交感神经兴奋与树突状细胞活化加剧脂多糖诱导大鼠心肌损伤

目的探讨交感系统兴奋及树突状细胞(DC)活化在脂多糖(LPS)诱导大鼠心肌损伤中的作用。方法将大鼠随机分为4组:对照组、LPS组(腹腔注射LPS:10 mg/kg)、阿替洛尔(Ate)干预组(LPS造模后给予β受体阻断剂阿替洛尔5 mg/kg)和VAG539组(LPS造模后给予DC抑制剂VAG539 30 mg/kg灌胃,每天2次,共2 d)。用Powerlab系统记录交感神经放电和血流动力学指标;高效液相色谱(HPLC)测定血浆中去甲肾上腺素(NE)含量;免疫组化检测心肌中TNF-α及DC含量。结果与对照组相比,经LPS诱导24 h的3组大鼠血浆NE水平明显升高(P0.05),心肌TNF-α水平和DC含量明显升高(P0.05),肾交感神经放电活动明显增加(P0.05);与LPS组比较,Ate干预组或VAG539干预组NE水平明显降低(P0.05),心肌细胞TNF-α水平和DC含量明显降低(P0.05),肾交感神经放电活动明显降低(P0.05)。结论交感神经系统的过度兴奋及DC激活加剧LPS致大鼠心肌损伤。     

股四头肌V-Y成形技术在僵直或强直膝TKA术中的应用

目的 探讨僵直或强直膝全膝关节置换术(TKA)中采用股四头肌V-Y成形技术改善膝关节活动度的可行性及有效性.方法 回顾性分析自2014年4月至2018年4月宁德师范学院附属宁德市医院采用TKA治疗11例(13膝)僵直或强直膝患者资料,术中采用股四头肌V-Y成形技术,比较分析手术前后膝关节HSS评分和关节活动度变化.结果...     

基于不同特征参数的脑电信号分类

分别以自回归(autoregression,AR)模型系数、相关系数和信息熵作为信号特征对不同思维作业脑电(EEG)信号进行分类,其中相关系数和信息熵均是首次用于思维作业EEG信号的特征提取.实验结果显示,采用信息熵作为EEG信号特征的分类准确率总体上明显高于采用另两种特征参数,且受提取特征的数据分段长度的影响最小,有利于提高基于思维作业实时脑- 机接口的通信准确度和速率.同时,研究结果也进一步证实了高频信息可用于EEG的分类.     

低场MRI对膝关节半月板损伤诊断的临床价值

目的 探讨低场MRI在膝关节半月板损伤中的诊断价值.材料和方法 搜集我院2007年2月-6月间,进行MRI检查的141例151个膝关节,进行半月板损伤诊断的回顾性分析.检查序列包括冠状面FSET2WI、矢状面SET1WI、FSET2WI及STIRE.结果低场MRI显示正常半月板内可以见似三角型或片状高信号,不延伸至关节面或游离缘,临床症状不明显;损伤半月板除信号改变外,其形态可全部或局部不规则,常同时伴关节组成骨及关节软骨、滑膜囊、软组织等的异常信号,临床症状明显.本组资料中,MRI诊断半月板损伤130个,14个膝关节外院行关节镜检查,12个证实有半月板撕裂.低场MRI对半月板撕裂的诊断率85.7%.结论 低场MRI能较好的显示膝关节半月板损伤,能对其进行损伤分级,显示损伤的特点和严重程度,为临床选择合适的治疗方案提供依据.     

基于肌电信号的膝关节跨越障碍角度预测方法

为解决人体跨越障碍物时膝关节角度输出的问题,针对性设计一种穿戴式信号获取实验台,对下肢运动姿态进行运动分析,将肌肉电信号及关节角度信号作为运动数据,对信号进行处理后利用BP神经网络预测跨越障碍时输出角度,提出一种利用BP神经网络算法,根据不同大腿抬起高度,分析膝关节运动主动肌与被动肌发力程度,预测输出人体跨越障碍时膝关节角度的方法,能够有效帮助假肢膝关节或康复机器人实现跨越障碍的复杂动作。     

