scholarly journals Analysis of Hand Tremor in Parkinson’s Disease: Frequency Domain Approach

2020 ◽  
Author(s):  
Elham Samadi ◽  
Hessam Ahmadi ◽  
Fereidoon Nowshiravan Rahatabad
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Frequency Domain ◽  
Rating Scale ◽  
Frequency Resolution ◽  
Accelerometer Sensor ◽  
Hand Tremor ◽  
Time Frequency ◽  
Time Domain Signal ◽  
The Time Domain

Purpose: Parkinson's Disease (PD) is a neuro-degenerative interminable issue causing dynamic loss of dopamine-creating synapses, which is one of the most far reaching ailments after Alzheimer's infection. In this paper, a system for the classification of Parkinson’s disease tremor using noninvasive measurement and frequency domain features is represented. Materials and Methods: Tremor time-series of Parkinson's disease patients were recorded via a smartphone’s accelerometer sensor. Short-Time Fourier Transform (STFT) was applied to transform the time-domain signal into the frequency domain with high time-frequency resolution. Several frequency features, including mean, max of power spectral density and side frequency have been extracted and by using the FDR algorithm combinations of features carried enough information to reliably assess the severity of tremor in Parkinson patients were determined. Results: Four different classifiers were implemented to estimate the severity of tremors based on the Unified Parkinson's Disease Rating Scale (UPDRS) in Parkinson's disease patients. Conclusion: Classifiers’ estimation was compared to clinical scores derived via neurologist UPDRS annotation on Parkinson's disease patients’ tremor. The best accuracy achieved was 95.91±1.51.

Quantitative Motor Assessment, Detection, and Suppression of Parkinson’s Disease Hand Tremor: A Literature Review

2016 ◽  
Author(s):  
Khoi Ly ◽  
Aimee Cloutier ◽  
James Yang
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scales ◽  
Rating Scale ◽  
Research Area ◽  
Hand Tremor ◽  
Active Research ◽  
State Of Research ◽  
Future Direction ◽  
Cognitive Problems

Parkinson’s disease (PD) is difficult to detect before the onset of symptoms; further, PD symptoms share characteristics with symptoms of other diseases, making diagnosis of PD a challenging task. Without proper diagnosis and treatment, PD symptoms including tremor, bradykinesia, and cognitive problems deteriorate quickly into patients’ late life. Among them, the most distinguishable manifestations of PD are rest and postural tremor. Tremor is defined as an involuntary shaking or quivering movement of the hands or feet. Unified Parkinson’s Disease Rating Scale (UPDRS), Hoehn and Yahr (H&Y) scales are the most common rating scales that quantify the severity of PD. Due to the lack of consistency in these diagnostic tests, researchers are looking for devices for quantification and detection that can provide more objective PD motor assessments. Additionally, since there is currently no cure for PD, temporary PD symptom suppression is an active research area for improving patients’ quality of life. In this survey, the current state of research on Parkinson’s disease hand tremor quantification, detection, and suppression is discussed, especially focusing on electromechanical devices. The future direction of research on these devices is also considered.

scholarly journals Ensemble deep model for continuous estimation of Unified Parkinson’s Disease Rating Scale III

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Murtadha D. Hssayeni ◽  
Joohi Jimenez-Shahed ◽  
Michelle A. Burack ◽  
Behnaz Ghoraani
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Examination ◽  
Rating Scale ◽  
Time Frequency ◽  
Deep Model ◽  
Severity Scores ◽  
Disease Rating ◽  
Continuous Estimation ◽  
At Home

Abstract Background Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson’s disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson’s disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications. Methods We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time–frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time–frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data. Results The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of $$0.79\, (\textit{p}<0.0001)$$ 0.79 ( p < 0.0001 ) and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks. Conclusion Our analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease.

A common-reflection-point gather random noise attenuation method based on the synchrosqueezing wavelet transform

2021 ◽  
pp. 1-81
Author(s):  
Xiaokai Wang ◽  
Zhizhou Huo ◽  
Dawei Liu ◽  
Weiwei Xu ◽  
Wenchao Chen
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Seismic Data ◽  
Random Noise ◽  
Principal Component ◽  
Frequency Resolution ◽  
Reflection Point ◽  
Transform Method ◽  
Time Frequency ◽  
The Time Domain

