scholarly journals The Detection of Freezing of Gait in Parkinson’s Disease Using Asymmetric Basis Function TV-ARMA Time–Frequency Spectral Estimation Method

2019 ◽  
Vol 27 (10) ◽  
pp. 2077-2086
Author(s):  
Yuzhu Guo ◽  
Lipeng Wang ◽  
Yang Li ◽  
Lingzhong Guo ◽  
Fangang Meng
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basis Function ◽  
Spectral Estimation ◽  
Freezing Of Gait ◽  
Estimation Method ◽  
Time Frequency

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 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.

Chronological View of Peak and Diphasic Dyskinesia, Wearing Off and Freezing of Gait in Parkinson’s Disease

2019 ◽  
Vol 9 (4) ◽  
pp. 741-747 ◽  
Author(s):  
Young Eun Kim ◽  
Beomseok Jeon ◽  
Ji Young Yun ◽  
Hui-Jun Yang ◽  
Han-Joon Kim
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait ◽  
Wearing Off

Effects of music-based movement therapy on motor function, balance, gait, mental health, and quality of life for patients with Parkinson’s disease: A systematic review and meta-analysis

2021 ◽  
pp. 026921552199052
Author(s):  
Zonglei Zhou ◽  
Ruzhen Zhou ◽  
Wen Wei ◽  
Rongsheng Luan ◽  
Kunpeng Li
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Motor Function ◽  
Meta Analysis ◽  
Freezing Of Gait ◽  
Movement Therapy ◽  
Walking Velocity

Objective: To conduct a systematic review evaluating the effects of music-based movement therapy on motor function, balance, gait, mental health, and quality of life among individuals with Parkinson’s disease. Data sources: A systematic search of PubMed, Embase, Cochrane Library, Web of Science, PsycINFO, CINAHL, and Physiotherapy Evidence Database was carried out to identify eligible papers published up to December 10, 2020. Review methods: Literature selection, data extraction, and methodological quality assessment were independently performed by two investigators. Publication bias was determined by funnel plot and Egger’s regression test. “Trim and fill” analysis was performed to adjust any potential publication bias. Results: Seventeen studies involving 598 participants were included in this meta-analysis. Music-based movement therapy significantly improved motor function (Unified Parkinson’s Disease Rating Scale motor subscale, MD = −5.44, P = 0.002; Timed Up and Go Test, MD = −1.02, P = 0.001), balance (Berg Balance Scale, MD = 2.02, P < 0.001; Mini-Balance Evaluation Systems Test, MD = 2.95, P = 0.001), freezing of gait (MD = −2.35, P = 0.039), walking velocity (MD = 0.18, P < 0.001), and mental health (SMD = −0.38, P = 0.003). However, no significant effects were observed on gait cadence, stride length, and quality of life. Conclusion: The findings of this study show that music-based movement therapy is an effective treatment approach for improving motor function, balance, freezing of gait, walking velocity, and mental health for patients with Parkinson’s disease.

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