scholarly journals 1 Application of neural network to detect freezing of gait in patients with Parkinson’s disease

2020 ◽  
pp. 1-12
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Freezing Of Gait

scholarly journals Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson’s Disease Based on Wearable Sensors

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1919
Author(s):  
Bochen Li ◽  
Zhiming Yao ◽  
Jianguo Wang ◽  
Shaonan Wang ◽  
Xianjun Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Cross Validation ◽  
Freezing Of Gait ◽  
Detection Performance ◽  
Operating Efficiency ◽  
The Impact ◽  
Fog Detection

Freezing of gait (FOG) is a paroxysmal dyskinesia, which is common in patients with advanced Parkinson’s disease (PD). It is an important cause of falls in PD patients and is associated with serious disability. In this study, we implemented a novel FOG detection system using deep learning technology. The system takes multi-channel acceleration signals as input, uses one-dimensional deep convolutional neural network to automatically learn feature representations, and uses recurrent neural network to model the temporal dependencies between feature activations. In order to improve the detection performance, we introduced squeeze-and-excitation blocks and attention mechanism into the system, and used data augmentation to eliminate the impact of imbalanced datasets on model training. Experimental results show that, compared with the previous best results, the sensitivity and specificity obtained in 10-fold cross-validation evaluation were increased by 0.017 and 0.045, respectively, and the equal error rate obtained in leave-one-subject-out cross-validation evaluation was decreased by 1.9%. The time for detection of a 256 data segment is only 0.52 ms. These results indicate that the proposed system has high operating efficiency and excellent detection performance, and is expected to be applied to FOG detection to improve the automation of Parkinson’s disease diagnosis and treatment.

Convolutional Neural Network to Model Articulation Impairments in Patients with Parkinson’s Disease

2017 ◽  
Author(s):  
J.C. Vásquez-Correa ◽  
Juan Rafael Orozco-Arroyave ◽  
Elmar Nöth
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Network

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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