Common-reflection-point (CRP) gather is one extensive-used prestack seismic data type. However, CRP suffers more noise than poststack seismic dataset. The events in the CRP gather are always flat, and the effective signals from neighboring traces in the CRP gather have similar forms not only in the time domain but also in the time-frequency domain. Therefore, we firstly use the synchrosqueezing wavelet transform (SSWT) to decompose seismic traces to the time-frequency domain, as the SSWT has better time-frequency resolution and reconstruction properties. Then we propose to use the similarity of neighboring traces to smooth and threshold the SSWT coefficients in the time-frequency domain. Finally, we used the modified SSWT coefficients to reconstruct the denoised traces for the CRP gather. Synthetic and field data examples show that our proposed method can effectively attenuate random noise with a better attenuation performance than the commonly-used principal component analysis, FX filter, and the continuous wavelet transform method.

scholarly journals A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson’s Patients

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 981
Author(s):  
Asma Channa ◽  
Rares-Cristian Ifrim ◽  
Decebal Popescu ◽  
Nirvana Popescu
Keyword(s):  
Older Adults ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale ◽  
Objective Assessment ◽  
Spectral Features ◽  
Motor Symptoms ◽  
Healthy Controls ◽  
Clinical Environment ◽  
Hand Tremor

Parkinson’s disease patients face numerous motor symptoms that eventually make their life different from those of normal healthy controls. Out of these motor symptoms, tremor and bradykinesia, are relatively prevalent in all stages of this disease. The assessment of these symptoms is usually performed by traditional methods where the accuracy of results is still an open question. This research proposed a solution for an objective assessment of tremor and bradykinesia in subjects with PD (10 older adults aged greater than 60 years with tremor and 10 older adults aged greater than 60 years with bradykinesia) and 20 healthy older adults aged greater than 60 years. Physical movements were recorded by means of an AWEAR bracelet developed using inertial sensors, i.e., 3D accelerometer and gyroscope. Participants performed upper extremities motor activities as adopted by neurologists during the clinical assessment based on Unified Parkinson’s Disease Rating Scale (UPDRS). For discriminating the patients from healthy controls, temporal and spectral features were extracted, out of which non-linear temporal and spectral features show greater difference. Both supervised and unsupervised machine learning classifiers provide good results. Out of 40 individuals, neural net clustering discriminated 34 individuals in correct classes, while the KNN approach discriminated 91.7% accurately. In a clinical environment, the doctor can use the device to comprehend the tremor and bradykinesia of patients quickly and with higher accuracy.

scholarly journals Correlation of visual area with tremor improvement after MRgFUS thalamotomy in Parkinson’s disease

2021 ◽  
pp. 1-8
Author(s):  
Yongqin Xiong ◽  
Dongshan Han ◽  
Jianfeng He ◽  
Rui Zong ◽  
Xiangbing Bian ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neuronal Activity ◽  
Repeated Measures ◽  
Rating Scale ◽  
Occipital Cortex ◽  
Visual Area ◽  
Rank Test ◽  
Hand Tremor ◽  
The Impact

OBJECTIVE MRI-guided focused ultrasound (MRgFUS) thalamotomy is a novel and minimally invasive alternative for medication-refractory tremor in Parkinson’s disease (PD). However, the impact of MRgFUS thalamotomy on spontaneous neuronal activity in PD remains unclear. The purpose of the current study was to evaluate the effects of MRgFUS thalamotomy on local fluctuations in neuronal activity as measured by the fractional amplitude of low-frequency fluctuations (fALFF) in patients with PD. METHODS Participants with PD undergoing MRgFUS thalamotomy were recruited. Tremor scores were assessed before and 3 and 12 months after treatment using the Clinical Rating Scale for Tremor. MRI data were collected before and 1 day, 1 week, 1 month, 3 months, and 12 months after thalamotomy. The fALFF was calculated. A whole-brain voxel-wise paired t-test was used to identify significant changes in fALFF at 12 months after treatment compared to baseline. Then fALFF in the regions with significant differences were extracted from fALFF maps of patients for further one-way repeated-measures ANOVA to investigate its dynamic alterations. The association between fALFF changes induced by thalamotomy and tremor improvement were evaluated using the nonparametric Spearman rank test. RESULTS Nine participants with PD (mean age ± SD 64.7 ± 6.1 years, 8 males) were evaluated. Voxel-based analysis showed that fALFF in the left occipital cortex (Brodmann area 17 [BA17]) significantly decreased at 12 months after thalamotomy compared to baseline (voxel p < 0.001, cluster p < 0.05 family-wise error [FWE] corrected). At baseline, fALFF in the left occipital BA17 in patients was elevated compared with that in 9 age- and gender-matched healthy subjects (p < 0.05). Longitudinal analysis displayed the dynamic changes of fALFF in this region (F (5,40) = 3.61, p = 0.009). There was a significant positive correlation between the falling trend in fALFF in the left occipital BA17 and hand tremor improvement after treatment over 3 time points (Spearman’s rho = 0.44, p = 0.02). CONCLUSIONS The present study investigated the impact of MRgFUS ventral intermediate nucleus thalamotomy on spontaneous neural activity in medication-refractory tremor-dominant PD. The visual area is, for the first time, reported as relevant to tremor improvement in PD after MRgFUS thalamotomy, suggesting a distant effect of MRgFUS thalamotomy and the involvement of specific visuomotor networks in tremor control in PD.

