A Systematic Review of Machine Learning Based Gait characteristics in Parkinson’s disease

2021 ◽  
Vol 21 ◽  
Author(s):  
Pooja Sharma ◽  
SK Pahuja ◽  
Karan Veer
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scales ◽  
Early Stage ◽  
Machine Learning Classification ◽  
Assessment Development ◽  
Gait Characteristics ◽  
Time Period

Objective: Parkinson’s disease is a pervasive neuro disorder that affects people's quality of life throughout the world. The unsatisfactory results of clinical rating scales open the door for more research. PD treatment using current biomarkers seems a difficult task. So automatic evaluation at an early stage may enhance the quality and time-period of life. Methods: Grading of Recommendations Assessment, Development, and Evaluation (GRADE) and Population, intervention, comparison, and outcome (PICO) search methodology schemes are followed to search the data and eligible studies for this survey. Approximate 1500 articles were extracted using related search strings. After the stepwise mapping and elimination of studies, 94 papers are found suitable for the present review. Results: After the quality assessment of extracted studies, nine inhibitors are identified to analyze people's gait with Parkinson’s disease, where four are critical. This review also differentiates the various machine learning classification techniques with their PD analysis characteristics in previous studies. The extracted research gaps are described as future perspectives. Results can help practitioners understand the PD gait as a valuable biomarker for detection, quantification, and classification. Conclusion: Due to less cost and easy recording of gait, gait-based techniques are becoming popular in PD detection. By encapsulating the gait-based studies, it gives an in-depth knowledge of PD, different measures that affect gait detection and classification.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals Slow Motion Analysis of Repetitive Tapping (SMART) Test: Measuring Bradykinesia in Recently Diagnosed Parkinson’s Disease and Idiopathic Anosmia

2021 ◽  
pp. 1-15
Author(s):  
Cristina Simonet ◽  
Miquel A. Galmes ◽  
Christian Lambert ◽  
Richard N. Rees ◽  
Tahrina Haque ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scales ◽  
Early Stage ◽  
Slow Motion ◽  
Motor Dysfunction ◽  
Prodromal Phase ◽  
Floor Effect ◽  
Cross Sectional ◽  
Early Stages

Background: Bradykinesia is the defining motor feature of Parkinson’s disease (PD). There are limitations to its assessment using standard clinical rating scales, especially in the early stages of PD when a floor effect may be observed. Objective: To develop a quantitative method to track repetitive tapping movements and to compare people in the early stages of PD, healthy controls, and individuals with idiopathic anosmia. Methods: This was a cross-sectional study of 99 participants (early-stage PD = 26, controls = 64, idiopathic anosmia = 9). For each participant, repetitive finger tapping was recorded over 20 seconds using a smartphone at 240 frames per second. From each video, amplitude between fingers, frequency (number of taps per second), and velocity (distance travelled per second) was extracted. Clinical assessment was based on the motor section of the MDS-UPDRS. Results: People in the early stage of PD performed the task with slower velocity (p <  0.001) and with greater frequency slope than controls (p = 0.003). The combination of reduced velocity and greater frequency slope obtained the best accuracy to separate early-stage PD from controls based on metric thresholds alone (AUC = 0.88). Individuals with anosmia exhibited slower velocity (p = 0.001) and smaller amplitude (p <  0.001) compared with controls. Conclusion: We present a simple, proof-of-concept method to detect early motor dysfunction in PD. Mean tap velocity appeared to be the best parameter to differentiate patients with PD from controls. Patients with anosmia also showed detectable differences in motor performance compared with controls which may suggest that some are in the prodromal phase of PD.

Mirroring Emotions: Patients' Versus Caregivers' Perceptions of Sexual Experience in Parkinson's Disease

2021 ◽  
Author(s):  
Giovanni Landi ◽  
Maria Rita Lo Monaco ◽  
Enrico Di Stasio ◽  
Diego Ricciardi ◽  
Marcella Solito ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Sexual Experience ◽  
Behavioral Changes ◽  
Dyadic Relationship ◽  
Complex Process ◽  
Sexual Wellbeing ◽  
Effect Of Pd

Abstract Background and aims: The need for intimacy and sexual expression is an essential dimension of quality of life. As patients with Parkinson's disease (PD) have to cope with essential changes in their global and sexual functioning, achieving a satisfying intimate and sexual relationship can be challenging. Sexual experience is a complex process that involves a dyadic relationship. In this study, we aimed to characterize the sexual experience of patients with Parkinson's disease and patients' vs caregivers' perceptions. Methods Twenty-seven PD patients and their caregivers were asked to complete the Arizona Sexual Experience Scale (ASEX) anonymously. They were instructed to refer to their sexual behavior over the past year and to consider behavioral changes that lasted for at least four consecutive weeks. Results Our data suggest that when considering sexual perceptions in PD, there is often agreement of judgment between patients and their partners. Overall, they have a rather good sex life, especially in the early stage of the disease, with similar behavior shown by men and women. Conclusions The effect of PD on the sexual and couple relationship challenges healthcare professionals to focus on the needs of both partners and to plan specific interventions in such a way as to prevent the deterioration of the couples' sexual wellbeing.

