scholarly journals Deep feature learning for FoG episodes prediction In patients with PD

2020 ◽  
Vol 5 (2) ◽  
pp. 79-95
Author(s):  
Hadeer Elziaat ◽  
◽  
Nashwa El-Bendary ◽  
Ramdan Mowad ◽  
◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
Feature Learning ◽  
Classification Problem ◽  
Common Symptom ◽  
Suggested Approach ◽  
Time Frequency ◽  
Deep Feature ◽  
Multi Class Classification

A common symptom of Parkinson's Disease is Freezing of Gait (FoG) that causes an interrupt of the forward progression of the patient’s feet while walking. Therefore, Freezing of Gait episodes is always engaged to the patient's falls. This paper proposes a model for Freezing of Gait episodes detection and prediction in patients with Parkinson's disease. Predicting Freezing of Gait in this paper considers as a multi-class classification problem with 3 classes namely, FoG, pre-FoG, and walking episodes. In this paper, the extracted feature scheme applied for the detection and the prediction of FoG is Convolutional Neural Network (CNN) spectrogram time-frequency features. The dataset is collected from three tri-axial accelerometer sensors for PD patients with FoG. The performance of the suggested approach has been distinguished by different machine learning classifiers and accelerometer axes.

scholarly journals Prediction of the Levodopa Challenge Test in Parkinson’s Disease Using Data from a Wrist-Worn Sensor

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5153 ◽  
Author(s):  
Hamid Khodakarami ◽  
Lucia Ricciardi ◽  
Maria Contarino ◽  
Rajesh Pahwa ◽  
Kelly Lyons ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Challenge Test ◽  
Classification Problem ◽  
Clinical Scales ◽  
Clinically Significant ◽  
Class Labels ◽  
Using Data ◽  
Multi Class Classification ◽  
Operator Curve

The response to levodopa (LR) is important for managing Parkinson’s Disease and is measured with clinical scales prior to (OFF) and after (ON) levodopa. The aim of this study was to ascertain whether an ambulatory wearable device could predict the LR from the response to the first morning dose. The ON and OFF scores were sorted into six categories of severity so that separating Parkinson’s Kinetigraph (PKG) features corresponding to the ON and OFF scores became a multi-class classification problem according to whether they fell below or above the threshold for each class. Candidate features were extracted from the PKG data and matched to the class labels. Several linear and non-linear candidate statistical models were examined and compared to classify the six categories of severity. The resulting model predicted a clinically significant LR with an area under the receiver operator curve of 0.92. This study shows that ambulatory data could be used to identify a clinically significant response to levodopa. This study has also identified practical steps that would enhance the reliability of this test in future studies.

scholarly journals Safety and Tolerability of Burst-Cycling Deep Brain Stimulation for Freezing of Gait in Parkinson’s Disease

2021 ◽  
Vol 15 ◽  
Author(s):  
Joshua K. Wong ◽  
Wei Hu ◽  
Ryan Barmore ◽  
Janine Lopes ◽  
Kathryn Moore ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Freezing Of Gait ◽  
Functional Mobility ◽  
Common Symptom ◽  
Burst Frequency ◽  
Serious Adverse Events ◽  
Deep Brain

Background: Freezing of gait (FOG) is a common symptom in Parkinson’s disease (PD) and can be difficult to treat with dopaminergic medications or with deep brain stimulation (DBS). Novel stimulation paradigms have been proposed to address suboptimal responses to conventional DBS programming methods. Burst-cycling deep brain stimulation (BCDBS) delivers current in various frequencies of bursts (e.g., 4, 10, or 15 Hz), while maintaining an intra-burst frequency identical to conventional DBS.Objective: To evaluate the safety and tolerability of BCDBS in PD patients with FOG.Methods: Ten PD subjects with STN or GPi DBS and complaints of FOG were recruited for this single center, single blinded within-subject crossover study. For each subject, we compared 4, 10, and 15 Hz BCDBS to conventional DBS during the PD medication-OFF state.Results: There were no serious adverse events with BCDBS. It was feasible and straightforward to program BCDBS in the clinic setting. The benefit was comparable to conventional DBS in measures of FOG, functional mobility and in PD motor symptoms. BCDBS had lower battery consumption when compared to conventional DBS.Conclusions: BCDBS was feasible, safe and well tolerated and it has potential to be a viable future DBS programming strategy.

scholarly journals Amyloid ß Impacts Future Freezing of Gait in Parkinson’s Disease Via White Matter Hyperintensities

2020 ◽  
Author(s):  
Mahsa Dadar ◽  
Janis Miyasaki ◽  
Simon Duchesne ◽  
Richard Camicioli
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
White Matter Hyperintensities ◽  
Freezing Of Gait ◽  
Amyloid Β ◽  
Common Symptom ◽  
Activity Levels ◽  
Lewy Body Pathology ◽  
The Impact

AbstractBackgroundFreezing of gait (FOG) is a common symptom in Parkinson’s Disease (PD) patients. Previous studies have reported relationships between FOG, substantia nigra (SN) degeneration, dopamine transporter (DAT) concentration, as well as amyloid β deposition. However, there is a paucity of research on the concurrent impact of white matter damage.ObjectivesTo assess the inter-relationships between these different co-morbidities, their impact on future FOG and whether they act independently of each other.MethodsWe used baseline MRI and longitudinal gait data from the Parkinson’s Progression Markers Initiative (PPMI). We used deformation based morphometry (DBM) from T1-weighted MRI to measure SN atrophy, and segmentation of white matter hyperintensities (WMH) as a measure of WM pathological load. Putamen and caudate DAT levels from SPECT as well as cerebrospinal fluid (CSF) amyloid β were obtained directly from the PPMI. Following correlation analyses, we investigated whether WMH burden mediates the impact of amyloid β on future FOG.ResultsSN DBM, WMH load, putamen and caudate DAT activity and CSF amyloid β levels were significantly different between PD patients with and without future FOG (p < 0.008). Mediation analysis demonstrated an effect of CSF amyloid β levels on future FOG via WMH load, independent of SN atrophy and striatal DAT activity levels.ConclusionsAmyloid β might impact future FOG in PD patients through an increase in WMH burden, in a pathway independent of Lewy body pathology.