肌骨超声对类风湿性指关节炎的病理特征及疾病严重程度的诊断价值分析

目的:探讨肌骨超声评价类风湿性关节炎(Rheumatoid arthritis,RA)的病理特征及疾病严重程度的临床意义.方法:回顾性分析2020年1月至2022年6月我院收治的90例RA患者(113个受累膝关节)的临床资料,所有患者均于入院后接受肌骨超声(Musculoskeletal ultrasound,MSUS)和核磁共振成像(Magnetic Resonance Imaging,MRI)检查,以关节镜检查结果为"金标准".根据28关节活动性评分评估患者病情,将患者分为重度活动组、中度活动组、轻度活动组及缓解组.比较两种检查方式对RA的检出率和诊断价值.分析MSUS征象、血流信号与疾病严重程度的相关性.结果:MSUS共检出受累膝关节101个,准确度为89.38％;MRI共检出受累膝关节100个,准确度为88.50％.MSUS对关节积液的检出率高于MRI(P<0.05).MSUS和MRI诊断RA的敏感度、特异度、准确率、Kappa值均较高.不同活动性滑膜厚度、关节腔积液深度、骨侵蚀程度评分及血流信号分级比较,重度活动组>中度活动期>轻度活动组>缓解组(P<0.05).结论:MSUS和MRI对RA均具有较高的诊断价值,但MSUS应用价值更高,MSUS征象和血流信号与RA疾病严重程度联系密切.     

膝关节交叉韧带血供的研究进展

膝关节前交叉韧带(ACL)及后交叉韧带(PCL)的损伤,在膝关节外伤中较为常见,常可导致膝关节功能受限,膝关节不稳,严重者可致退行性膝关节疾病.     

Unsupervised spike detection and sorting with mathematical morphology and kernel principal components analysis

目的 为抑制高强度背景噪声及信号叠加的干扰,提高峰电位的检出率和分类的正确性,本文提出一种新的无监督方法.方法 首先,应用数学形态学的复合操作对信号进行降噪,采用定阈值提取峰电位.然后,小波变换和核主成分分析法(kernel principal components analysis,KPCA)相结合,对已提取的峰电位波形进行特征提取.最后,用改进的最小距离法实现峰电位分类.结果 仿真实验结果表明,此方法对于不同噪声强度的信号,峰电位检出率达94%,总分类正确率91%以上,其中大量叠加信号的分类正确率88%以上.结论 本方法能在有效抑制噪声的基础上,准确提取峰电位并有效分类.     

EMD和SVM结合的脑电信号分类方法

脑电(EEG)癫痫波的自动检测与分类在临床医学上具有重要意义。针对EEG信号的非平稳特点,本文提出了一种基于经验模式分解(EMD)和支持向量机(SVM)的EEG分类方法。首先利用EMD将EEG信号分成多个经验模式分量,然后提取有效特征,最后用SVM对EEG信号进行分类。结果表明,该方法对癫痫发作间歇期和发作期EEG的分类效果比较理想,识别率达到99%。     

灰阶联合能量多普勒膝关节检查对类风湿性关节炎活动度的评估

目的探讨灰阶联合能量多普勒膝关节检查对类风湿性关节炎活动度评估价值。方法选取类风湿性关节炎患者80例160个膝关节作为观察组,抽取80例健康体检者160个膝关节作为对照组。均进行灰阶联合能量多普勒膝关节检查,比较两组关节的化膜厚度以及内部血流信号的分级。结果 (1)观察组的膝关节滑膜厚度为(3.79±0.63)mm,明显厚于对照组的(1.50±0.20)mm,组间差异显著,存在统计学意义(P0.05)。(2)观察组的0级所占的比例为78.75%,明显高于对照组的3.125%;其2级和3级所占比例0,均低于对照组的43.75%和31.25%,组间差异均具有统计学意义(P0.05)。结论灰阶联合能量多普勒膝关节检查对类风湿性关节炎活动度的评估具有良好的应用价值。     

Adaptive filtering, modelling and classification of knee joint vibroarthrographic signals for non-invasive diagnosis of articular cartilage pathology

