基于ERD/ERS脑电信号的智能化功能电刺激系统的设计

设计一套基于事件相关同步电位(Event related desynchron ization,ERD)和事件相关去同步电位(Event related synchronization,ERS)脑电信号反馈控制功能电刺激仪系统。当大脑想象残肢运动中,出现ERD/ERS脑电信号,经过特征提取和特征分类转换为控制命令去触发功能电刺激系统,实现对相应残肢的电刺激。实验结果成功实现了对残肢的电刺激。     

脑机接口控制的下肢功能性电刺激系统研究

研发基于脑机接口控制的功能性电刺激系统,服务于下肢的运动康复。脑机接口采用的是基于稳态视觉诱发电位的脑电信号技术。使用线性判别分析分类器来处理脑电信号的频域特征,实现对下肢5种运动状态的控制意图识别,即开始、快速、慢速、停止和空闲状态。识别的意图转化为指令触发电刺激系统,刺激下肢的相关肌肉产生运动,并测量关节角度。设计的系统在6位正常受试者上进行了单纯的脑机接口实验,其中2位分别进行了脑机接口控制的小腿摆动与下肢行走的电刺激实验。在小腿摆动实验中刺激的是股直肌,行走运动实验中刺激的是两条腿的髂腰肌、臀大肌、股直肌和腘绳肌。实验分析了电刺激尾迹对脑电信号的影响,结果表明设计的脑机接口可以准确地识别运动意图(平均识别率高于85%),并能够实现电刺激作用下与该意图相对应的下肢期望运动。     

经穴位电刺激下的表面肌电信号特征分析

目的:探究人体穴位电刺激与相关肌肉活动的关系,通过对经穴刺激诱发的表面肌电信号进行特征分析,阐明经穴电刺激下的肌电信号时域、频域特征参数变化与肌肉的激活程度、疲劳程度之间的定量关系,为康复工程和运动医学研究提供参考依据。方法:以8名健康青年志愿者为研究对象,设计穴位电刺激和自主收缩两种实验模式,采集记录两种模式下对应的指伸肌和尺侧腕屈肌的表面肌电信号,提取表面肌电信号均方根值、平均功率频率、中值频率等特征值,并分析其功率谱变化。通过配对t检验,分析经穴电刺激和自主动作模式的肌电信号特征差异。结果:在经穴电刺激模式下,对应的表面肌电信号均方根幅度增大,且经穴电刺激后,肌电信号的谱分布向高频方向移动,肌电的平均功率频率和中值频率随着刺激时间的延长保持稳定。结论:表明对曲池穴和内关穴位电刺激,能够激活对应的指伸肌和尺侧腕屈肌更多的快肌纤维,使肌肉激活程度提高,对缓解肌肉疲劳具有一定作用。     

基于认知再学习疗法的脑卒中康复治疗系统的设计及临床试验

针对脑卒中患者运动功能康复问题,提出认知再学习疗法、肌电视觉反馈疗法和肌电触发神经肌肉电刺激疗法三者结合的运动功能康复复合疗法,开发了一种基于嵌入式技术的脑卒中康复治疗系统以实现复合疗法。以Advanced RISC Machines (ARM) 9处理器S3C2440为控制核心,利用sEMG采集电路实现对脑卒中患者患肢肌电信号实时采集,并通过触摸显示模块实现肌电信号可视化,同时将采集到的信号与所设定的阈值相比较,根据比较的结果决定是否触发NMES刺激电路以实现有效的肌肉电刺激。软件设计运用Linux+QT平台开发,实现肌电参数的处理和显示,以及刺激参数的设置。采用脑卒中康复治疗系统对急性脑卒中患者(n=8)进行上肢运动功能治疗,试验结果表明接受系统康复治疗的试验组FMA上肢评分高于对照组(n=8, P<0.05),肌力分级水平也得到一定的提高。脑卒中康复治疗系统性能稳定,可显著改善脑卒中患者运动功能障碍。     

表面肌电信号实时反馈式电刺激康复训练系统的设计

为使脑卒中偏瘫病人在早期康复训练和护理中得到更加有效更加安全的治疗手段,该文提出一种基于皮肤表面肌电信号(s EMG)作为反馈的功能性电刺激康复治疗仪系统。系统中包含皮肤表面肌电信号拾取电路、刺激信号发生电路、残余电荷释放电路(用于释放刺激堆积在人体组织和电极上的刺激信号残余电荷,降低刺激信号对肌电信号的干扰)和STM32单片机控制电路。该系统是一种以皮肤表面肌电信号作为反馈的功能性电刺激康复训练系统,具有较高有效性和安全性。在脑卒中偏瘫患者早期康复训练中起着重要的作用,拥有前景广阔的医疗应用市场。     