scholarly journals Foot Pressure Wearable Sensors for Freezing of Gait Detection in Parkinson’s Disease

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 128
Author(s):  
Andrea Marcante ◽  
Roberto Di Marco ◽  
Giovanni Gentile ◽  
Clelia Pellicano ◽  
Francesca Assogna ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Everyday Life ◽  
False Negative ◽  
Freezing Of Gait ◽  
False Negative Rate ◽  
Common Symptom ◽  
Foot Pressure ◽  
Assessment Protocol ◽  
Increased Risk

Freezing of Gait (FoG) is a common symptom in Parkinson’s Disease (PD) occurring with significant variability and severity and is associated with increased risk of falls. FoG detection in everyday life is not trivial, particularly in patients manifesting the symptom only in specific conditions. Various wearable devices have been proposed to detect PD symptoms, primarily based on inertial sensors. We here report the results of the validation of a novel system based on a pair of pressure insoles equipped with a 3D accelerometer to detect FoG episodes. Twenty PD patients attended a motor assessment protocol organized into eight multiple video recorded sessions, both in clinical and ecological settings and both in the ON and OFF state. We compared the FoG episodes detected using the processed data gathered from the insoles with those tagged by a clinician on video recordings. The algorithm correctly detected 90% of the episodes. The false positive rate was 6% and the false negative rate 4%. The algorithm reliably detects freezing of gait in clinical settings while performing ecological tasks. This result is promising for freezing of gait detection in everyday life via wearable instrumented insoles that can be integrated into a more complex system for comprehensive motor symptom monitoring in PD.

scholarly journals Falls risk is predictive of dysphagia in Parkinson’s disease

2021 ◽  
Author(s):  
Christopher Kobylecki ◽  
Irena Shiderova ◽  
Mihaela Boca ◽  
Emilia Michou
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Correlation Analysis ◽  
Freezing Of Gait ◽  
Gait Disturbance ◽  
Common Symptom ◽  
Factors Associated ◽  
Axial Symptoms ◽  
Gait Score ◽  
The Relationship

Abstract Objective Evaluate the relationship between falls, freezing of gait, and swallowing disturbance in Parkinson’s disease (PD). Background Dysphagia is a common symptom in PD, and is often thought of as an axial feature along with falls and gait disturbance. It is of interest to examine the relationship between these symptoms in PD, given the possibility of shared pathophysiology due to non-dopaminergic and extranigral dysfunction. Methods We recruited 29 consecutive non-demented patients with idiopathic PD and at least one clinically determined impairment in swallowing, falls, or freezing of gait. Swallow dysfunction was assessed using the Swallowing Disturbance Questionnaire (SDQ). The Falls Efficacy Scale and Freezing-of-gait questionnaire were recorded. Correlation analysis and multiple regression were used to determine the relationship between swallow and gait disturbance. Results Total SDQ score correlated strongly with the falls efficacy scale (Spearman’s rho = 0.594; P = 0.001), but not with the freezing-of-gait score. Linear regression controlling for other factors associated with dysphagia identified falls efficacy score as a significant predictor of swallow dysfunction. Conclusions The severity of dysphagia in PD is closely related to severity of falls, but not gait freezing. This may be helpful to more precisely determine the anatomical substrate of levodopa-resistant axial symptoms in PD and provide clues to further management.

scholarly journals Application of Wavelet in Quantitative Evaluation of Gait Events of Parkinson’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Noore Zahra
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Energy Distribution ◽  
Freezing Of Gait ◽  
Drug Regimen ◽  
Frequency Resolution ◽  
Discrete Wavelet ◽  
Postural Adjustments ◽  
Time Frequency ◽  
Gait Abnormalities

Motivation. In Parkinson’s disease, disturbances in gait initiation are of particular interest as they affect postural adjustments and movement disorders which may lead to falling. This falling down may be dangerous and at times life threatening, thus becoming a major concern for the patient and the clinician. These gait abnormalities are due to dependencies of movement on the motor system. Paroxysmal dyskinesia (commonly termed as freezing of gait) is one of the extreme cases of motor blocks. Since the last two decades, automated methods for monitoring motor activities, their data analysis, and algorithm techniques have been subjects of research for Parkinson’s disease (PD). This research will be of help to clinicians in prescribing a drug regimen. Problem Statement. Development of a system based on an algorithm for automatic detection of the freezing of gait (FOG) and other postural adjustments, with the help of wearable sensor’s data and to provide a quantitative approach for assessing the intensity of PD by analyzing frequency components associated with different motor movements and gait. Methodology. This paper presents a novel wavelet energy distribution approach to distinguish between walking, standing, and FOG. Data from the acceleration sensor is taken as input. After preprocessing, discrete wavelet transform (DWT) is applied on the data which shows its entire frequency spectrum. In the next step, energy is computed for the decomposed level of interest. Results. Systems detected FOG and other gait postures and showed time-frequency range by examining differentiated decomposed signals by DWT. Energy distribution and PSD graph proved the accuracy of the system. Validation is done by the LOSO method which shows 90% accuracy for the proposed method. Conclusion. Observations of the clinical trials validate the proposed technique. In comparison to the previous techniques reported in literature, it is seen that the proposed method shows improvement in time and frequency resolution as well as processing time.

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