scholarly journals Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson’s Disease: A Comprehensive Machine Learning Approach

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rana Zia Ur Rehman ◽  
Silvia Del Din ◽  
Yu Guan ◽  
Alison J. Yarnall ◽  
Jian Qing Shi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Decision Making ◽  
Information Gain ◽  
Step Length ◽  
Support Vector ◽  
Step Width ◽  
Gait Characteristics

AbstractParkinson’s disease (PD) is the second most common neurodegenerative disease; gait impairments are typical and are associated with increased fall risk and poor quality of life. Gait is potentially a useful biomarker to help discriminate PD at an early stage, however the optimal characteristics and combination are unclear. In this study, we used machine learning (ML) techniques to determine the optimal combination of gait characteristics to discriminate people with PD and healthy controls (HC). 303 participants (119 PD, 184 HC) walked continuously around a circuit for 2-minutes at a self-paced walk. Gait was quantified using an instrumented mat (GAITRite) from which 16 gait characteristics were derived and assessed. Gait characteristics were selected using different ML approaches to determine the optimal method (random forest with information gain and recursive features elimination (RFE) technique with support vector machine (SVM) and logistic regression). Five clinical gait characteristics were identified with RFE-SVM (mean step velocity, mean step length, step length variability, mean step width, and step width variability) that accurately classified PD. Model accuracy for classification of early PD ranged between 73–97% with 63–100% sensitivity and 79–94% specificity. In conclusion, we identified a subset of gait characteristics for accurate early classification of PD. These findings pave the way for a better understanding of the utility of ML techniques to support informed clinical decision-making.

scholarly journals Apathy and Anhedonia in Parkinson's Disease

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Yoshiaki Kaji ◽  
Koichi Hirata
Keyword(s):  
Quality Of Life ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage

Depression, apathy, and anhedonia are often comorbid in patients with Parkinson's disease. Since the morbid states of apathy and anhedonia are complicated, these symptoms are often difficult to diagnose. Several therapeutic methods for apathy and anhedonia are considered effective. However, the validity of these methods has not been established. Similar to depression, apathy and anhedonia clearly affect the quality of life of patients and their families. Therefore, accurate diagnoses of morbid states in the early stage of the disease and corresponding appropriate treatments should be given high priority.

scholarly journals Slow Motion Analysis of Repetitive Tapping (SMART) test: measuring bradykinesia in recently diagnosed Parkinson’s disease and idiopathic anosmia

2021 ◽  
Author(s):  
C. Simonet ◽  
MA. Galmes ◽  
C. Lambert ◽  
RN. Rees ◽  
T. Haque ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scales ◽  
Early Stage ◽  
Motor Dysfunction ◽  
Finger Tapping ◽  
Prodromal Phase ◽  
Floor Effect ◽  
Simple Method ◽  
Early Stages

ABSTRACTBackgroundBradykinesia is the defining motor feature of Parkinson’s disease (PD). There are limitations to its assessment using standard clinical rating scales, especially in the early stages of PD when a floor effect may be observed.ObjectivesTo develop a quantitative method to track repetitive finger tapping movements and to compare people in the early stages of PD, healthy controls, and individuals with idiopathic anosmia.MethodsThis was a cross-sectional study of 99 participants (early-stage PD=26, controls=64, idiopathic anosmia=9). For each participant, repetitive finger tapping was recorded over 20 seconds using a smartphone at 240 frames per second. Three parameters were extracted from videos: amplitude between fingers, frequency (number of taps per second), and velocity (distance travelled per second). Clinical assessment was based on the motor section of MDS-UPDRS.ResultsPeople in the early stage of PD performed the task with slower velocity (p<0.001) and with greater decrement in frequency than controls (p=0.003). The combination of slower velocity and greater decrement in frequency obtained the best accuracy to separate early-stage PD from controls based on metric thresholds alone (AUC = 0.88). Individuals with anosmia exhibited slower velocity (p=0.001) and smaller amplitude (p<0.001) compared with controls.ConclusionsWe present a new simple method to detect early motor dysfunction in PD. Mean tap velocity appeared to be the best parameter to differentiate patients with PD from controls. Patients with anosmia also showed detectable differences in motor performance compared with controls which may be important indication of the prodromal phase of PD.

scholarly journals Identification of an early-stage Parkinson’s disease neuromarker using event-related potentials, brain network analytics and machine-learning

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0261947
Author(s):  
Sharon Hassin-Baer ◽  
Oren S. Cohen ◽  
Simon Israeli-Korn ◽  
Gilad Yahalom ◽  
Sandra Benizri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Early Stage ◽  
Event Related Potentials ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Network Analytics ◽  
Related Potentials ◽  
Advanced Analysis

Objective The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson’s disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. Background Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. Methods Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. Results The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). Conclusions This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Machine Learning Classification of Parkinson's Disease and Essential Tremor Using Wearable Sensors

2020 ◽  
Vol 101 (11) ◽  
pp. e44
Author(s):  
Sanghee Moon ◽  
Hyun-Je Song ◽  
Kelly Lyons ◽  
Rajesh Pahwa ◽  
Vibhash Sharma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Essential Tremor ◽  
Wearable Sensors ◽  
Machine Learning Classification
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