New Technologies and Tools for Diagnosing and Monitoring Movement Disorders

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Neurology".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 3692

Special Issue Editors


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Guest Editor
Department of Neuroscience "Rita Levi Montalcini”, University of Torino, 10124 Torino, Italy
Interests: movement disorders; Parkinson’s disease

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Guest Editor
Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
Interests: non-invasive brain stimulation; neurophysiology; movement disorders; dementia; transcranial magnetic stimulation; transcranial electrical stimulation; electroencephalography
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Systems Medicine, University of Roma Tor Vergata, Rome, Italy
Interests: Parkinson’s disease; movement disorders; neurodegenerative diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Several technological innovations have been implemented in recent years for application in health sciences. Devices encompassing sensors, computer interfaces, hardware and software for phenomenological assessment and biomarker identification, as well as different types of brain stimulation techniques represent the new frontiers of research in the field of movement disorders. Device-based instrumented tests conducted by clinicians in standardized environments or self-administered by patients, as well as novel neurophysiological and neuroimaging tools, can be used for the quantification of symptoms or the identification of specific pathophysiological processes. Moreover, new technologies and tools might prove relevant in differential diagnosis, disease progression, and treatment response assessment in patients with movement disorders.

This Special Issue aims to collect a series of articles specifically related to the use of new tools or devices to identify novel trial endpoints or outcome measures that can help early diagnosis, disease monitoring, or a new insight into the pathophysiology of movement disorders. We are interested in original research, pilot studies, and systematic reviews or meta-analyses, providing new insights into the use of novel techniques for evaluating patients with movement disorders. In particular, we will consider studies reporting discoveries on pathophysiology, progression, impact on patients’ functionality, and novel therapeutic approaches for Parkinson’s disease, parkinsonism, tremor, dystonia, ataxia, or other movement disorders. We are also interested in reviews providing information on the state of the art, ongoing research, and future perspectives in the field.

Dr. Carlo Alberto Artusi
Dr. Andrea Guerra
Dr. Tommaso Schirinzi
Guest Editors

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Keywords

  • Parkinson’s disease
  • tremor
  • dystonia
  • parkinsonism
  • ataxia
  • technology
  • mHealth
  • device
  • neuroimaging
  • neurophysiology
  • biomarkers

Published Papers (1 paper)

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Research

13 pages, 729 KiB  
Article
Hybrid Feature Selection Framework for the Parkinson Imbalanced Dataset Prediction Problem
by Hayder Mohammed Qasim, Oguz Ata, Mohammad Azam Ansari, Mohammad N. Alomary, Saad Alghamdi and Mazen Almehmadi
Medicina 2021, 57(11), 1217; https://doi.org/10.3390/medicina57111217 - 8 Nov 2021
Cited by 13 | Viewed by 2758
Abstract
Background and Objectives: Recently, many studies have focused on the early detection of Parkinson’s disease (PD). This disease belongs to a group of neurological problems that immediately affect brain cells and influence the movement, hearing, and various cognitive functions. Medical data sets [...] Read more.
Background and Objectives: Recently, many studies have focused on the early detection of Parkinson’s disease (PD). This disease belongs to a group of neurological problems that immediately affect brain cells and influence the movement, hearing, and various cognitive functions. Medical data sets are often not equally distributed in their classes and this gives a bias in the classification of patients. We performed a Hybrid feature selection framework that can deal with imbalanced datasets like PD. Use the SOMTE algorithm to deal with unbalanced datasets. Removing the contradiction from the features in the dataset and decrease the processing time by using Recursive Feature Elimination (RFE), and Principle Component Analysis (PCA). Materials and Methods: PD acoustic datasets and the characteristics of control subjects were used to construct classification models such as Bagging, K-nearest neighbour (KNN), multilayer perceptron, and the support vector machine (SVM). In the prepressing stage, the synthetic minority over-sampling technique (SMOTE) with two-feature selection RFE and PCA were used. The PD dataset comprises a large difference between the numbers of the infected and uninfected patients, which causes the classification bias problem. Therefore, SMOTE was used to resolve this problem. Results: For model evaluation, the train–test split technique was used for the experiment. All the models were Grid-search tuned, the evaluation results of the SVM model showed the highest accuracy of 98.2%, and the KNN model exhibited the highest specificity of 99%. Conclusions: the proposed method is compared with the current modern methods of detecting Parkinson’s disease and other methods for medical diseases, it was noted that our developed system could treat data bias and reach a high prediction of PD and this can be beneficial for health organizations to properly prioritize assets. Full article
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