帕金森病冻结步态的实时监测系统
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摘要
目的由于帕金森病冻结步态的突发性,临床上对其进行评估存在一定困难,为此本文研究了一种用于实时监测冻结步态的系统。方法该系统由可穿戴设备和配套的APP两部分构成,其中设备通过惯性传感器和超声波传感器采集患者腿部运动的加速度和抬脚高度数据,并传输至APP软件中,通过软件中的冻结步态识别模型进行分析。系统为构建冻结步态识别模型,首先通过实验采集12位患者的运动数据,然后经过信号预处理、特征提取和机器学习算法训练出模型,最后通过对数据集采用十折交叉验证来评估模型的准确度和精确度。结果系统对冻结步态的识别准确率可达98.6%,精确率达97.2%。结论该系统能够实时监测帕金森病患者日常生活中的冻结步态发作情况,为医生的诊疗提供定量、可靠的参考依据。
Objective Due to the unexpected nature of freezing of gait( FOG) in Parkinson disease,there are some difficulties in clinical assessment. This paper investigates a system for real-time monitoring of freezing of gait in Parkinson disease. Methods The system consists of wearable device and supporting APP. The device collects the patient's acceleration and foot height data of the leg movement by inertial sensors and ultrasonic sensors. Then the data are transmitted to the APP software and analyzed by the FOG recognition model in the software. In order to construct the FOG recognition model,we first acquire the motion data of 12 patients through experiments and then train the model through signal preprocessing,feature extraction and machine learning algorithms. Finally,we use the ten-fold cross-validation of the data set to evaluate the accuracy and precision of the model. Results The identification accuracy of the system for freezing of gait reachs98.6% and the precision rate is 97.2%. Conclusions The system can monitor the situation of FOG in patients' daily life, which can provide a quantitative and reliable reference for the diagnosis and treatment.
Objective Due to the unexpected nature of freezing of gait( FOG) in Parkinson disease,there are some difficulties in clinical assessment. This paper investigates a system for real-time monitoring of freezing of gait in Parkinson disease. Methods The system consists of wearable device and supporting APP. The device collects the patient's acceleration and foot height data of the leg movement by inertial sensors and ultrasonic sensors. Then the data are transmitted to the APP software and analyzed by the FOG recognition model in the software. In order to construct the FOG recognition model,we first acquire the motion data of 12 patients through experiments and then train the model through signal preprocessing,feature extraction and machine learning algorithms. Finally,we use the ten-fold cross-validation of the data set to evaluate the accuracy and precision of the model. Results The identification accuracy of the system for freezing of gait reachs98.6% and the precision rate is 97.2%. Conclusions The system can monitor the situation of FOG in patients' daily life, which can provide a quantitative and reliable reference for the diagnosis and treatment.
引文
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[10] Cole BT,Roy SH,Nawab SH. Detecting freezing-of-gait during unscripted and unconstrained activity[C]//33rd Annual International Conference of the IEEE EMBS. Boston,USA:IEEE Press,2011:5649-5652.
[11]王金甲,刘青玉,陈浩.基于深度卷积神经网络的帕金森步态识别[J].中国生物医学工程学报,2017,36(4):418-425.Wang JJ,Liu QY,Chen H. 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.
[12] Mazilu S,Hardegger M,Zhu Z,et al. Online detection of freezing of gait with smartphones and machine learning techniques[C]//International Conference on Pervasive Computing Technologies for Healthcare. San Diego, USA:IEEE Press,2012:123-130.
[13] Moore ST,Macdougall HG,Ondo WG. Ambulatory monitoring of freezing of gait in Parkinson’s disease[J]. Journal of Neuroscience Methods,2008,167(2):340-348.
[14]安文瑞,贺娟,丛日博,等.帕金森病早期临床表现及影像学诊断研究进展[J].中国医药导报,2007,14(5):35-38.An WR,He J,Cong RB,et al. Research progress of early clinical manifestation and imaging diagnosis of Pakinson disease[J].China Medical Herald,2017,14(5):35-38.
[15] Djuric-Jovicic MD, Jovicic NS, Radovanovic SM, et al.Automatic identification and classification of freezing of gait episodes in Parkinson’s disease patients[J]. IEEE Transactions on Neural Systems&Rehabilitation Engineering,2014,22(3):685-694.
[2]何安琪,张煜,刘振国.帕金森病步态障碍临床特点及研究进展[J].中华神经科杂志,2016,49(4):324-327.He AQ,Zhang Y,Liu ZG. Clinical characteristics and research progress of gait disorder in Parkinson disease[J]. Chinese Journal of Neurology,2016,49(4):324-327.
[3]李利,刘晶,章文斌.帕金森病冻结步态治疗进展[J].临床神经外科杂志,2014,11(4):318-320.Li L,Liu J,Zhang WB,et al. Treatment progress of freezing of gait in Parkinson’s disease[J]. Journal of Clinical Neurosurgery,2014,11(4):318-320.
[4]蒋思明,张克忠.帕金森病冻结步态的研究进展[J].中国临床神经科学,2015,23(2):199-204.Jiang SM,Zhang KZ. Research progress of freezing of gait in Parkinson’s disease[J]. Chinese Journal of Clinical Neurosciences,2015,23(2):199-204.
[5] Giladi N,Shabtai H,Simon ES,et al. Construction of freezing of gait questionnaire for patients with Parkinsonism[J].Parkinsonism&Related Disorders,2000,6(3):165-170.
[6] Nieuwboer A,Rochester L,Herman T,et al. Reliability of the new freezing of gait questionnaire:agreement between patients with Parkinson’s disease and their carers[J]. Gait&Posture,2009,30(4):459-463.
[7] 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,2013,110(1):12-26.
[8] Coste CA,Sijobert B,Pissardgibollet R,et al. Detection of freezing of gait in Parkinson disease:preliminary results[J].Sensors,2014,14(4):6819-6827.
[9] 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&Biological Research,2010,43(9):883-889.
[10] Cole BT,Roy SH,Nawab SH. Detecting freezing-of-gait during unscripted and unconstrained activity[C]//33rd Annual International Conference of the IEEE EMBS. Boston,USA:IEEE Press,2011:5649-5652.
[11]王金甲,刘青玉,陈浩.基于深度卷积神经网络的帕金森步态识别[J].中国生物医学工程学报,2017,36(4):418-425.Wang JJ,Liu QY,Chen H. 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.
[12] Mazilu S,Hardegger M,Zhu Z,et al. Online detection of freezing of gait with smartphones and machine learning techniques[C]//International Conference on Pervasive Computing Technologies for Healthcare. San Diego, USA:IEEE Press,2012:123-130.
[13] Moore ST,Macdougall HG,Ondo WG. Ambulatory monitoring of freezing of gait in Parkinson’s disease[J]. Journal of Neuroscience Methods,2008,167(2):340-348.
[14]安文瑞,贺娟,丛日博,等.帕金森病早期临床表现及影像学诊断研究进展[J].中国医药导报,2007,14(5):35-38.An WR,He J,Cong RB,et al. Research progress of early clinical manifestation and imaging diagnosis of Pakinson disease[J].China Medical Herald,2017,14(5):35-38.
[15] Djuric-Jovicic MD, Jovicic NS, Radovanovic SM, et al.Automatic identification and classification of freezing of gait episodes in Parkinson’s disease patients[J]. IEEE Transactions on Neural Systems&Rehabilitation Engineering,2014,22(3):685-694.