjunction with machine learning, may provide a more objective assessment. In this study, we will use event-related potentials to develop machine learning models, aiming to provide an objective way to assess disease status and predict disease progression in Parkinson’s disease.

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Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-019-04570-7 ◽

2019 ◽

Vol 47 (2) ◽

pp. 437-450 ◽

Cited By ~ 7

Author(s):

Sanne K. Meles ◽

Remco J. Renken ◽

Marco Pagani ◽

L. K. Teune ◽

Dario Arnaldi ◽

...

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

The Netherlands ◽

Late Stage ◽

De Novo ◽

Objective Assessment ◽

Covariance Analysis ◽

Spatial Covariance ◽

Spanish Cohort ◽

Italian Cohort

Abstract Rationale In Parkinson’s disease (PD), spatial covariance analysis of 18F-FDG PET data has consistently revealed a characteristic PD-related brain pattern (PDRP). By quantifying PDRP expression on a scan-by-scan basis, this technique allows objective assessment of disease activity in individual subjects. We provide a further validation of the PDRP by applying spatial covariance analysis to PD cohorts from the Netherlands (NL), Italy (IT), and Spain (SP). Methods The PDRPNL was previously identified (17 controls, 19 PD) and its expression was determined in 19 healthy controls and 20 PD patients from the Netherlands. The PDRPIT was identified in 20 controls and 20 “de-novo” PD patients from an Italian cohort. A further 24 controls and 18 “de-novo” Italian patients were used for validation. The PDRPSP was identified in 19 controls and 19 PD patients from a Spanish cohort with late-stage PD. Thirty Spanish PD patients were used for validation. Patterns of the three centers were visually compared and then cross-validated. Furthermore, PDRP expression was determined in 8 patients with multiple system atrophy. Results A PDRP could be identified in each cohort. Each PDRP was characterized by relative hypermetabolism in the thalamus, putamen/pallidum, pons, cerebellum, and motor cortex. These changes co-varied with variable degrees of hypometabolism in posterior parietal, occipital, and frontal cortices. Frontal hypometabolism was less pronounced in “de-novo” PD subjects (Italian cohort). Occipital hypometabolism was more pronounced in late-stage PD subjects (Spanish cohort). PDRPIT, PDRPNL, and PDRPSP were significantly expressed in PD patients compared with controls in validation cohorts from the same center (P < 0.0001), and maintained significance on cross-validation (P < 0.005). PDRP expression was absent in MSA. Conclusion The PDRP is a reproducible disease characteristic across PD populations and scanning platforms globally. Further study is needed to identify the topography of specific PD subtypes, and to identify and correct for center-specific effects.

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