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.

Parkinson's Disease Cognitive Functional Rating Scale

2012 ◽  
Author(s):  
Jaime Kulisevsky ◽  
Ramón Fernández de Bobadilla ◽  
Javier Pagonabarraga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Rating Scale

scholarly journals Azathioprine immunosuppression and disease modification in Parkinson’s disease (AZA-PD): a randomised double-blind placebo-controlled phase II trial protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e040527
Author(s):  
Julia C Greenland ◽  
Emma Cutting ◽  
Sonakshi Kadyan ◽  
Simon Bond ◽  
Anita Chhabra ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Immune System ◽  
Phase Ii ◽  
Rating Scale ◽  
Positron Emission Tomography Imaging ◽  
Clinical Markers ◽  
Double Blind ◽  
Positron Emission ◽  
Immunosuppressant Medication

IntroductionThe immune system is implicated in the aetiology and progression of Parkinson’s disease (PD). Inflammation and immune activation occur both in the brain and in the periphery, and a proinflammatory cytokine profile is associated with more rapid clinical progression. Furthermore, the risk of developing PD is related to genetic variation in immune-related genes and reduced by the use of immunosuppressant medication. We are therefore conducting a ‘proof of concept’ trial of azathioprine, an immunosuppressant medication, to investigate whether suppressing the peripheral immune system has a disease-modifying effect in PD.Methods and analysisAZA-PD is a phase II randomised placebo-controlled double-blind trial in early PD. Sixty participants, with clinical markers indicating an elevated risk of disease progression and no inflammatory or immune comorbidity, will be treated (azathioprine:placebo, 1:1) for 12 months, with a further 6-month follow-up. The primary outcome is the change in the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale gait/axial score in the OFF state over the 12-month treatment period. Exploratory outcomes include additional measures of motor and cognitive function, non-motor symptoms and quality of life. In addition, peripheral and central immune markers will be investigated through analysis of blood, cerebrospinal fluid and PK-11195 positron emission tomography imaging.Ethics and disseminationThe study was approved by the London-Westminster research ethics committee (reference 19/LO/1705) and has been accepted by the Medicines and Healthcare products Regulatory Agency (MHRA) for a clinical trials authorisation (reference CTA 12854/0248/001–0001). In addition, approval has been granted from the Administration of Radioactive Substances Advisory Committee. The results of this trial will be disseminated through publication in scientific journals and presentation at national and international conferences, and a lay summary will be available on our website.Trial registration numbersISRCTN14616801 and EudraCT- 2018-003089-14.

scholarly journals Glucocerebrosidase Activity is Reduced in Cryopreserved Parkinson’s Disease Patient Monocytes and Inversely Correlates with Motor Severity

2021 ◽  
pp. 1-9
Author(s):  
Laura P. Hughes ◽  
Marilia M.M. Pereira ◽  
Deborah A. Hammond ◽  
John B. Kwok ◽  
Glenda M. Halliday ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Peripheral Blood ◽  
Mononuclear Cells ◽  
Rating Scale ◽  
Disease Patient ◽  
Motor Symptom ◽  
Blood Monocytes ◽  
Peripheral Blood Monocytes ◽  
Classical Monocytes

Background: Reduced activity of lysosomal glucocerebrosidase is found in brain tissue from Parkinson’s disease patients. Glucocerebrosidase is also highly expressed in peripheral blood monocytes where its activity is decreased in Parkinson’s disease patients, even in the absence of GBA mutation. Objective: To measure glucocerebrosidase activity in cryopreserved peripheral blood monocytes from 30 Parkinson’s disease patients and 30 matched controls and identify any clinical correlation with disease severity. Methods: Flow cytometry was used to measure lysosomal glucocerebrosidase activity in total, classical, intermediate, and non-classical monocytes. All participants underwent neurological examination and motor severity was assessed by the Movement Disorders Society Unified Parkinson’s Disease Rating Scale. Results: Glucocerebrosidase activity was significantly reduced in the total and classical monocyte populations from the Parkinson’s disease patients compared to controls. GCase activity in classical monocytes was inversely correlated to motor symptom severity. Conclusion: Significant differences in monocyte glucocerebrosidase activity can be detected in Parkinson’s disease patients using cryopreserved mononuclear cells and monocyte GCase activity correlated with motor features of disease. Being able to use cryopreserved cells will facilitate the larger multi-site trials needed to validate monocyte GCase activity as a Parkinson’s disease biomarker.

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