Interpretation of vibrations or sound signals emitted from the patellofemoral joint during movement of the knee, also known as vibroarthrography (VAG), could lead to a safe, objective, and non-invasive clinical tool for early detection, localisation, and quantification of articular cartilage disorders. In this study with a reasonably large database of VAG signals of 90 human knee joints (51 normal and 39 abnormal), a new technique for adaptive segmentation based on the recursive least squares lattice (RLSL) algorithm was developed to segment the non-stationary VAG signals into locally-stationary components; the stationary components were then modelled autoregressively, using the Burg-Lattice method. Logistic classification of the primary VAG signals into normal and abnormal signals (with no restriction on the type of cartilage pathology) using only the AR coefficients as discriminant features provided an accuracy of 68.9% with the leave-one-out method. When the abnormal signals were restricted to chondromalacia patella only, the classification accuracy rate increased to 84.5%. The effects of muscle contraction interference (MCI) on VAG signals were analysed using signals from 53 subjects (32 normal and 21 abnormal), and it was found that adaptive filtering of the MCI from the primary VAG signals did not improve the classification accuracy rate. The results indicate that VAG is a potential diagnostic tool for screening for chondromalacia patella.     

Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions

Externally detected vibroarthrographic (VAG) signals bear diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces of the knee joint. Analysis of VAG signals could provide quantitative indices for noninvasive diagnosis of articular cartilage breakdown and staging of osteoarthritis. We propose the use of statistical parameters of VAG signals, including the form factor involving the variance of the signal and its derivatives, skewness, kurtosis, and entropy, to classify VAG signals as normal or abnormal. With a database of 89 VAG signals, screening efficiency of up to 0.82 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial basis functions.     

基于极限梯度提升和深度神经网络共同决策的心音分类方法

心音是诊断心血管疾病常用的医学信号之一。本文对心音正常/异常的二分类问题进行了研究,提出了一种基于极限梯度提升(XGBoost)和深度神经网络共同决策的心音分类算法,实现了对特征的选择和模型准确率的进一步提升。首先,本文对预处理后的心音信号进行心音分割,在此基础上提取了5个大类的特征,前4类特征采用递归特征消除法进行特征选择,作为XGBoost分类器的输入,最后一类为梅尔频率倒谱系数(MFCC),作为长短时记忆网络(LSTM)的输入。考虑到数据集的不平衡性,本文在两种分类器中皆使用了加权改进的方法。最后采用异质集成决策方法得到预测结果。将本文所提心音分类算法应用于PhysioNet网站在2016年发起的PhysioNet心脏病学挑战赛(CINC)所用公开心音数据库,以测试灵敏度、特异性、修正后的准确率以及F得分,结果分别为93%、89.4%、91.2%、91.3%,通过与其他研究者应用机器学习、卷积神经网络(CNN)等方法的结果比较,在准确率和灵敏度上有明显提高,证明了本文方法能有效地提高心音信号分类的准确性,在部分心血管疾病的临床辅助诊断应用中有很大的潜力。     

融合CNN和BiLSTM的心律失常心拍分类模型

为更加准确地从动态心电中提取异常心拍,设计一种融合卷积神经网络(CNN)和多层双边长短时记忆网络(BiLSTM)的心律失常心拍分类模型。心电信号首先被分割成0.75 s和4 s两种不同尺度大小的心拍信号,然后利用11层CNN网络和3层BiLSTM网络分别对小/大尺度心拍信号进行特征提取与合并,并使用3层全连接网络对合并特征进行降维,最后利用softmax函数实现分类。针对MIT心律失常数据库异常心拍类型分布不均衡的问题,采用添加随机运动噪声和基线漂移噪声的样本扩展方法,降低模型的过拟合。采用基于患者的5折交叉检验进行模型验证。MIT心律失常数据库116 000个心拍的分类结果表明:所建立的模型针对4类心拍(正常、房性早搏、室性早搏、未分类)的识别准确率为90.42%,比单独使用CNN(76.45%)和BiLSTM(83.28%)的模型分别提高13.97%和7.14%。所提出的融合CNN和BiLSTM的心律失常心拍分类模型,相比单一基于CNN模型或者BiLSTM模型的机器学习算法,有更好的异常心拍分类准确率。     

