基于深度卷积神经网络的帕金森步态识别

doi:10.3969/j.issn.0258-8021.2017.04.005

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (991 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The freezing of gait(FOG)is the most common symptoms of late-stage Parkinson's disease. The sudden attack of FOG can cause patients walking trouble. It is an effective and feasible treatment method to equip patients with wearable device assistant which can detect FOG. When the FOG attack is detected, the wearable device provides patients with the rhythm of the auditory signal to HELP the recovery of walking. In this article, in view of FOG detection, we proposed a systemic feature learning method. This method used a convolutional neural networkbased on deep learning to automatically conduct feature learning from the original input signals. And a supervised learning method was adopted to improve learned features' recognition capability using tag information. In the entire network model, feature learning and classification reinforced each other to make the whole network more stable and more intelligent, which was verified by the DAPHNet datasets. Compared with the previous threshold method, the average correct rate was increased to 91.43%, the sensitivity was increased to 85.58% and the specificity was increased to 93.63%. To some extent, the proposed methodcould alleviate the FOG of patients with Parkinson's disease, and reduce the number of falls, which is of great significance to improve the ability of daily life of the patients and the quality of life.

Key words： Parkinson's disease the gait freeze identification deep learning convolutional neural network

Received: 27 April 2016

PACS:

R318

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Wang Jinjia
	Liu Qingyu
	Chen Hao

Cite this article:

Wang Jinjia,Liu Qingyu,Chen Hao. Detection of Freezing of Gait for Parkinson's Disease Patients Based on Deep Convolutional Neural Networks[J]. Chinese Journal of Biomedical Engineering, 2017, 36(4): 418-425.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2017.04.005 OR http://cjbme.csbme.org/EN/Y2017/V36/I4/418

[1] Nutt JG, Bloem BR, Giladi N, et al. Freezing of gait: moving forward on a mysterious clinical phenomenon.[J]. Lancet Neurology, 2011, 10(8):734-744.
[2] Macht M, Kaussner YJ, Stiasny-Kolster K, et al. Predictors of freezing in Parkinson's disease: a survey of 6,620 patients.[J]. Movement Disorders, 2007, 22(7):953-956.
[3] Giladi N, Mcdermott MP, Fahn S, et al. Freezing of gait in PD: prospective assessment in the DATATOP cohort.[J]. Neurology, 2001, 56(12):1712-1721.
[4] 李利, 刘晶, 罗蔚锋,等. 帕金森病冻结步态研究进展[J]. 中华神经科杂志, 2014, 47(8):565-567.
[5] Morris TR, Cho C, Dilda V, et al. A comparison of clinical and objective measures of freezing of gait in Parkinson's disease.[J]. Parkinsonism & Related Disorders, 2012, 18(5):572-577.
[6] Hausdorff JM, Schaafsma JD, Balash Y, et al. Impaired regulation of stride variability in Parkinson's disease subjects with freezing of gait.[J]. Experimental Brain Research, 2003, 149(2):187-194.
[7] Moore ST, Macdougall HG, Ondo WG. Ambulatory monitoring of freezing of gait in Parkinson's disease[J]. Journal of the Neurological Sciences, 2008, 167(2):340-348.
[8] Arnaud D, Snijders AH, Vivian W, et al. Objective detection of subtle freezing of gait episodes in Parkinson's disease[J]. Movement Disorders, 2010, 25(11):1684-1693.
[9] Giladi N. Freezing of gait. Clinical overview.[J]. Advances in Neurology, 2001, 87(2):191-197.
[10] Macht M, Kaussner YJ, Stiasny-Kolster K, et al. Predictors of freezing in Parkinson's disease: a survey of 6,620 patients.[J]. Movement Disorders, 2007, 22(7):953-956.
[11] HashimotoT. Speculation on the responsible sites and pathophysiology of freezing of gait[J]. Parkinsonism & Related Disorders, 2006, 12(S2):S55-S62.
[12] Hausdorff JM, Balash Y, Giladi N. Time series analysis of leg movements during freezing of gait in Parkinson's disease: akinesia, rhyme or reason?[J]. Physica A Statistical Mechanics & its Applications, 2003, 321(3-4):565-570.
[13] Bonato P, Sherrill DM, Standaert DG, et al. Data mining techniques to detect motor fluctuations in Parkinson's disease[C]//Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Francisco:IEEE,2004:4766-4769.
[14] Moore ST, Macdougall HG, Ondo WG. Ambulatory monitoring of freezing of gait in Parkinson's disease[J]. Journal of the Neurological Sciences, 2008, 167(2):340-348.
[15] Tay A, Yen SC, Lee PY, et al. Freezing of Gait (FoG) detection for Parkinson Disease[C] //Tay A,2015 10th Asian Control Conference. Kota Kinabal: IEEE, 2015:1-6.
[16] Marc B, Meir P, Daniel R, et al. Wearable assistant for Parkinson's disease patients with the freezing of gait symptom.[J]. IEEE Transactions on Information Technology in Biomedicine, 2010, 14(2):436-446.
[17] Hausdorff JM, Balash Y, Giladi N. Time series analysis of leg movements during freezing of gait in Parkinson's disease: akinesia, rhyme or reason?[J]. Physica A Statistical Mechanics &its Applications, 2003, 321(3-4):565-570.
[18] Popovic MB, Djuric-Jovicic M, Radovanovic S, et al. A simple method to assess freezing of gait in Parkinson's disease patients[J]. Brazilian Journal of Medical And Biological Research, 2010, 43(9):883-889.
[19] Mazilu S, Hardegger M, Zhu Z, et al. Online detection of freezing of gait with smartphones and machine learning techniques[C]//2012 6th International Conference on Pervasive Computing Technologies for Health Care and Workshops. San Diego: IEEE, 2012:123-130.
[20] Tripoliti EE, Tzallas AT, Tsipouras MG, et al. Automatic detection of freezing of gait events in patients with Parkinson's disease[J]. Computer Methods & Programs in Biomedicine, 2012, 110(1):12-26.
[21] Saad A, Zaarour I, Guerin F, et al. Detection of freezing of gait for Parkinson's disease patients with multi-sensor device and Gaussian neural networks[J]. International Journal of Machine Learning & Cybernetics, 26 Dec 2015:1-14[Epub ahead of print].
[22] 孙涛, 吴海丰, 梁志刚,等. SMOTE算法在不平衡数据中的应用[J]. 北京生物医学工程, 2012,1(5):528-530.
[23] Sánchez-Gutiérrez ME, Albornoz EM, Martinez-Licona F, et al. Deep learning for emotional speech recognition[C]//Proceedings of 6th Mexican Conference on Pattern Recognition. Cancun: Springer, 2014. 311-320
[24] Zhao Yue, Xu Yan,WangHui, et al. Cross-language transfer speech recognition using deep learning [C]//2014 11th IEEE International Conference on Control & Automation (ICCA 2014).Taichung:IEEE,2014:1422-1426.
[25] Yan Zhennan, Zhan Yiqiang, PengZhigang, et al. Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition.[J]. IEEE Transactions on Medical Imaging, 2016,35(5):1332-1343.
[26] Druzhkov PN, Kustikova VD. A survey of deep learning methods and software tools for image classification and object detection[J]. Pattern Recognition & Image Analysis, 2016, 26(1):9-15.