肌电反馈磁场治疗仪的研制

目的：研制可用于神经损伤治疗的肌电反馈磁场治疗仪器。方法：采用自动控制理论和电磁场理论将肌电传感器采集磁场作用后的人体肌电信号作为反馈信号，利用单片机进行磁场强度以及波形的自动调整。结果：对所研制的肌电反馈式磁场治疗仪进行测试，可产生磁感应强度0～50mT、频率1—100Hz的正弦、方波和三角波；已经用于刺激动物神经的实验研究，得到了许多有意义的研究结果。结论：肌电反馈磁场治疗仪的研制将提供患者个体差异的自适应治疗，确保了最佳的临床治疗效果。     

脑电生物反馈系统设计

脑电生物反馈仪是治疗儿童注意缺陷多动症很有效的工具.本文介绍了一套用于治疗注意缺陷多动症的脑电生物反馈仪的设计方法.该系统选用了已商品化的LQWY-N型脑电信号采集仪采集脑电数据,通过USB接口将数据传输给电脑进行信号处理,并在此基础上开发了系统软件,实现了脑电生物反馈功能.经过测试证实该系统具有很强的稳定性和可靠性,近期有望进一步用于临床实验.     

基于低频电刺激诱发表面肌电信号的肌肉疲劳度研究

目的 通过对肌肉疲劳过程中非诱发表面肌电(surface electromyography,sEMG)信号和诱发表面肌电信号的研究分析,寻找有效评价肌肉疲劳的分析测量方法.方法 对7名受试者进行自主运动和电刺激两种致肌疲劳的实验,并在两组实验中分别记录电刺激诱发与非诱发肌电信号,然后对每组信号进行傅里叶变换求取功率谱和近似熵.结果 随着疲劳的产生,两组实验诱发信号的频谱曲线左移效果优于非诱发信号,近似熵分析中电刺激组诱发信号出现先上升后下降的变化,自主运动组诱发信号则呈现单调递减的趋势.结论 低频电刺激诱发表面肌电信号更适于测量肌疲劳的动态变化.相对于传统功率谱,近似熵分析方法更适于处理电刺激诱发的表面肌电信号.     

肌电生物反馈的非线性机制

目的探讨肌电生物反馈中肌电与脑电活动间的相关联系及其机制。方法动态同步采集肌电生物反馈中肌电和脑电信号后,在评价肌电幅值和频率的基础上,利用非线性动力学参数——近似熵(ApEn)和互近似熵(Cross-ApEn),分析肌电信号内部以及肌电-脑电信号间的非线性改变。结果随生物反馈次数的增加,对照组及生物反馈组实验前后肌电振幅的最大值、最小值和平均值都明显降低(F=3.85~25.59,P<0.05),生物反馈组实验前及实验后肌电频率明显上升(F=6.71、8.67,P<0.05);同时,肌电信号的ApEn明显降低(F=5.42、2.81,P<0.05),肌电与脑电信号间的互近似熵也明显升高(F=13.77~19.52,P<0.05)。最后2次反馈中上述指标均明显不同于对照组(P<0.05)。结论肌电生物反馈中肌电变化的机理,可能与生物反馈加强了大脑的有意识的调控作用而减弱了大脑对下运动神经元-肌肉系统的非线性易化有关。     

多参数经皮神经肌肉电刺激仪的设计

目的：电刺激仪器是通过电流作用于目标组织，使目标组织产生相应的功能变化。刺激方式、刺激电流、波形等均会影响治疗效果。市面上现有的神经肌肉电刺激仪一般只有单一的治疗模式，局限性很大。为了有针对性地治疗不同肌肉疾病，我们设计了一个能够方便准确地控制刺激电流的多参数经皮神经肌肉电刺激仪系统：方法：设计了一个以STC12C5410AD单片机为核心控制芯片的多参数经皮神经肌肉电刺激仪系统，采用串口实现上下位机的通讯，通过软件编程，上位机发送波形、频率、脉宽、间隙时间、最大刺激电流等各项参数指令，由单片机控制D／A转换芯片DAC8532输出指定波形，经过高压开关保护电路加入恒流源电路，产生恒定的电流对目标组织进行刺激。系统加入多项安全控制措施，有效地避免了实际操作过程中的安全隐患；结果：通过对系统进行电阻测试，验证了系统为恒流型仪器，经过电阻的刺激电流由且仅由输入信号的各项参数确定，与电阻的大小无关。由临床试验证明了通过改变参数，可以改变刺激模式，并验证了该系统的安全性和有效性。结论：该系统能设置成临床上使用的经皮电神经刺激疗法的各种模式。安全有效，有利于临床实验和相关科研的开展。     

