scholarly journals Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1395
Author(s):  
S. Jeba Priya ◽  
Arockia Jansi Rani ◽  
M. S. P. Subathra ◽  
Mazin Abed Mohammed ◽  
Robertas Damaševičius ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Extraction ◽  
Human Gait ◽  
Degenerative Disorder ◽  
Artificial Neural Network Ann ◽  
Local Gradient ◽  
Pattern Transformation ◽  
Optimal Feature ◽  
Selection Of

Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal–Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait.

scholarly journals Gait analysis for early Parkinson’s disease detection based on deep learning

2019 ◽  
Vol 5 (1) ◽  
pp. 9-12
Author(s):  
Jyothsna Kondragunta ◽  
Christian Wiede ◽  
Gangolf Hirtz
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Gait Analysis ◽  
Pose Estimation ◽  
Early Identification ◽  
Human Gait ◽  
3D Pose Estimation ◽  
Wearable Technologies ◽  
Advantages And Disadvantages

AbstractBetter handling of neurological or neurodegenerative disorders such as Parkinson’s Disease (PD) is only possible with an early identification of relevant symptoms. Although the entire disease can’t be treated but the effects of the disease can be delayed with proper care and treatment. Due to this fact, early identification of symptoms for the PD plays a key role. Recent studies state that gait abnormalities are clearly evident while performing dual cognitive tasks by people suffering with PD. Researches also proved that the early identification of the abnormal gaits leads to the identification of PD in advance. Novel technologies provide many options for the identification and analysis of human gait. These technologies can be broadly classified as wearable and non-wearable technologies. As PD is more prominent in elderly people, wearable sensors may hinder the natural persons movement and is considered out of scope of this paper. Non-wearable technologies especially Image Processing (IP) approaches captures data of the person’s gait through optic sensors Existing IP approaches which perform gait analysis is restricted with the parameters such as angle of view, background and occlusions due to objects or due to own body movements. Till date there exists no researcher in terms of analyzing gait through 3D pose estimation. As deep leaning has proven efficient in 2D pose estimation, we propose an 3D pose estimation along with proper dataset. This paper outlines the advantages and disadvantages of the state-of-the-art methods in application of gait analysis for early PD identification. Furthermore, the importance of extracting the gait parameters from 3D pose estimation using deep learning is outlined.

scholarly journals Dementia with Parkinson's disease: Clinical diagnosis, neuropsychological aspects and treatment

2008 ◽  
Vol 2 (4) ◽  
pp. 261-266
Author(s):  
Jorge Lorenzo Otero
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Lewy Body ◽  
Task Force ◽  
Lewy Body Dementia ◽  
Data Bases ◽  
Main Text ◽  
Clinical Diagnoses ◽  
Movement Disorder Society ◽  
Selection Of

Abstract Dementia with Parkinson's disease represents a controversial issue in the complex group of alpha-synucleinopathies. The author acknowledges the concept of a "continuum" between Parkinson disease's (PD), Lewy body dementia (LBD), and dementia in Parkinson's disease (PDD). However, the practicing neurologist needs to identify the phenotypic signs of each dementia. The treatment and prognosis are different in spite of the overlaps between them. The main aim of this review was to characterize the clinical diagnoses of dementia associated with Parkinson's disease (PDD). Secondarily, the review discussed some epidemiological and neuropsychological issues. Selection of articles was not systematic and reflects the author's opinion, where the main text selected was the recommendations from the Movement Disorder Society Task Force for PDD diagnosis. The Pub Med, OVID, and Proquest data bases were used for the search.

scholarly journals Standardised Neuropsychological Assessment for the Selection of Patients Undergoing DBS for Parkinson’s Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Jennifer A. Foley ◽  
Tom Foltynie ◽  
Patricia Limousin ◽  
Lisa Cipolotti
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Mood Disorder ◽  
Neuropsychological Assessment ◽  
Careful Consideration ◽  
Baseline Risk ◽  
General Decline ◽  
Before And After ◽  
Selection Of

DBS is an increasingly offered advanced treatment for Parkinson’s disease (PD). Neuropsychological assessment is considered to be an important part of the screening for selection of candidates for this treatment. However, no standardised screening procedure currently exists. In this study, we examined the use of our standardised neuropsychological assessment for the evaluation of surgical candidates and to identify risk factors for subsequent decline in cognition and mood. A total of 40 patients were assessed before and after DBS. Evaluation of mood and case notes review was also undertaken. Before DBS, patients with PD demonstrated frequent impairments in intellectual functioning, memory, attention, and executive function, as well as high rates of mood disorder. Post-DBS, there was a general decline in verbal fluency only, and in one patient, we documented an immediate and irreversible global cognitive decline, which was associated with older age and more encompassing cognitive deficits at baseline. Case note review revealed that a high proportion of patients developed mood disorder, which was associated with higher levels of depression at baseline and greater reduction in levodopa medication. We conclude that our neuropsychological assessment is suitable for the screening of candidates and can identify baseline risk factors, which requires careful consideration before and after surgery.

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