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小波在帕金森病步态事件定量评估中的应用

Application of Wavelet in Quantitative Evaluation of Gait Events of Parkinson's Disease.

作者信息

Zahra Noore

机构信息

College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

出版信息

Appl Bionics Biomech. 2021 Dec 9;2021:7199007. doi: 10.1155/2021/7199007. eCollection 2021.

DOI:10.1155/2021/7199007
PMID:34925552
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8677364/
Abstract

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.

摘要

结果

系统检测到冻结步态及其他步态姿势，并通过离散小波变换（DWT）检查微分分解信号来显示时频范围。能量分布和功率谱密度（PSD）图证明了系统的准确性。通过留一法（LOSO）进行验证，结果表明所提出的方法准确率达90%。

结论

临床试验观察验证了所提出的技术。与文献中报道的先前技术相比，可以看出所提出的方法在时间和频率分辨率以及处理时间方面都有改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/1e0ca2afaba7/ABB2021-7199007.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/aa00b1847075/ABB2021-7199007.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/4bc81cd47e3b/ABB2021-7199007.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/54dc126cf08c/ABB2021-7199007.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/3ccf196ce106/ABB2021-7199007.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/1e0ca2afaba7/ABB2021-7199007.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/aa00b1847075/ABB2021-7199007.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/4bc81cd47e3b/ABB2021-7199007.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/54dc126cf08c/ABB2021-7199007.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/3ccf196ce106/ABB2021-7199007.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f520/8677364/1e0ca2afaba7/ABB2021-7199007.005.jpg

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本文引用的文献

1
Quantitative Analysis of Postural Instability in Patients with Parkinson's Disease.帕金森病患者姿势不稳的定量分析
Parkinsons Dis. 2021 Apr 13;2021:5681870. doi: 10.1155/2021/5681870. eCollection 2021.
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The prevalence of freezing of gait in Parkinson's disease and in patients with different disease durations and severities.帕金森病以及不同病程和严重程度患者中冻结步态的患病率。
Chin Neurosurg J. 2020 May 14;6:17. doi: 10.1186/s41016-020-00197-y. eCollection 2020.
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The Detection of Freezing of Gait in Parkinson's Disease Using Asymmetric Basis Function TV-ARMA Time-Frequency Spectral Estimation Method.
使用非对称基函数 TV-ARMA 时频谱估计方法检测帕金森病的冻结步态。
IEEE Trans Neural Syst Rehabil Eng. 2019 Oct;27(10):2077-2086. doi: 10.1109/TNSRE.2019.2938301. Epub 2019 Aug 29.
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Clinical and methodological challenges for assessing freezing of gait: Future perspectives.评估冻结步态的临床和方法学挑战：未来展望。
Mov Disord. 2019 Jun;34(6):783-790. doi: 10.1002/mds.27709. Epub 2019 May 2.
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Using Wearable Technology to Generate Objective Parkinson's Disease Dyskinesia Severity Score: Possibilities for Home Monitoring.利用可穿戴技术生成客观的帕金森病运动障碍严重程度评分：家庭监测的可能性。
IEEE Trans Neural Syst Rehabil Eng. 2017 Oct;25(10):1853-1863. doi: 10.1109/TNSRE.2017.2690578. Epub 2017 Apr 3.
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Parkinson's Disease and Its Management: Part 1: Disease Entity, Risk Factors, Pathophysiology, Clinical Presentation, and Diagnosis.帕金森病及其管理：第1部分：疾病实体、危险因素、病理生理学、临床表现及诊断。
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Using EEG spatial correlation, cross frequency energy, and wavelet coefficients for the prediction of Freezing of Gait in Parkinson's Disease patients.利用脑电图空间相关性、交叉频率能量和小波系数预测帕金森病患者的步态冻结。
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:4263-6. doi: 10.1109/EMBC.2013.6610487.
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Is freezing of gait in Parkinson's disease a result of multiple gait impairments? Implications for treatment.帕金森病冻结步态是否是多种步态障碍的结果？对治疗的启示。
Parkinsons Dis. 2012;2012:459321. doi: 10.1155/2012/459321. Epub 2012 Jan 12.
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