用于脑瘫儿下肢康复装置的表面肌电信号特征提取方法研究

采用时域、频域、时频域和熵的特征提取方法,找到适合脑瘫儿表面肌电信号的特征提取方法.通过在训练过程加一个阻力得到四个不同肌肉活性的训练阶段数据,对数据进行预处理和特征提取,然后用因子分析法对所提取的特征进行分析,实验结果显示本研究所提出的时域、时频域和熵特征部分适用于脑瘫儿,频域特征不适用于脑瘫儿.本研究结果对脑瘫儿的康复训练有很大的帮助.     

基于新型双输入卷积神经网络的肌电模式识别

针对双臂协同运动中蕴含的运动信息量大,难以充分解读且识别率不高的问题,提出一种新型的双输入卷积神经网络（ND-CNN）模型。首先,根据双臂运动的特点,分别设计数据整理和模型输入两种策略。然后,利用两个结构相同、参数共享的特征提取层提取信号本身的特征和信号之间的差别特征。最后,利用所提取的两类特征实现双臂协同动作的识别。在自主设计的双臂实验中,将ND-CNN与其余3种先进的神经网络对比。实验结果表明,本文所提的ND-CNN模型在识别精度和可靠性上优于其他网络模型,能够对双臂肌电动作有效识别。     

Spatio-spectral filters for low-density surface electromyographic signal classification

In this paper, we proposed to utilize a novel spatio-spectral filter, common spatio-spectral pattern (CSSP), to improve the classification accuracy in identifying intended motions based on low-density surface electromyography (EMG). Five able-bodied subjects and a transradial amputee participated in an experiment of eight-task wrist and hand motion recognition. Low-density (six channels) surface EMG signals were collected on forearms. Since surface EMG signals are contaminated by large amount of noises from various sources, the performance of the conventional time-domain feature extraction method is limited. The CSSP method is a classification-oriented optimal spatio-spectral filter, which is capable of separating discriminative information from noise and, thus, leads to better classification accuracy. The substantially improved classification accuracy of the CSSP method over the time-domain and other methods is observed in all five able-bodied subjects and verified via the cross-validation. The CSSP method can also achieve better classification accuracy in the amputee, which shows its potential use for functional prosthetic control.     

Feature extraction from Doppler ultrasound signals for automated diagnostic systems

This paper presented the assessment of feature extraction methods used in automated diagnosis of arterial diseases. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Different feature extraction methods were used to obtain feature vectors from ophthalmic and internal carotid arterial Doppler signals. In addition to this, the problem of selecting relevant features among the features available for the purpose of classification of Doppler signals was dealt with. Multilayer perceptron neural networks (MLPNNs) with different inputs (feature vectors) were used for diagnosis of ophthalmic and internal carotid arterial diseases. The assessment of feature extraction methods was performed by taking into consideration of performances of the MLPNNs. The performances of the MLPNNs were evaluated by the convergence rates (number of training epochs) and the total classification accuracies. Finally, some conclusions were drawn concerning the efficiency of discrete wavelet transform as a feature extraction method used for the diagnosis of ophthalmic and internal carotid arterial diseases.     

数字信号处理器在脑-机接口系统中的应用

本文研究数字信号处理器(DSP)在基于稳态视觉诱发电位的脑-机接口系统中的应用,同时详细介绍了系统的构成及各部分的设计与实现方法,并展示了初步的实验结果.本系统主要由刺激器、模拟放大电路、DSP核心电路和控制外设的红外发射电路组成.所选用的都是低功耗、高速的芯片,以满足实用性和实时性的要求.软件部分采用汇编语言编程,主要包括控制信号采集、50Hz陷波和带通滤波、快速傅立叶变换、特征提取和识别.经过测试,该系统不仅可以将输入的正弦波正确地检测出来,而且对于接入的实际脑电中的诱发响应也能很好地检测和识别.因此使用DSP使脑-机接口系统的小型化、实用化是可行的.     

Survival analysis for high-dimensional,heterogeneous medical data: Exploring feature extraction as an alternative to feature selection

BackgroundIn clinical research, the primary interest is often the time until occurrence of an adverse event, i.e., survival analysis. Its application to electronic health records is challenging for two main reasons: (1) patient records are comprised of high-dimensional feature vectors, and (2) feature vectors are a mix of categorical and real-valued features, which implies varying statistical properties among features. To learn from high-dimensional data, researchers can choose from a wide range of methods in the fields of feature selection and feature extraction. Whereas feature selection is well studied, little work focused on utilizing feature extraction techniques for survival analysis.ResultsWe investigate how well feature extraction methods can deal with features having varying statistical properties. In particular, we consider multiview spectral embedding algorithms, which specifically have been developed for these situations. We propose to use random survival forests to accurately determine local neighborhood relations from right censored survival data. We evaluated 10 combinations of feature extraction methods and 6 survival models with and without intrinsic feature selection in the context of survival analysis on 3 clinical datasets. Our results demonstrate that for small sample sizes – less than 500 patients – models with built-in feature selection (Cox model with ℓ₁ penalty, random survival forest, and gradient boosted models) outperform feature extraction methods by a median margin of 6.3% in concordance index (inter-quartile range: [−1.2 % ;14.6 %]).ConclusionsIf the number of samples is insufficient, feature extraction methods are unable to reliably identify the underlying manifold, which makes them of limited use in these situations. For large sample sizes – in our experiments, 2500 samples or more – feature extraction methods perform as well as feature selection methods.     

The application of a use case/task based approach in the development of software for a portable neuromuscular stimulator device

This paper describes the use of a use case/task based method in the development of a portable neuromuscular stimulator device. The developed unit allows a variety of stimulus delivery algorithms to be incorporated dependent on the patient's requirements. The developed system consists of a stimulator unit, stimulator firmware, external sensors, a programmer unit, two stimulation channels and electrodes. A clinician specifies a suitable algorithm for a particular patient and then selects the relevant stimulus parameters for that algorithm using the programmer unit. The stimulator unit's architecture supports the addition of future algorithms. The device was developed in accordance with the European Medical Devices Directive 93/42/EEC resulting in the need for a well-defined development lifecycle during the design and development of the neuromuscular stimulator. This development lifecycle must place emphasis on the need to identify potential hazards. Therefore, the adoption of a use case/task driven approach as one of the strategies in eliciting the requirements, both functional and non-functional and specification stages of the development lifecycle resulted in a more rigid hazard/risk analysis leading ultimately to a more robust final system. A comprehensive review of the literature has revealed that use cases have been in use in other contexts but not so in a biomedical context. Therefore, this is a novel strategy to the development of a device in this field. A brief background on the historical development of drop foot stimulators shall be presented thereby displaying the benefits of the programmability feature of our stimulator.     

基于经验模态分解-小波包变换的表面肌电信号手势识别

为了提高表面肌电信号（sEMG）手部运动识别的正确率,比较常规的sEMG预处理和特征提取方法,提出一种基于经验模态分解（EMD）和小波包变换（WPT）的sEMG手势识别模型。首先,使用EMD方法将sEMG进行平稳化,得到一系列的固有模态函数。其次,求取各个固有模态函数与原始信号的相关性,选取相关性较高的前4个分量作为有效分量。然后,采用Db3小波函数进行WPT,提取小波包系数中的平均能量、平均绝对值、最大值、均方根和方差等特征。分别采用线性判别分析和支持向量机对12种手部运动进行模式识别。结果表明基于EMD和WPT的sEMG手势识别正确率比直接提取小波包系数中的特征识别正确率高。     

一种基于表面肌电信号映射人体下肢运动意图的方法

智能膝关节假肢是截肢患者恢复日常运动的重要辅具。对人体下肢运动意图的识别是实现下肢假肢控制的关键。该文针对此问题,提出了一种通过表面肌电信号预测膝关节角度的方法。对表面肌电提取时域特征,通过 BP 神经网络模型建立平地行走过程中表面肌电信号和膝关节角度的映射关系,预测膝关节角度。     

纹理分割中的特征提取

本文对纹理分割中的特征提取方法做了系统的比较和分析。重点介绍流行的Gibbs随机场的特征提取方法,并分析其主要面临的困难,最后我们提出一种有效的解决方案。