Analysis of Vibroarthrographic Signals with Features Related to Signal Variability and Radial-Basis Functions

Knee-joint sounds or vibroarthrographic (VAG) signals contain diagnostic information related to the roughness, softening, breakdown, or the state of lubrication of the articular cartilage surfaces. Objective analysis of VAG signals provides features for pattern analysis, classification, and noninvasive diagnosis of knee-joint pathology of various types. We propose parameters related to signal variability for the analysis of VAG signals, including an adaptive turns count and the variance of the mean-squared value computed during extension, flexion, and a full swing cycle of the leg, for the purpose of classification as normal or abnormal, that is, screening. With a database of 89 VAG signals, screening efficiency of up to 0.8570 was achieved, in terms of the area under the receiver operating characteristics curve, using a neural network classifier based on radial-basis functions, with all of the six proposed features. Using techniques for feature selection, the turns counts for the flexion and extension parts of the VAG signals were chosen as the top two features, leading to an improved screening efficiency of 0.9174. The proposed methods could lead to objective criteria for improved selection of patients for clinical procedures and reduce healthcare costs.     

Computer-aided diagnosis of knee-joint disorders via vibroarthrographic signal analysis: a review

The knee is the lower-extremity joint that supports nearly the entire weight of the human body. It is susceptible to osteoarthritis and other knee-joint disorders caused by degeneration or loss of articular cartilage. The detection of a knee-joint abnormality at an early stage is important, because it helps increase therapeutic options that may slow down the degenerative process. Imaging-based arthrographic modalities can provide anatomical images of the joint cartilage surfaces, but fail to demonstrate the functional integrity of the cartilage. Knee-joint auscultation, by means of recording the vibroarthrographic (VAG) signal during bending motion of a knee, could be used to develop a noninvasive diagnostic tool. Computer-aided analysis of VAG signals could provide quantitative indices for screening of degenerative conditions of the cartilage surface and staging of osteoarthritis. In addition, the diagnosis of knee-joint pathology by means of VAG signal analysis may reduce the number of semi-invasive diagnostic arthroscopic examinations. This article reviews studies related to VAG signal analysis, first summarizing the pilot studies that demonstrated the diagnostic potential of knee-joint auscultation for the detection of degenerative diseases, and then describing the details of recent progress in analysis of VAG signals using temporal analysis, frequency-domain analysis, time-frequency analysis, and statistical modeling. The decision-making methods used in the related studies are summarized, followed by a comparison of the diagnostic performance achieved by different pattern classifiers. The final section is a perspective on the future and further development of VAG signal analysis.     

Representation of fluctuation features in pathological knee joint vibroarthrographic signals using kernel density modeling method

This article applies advanced signal processing and computational methods to study the subtle fluctuations in knee joint vibroarthrographic (VAG) signals. Two new features are extracted to characterize the fluctuations of VAG signals. The fractal scaling index parameter is computed using the detrended fluctuation analysis algorithm to describe the fluctuations associated with intrinsic correlations in the VAG signal. The averaged envelope amplitude feature measures the difference between the upper and lower envelopes averaged over an entire VAG signal. Statistical analysis with the Kolmogorov–Smirnov test indicates that both of the fractal scaling index (p = 0.0001) and averaged envelope amplitude (p = 0.0001) features are significantly different between the normal and pathological signal groups. The bivariate Gaussian kernels are utilized for modeling the densities of normal and pathological signals in the two-dimensional feature space. Based on the feature densities estimated, the Bayesian decision rule makes better signal classifications than the least-squares support vector machine, with the overall classification accuracy of 88% and the area of 0.957 under the receiver operating characteristic (ROC) curve. Such VAG signal classification results are better than those reported in the state-of-the-art literature. The fluctuation features of VAG signals developed in the present study can provide useful information on the pathological conditions of degenerative knee joints. Classification results demonstrate the effectiveness of the kernel feature density modeling method for computer-aided VAG signal analysis.     

Automated EEG signal analysis for identification of epilepsy seizures and brain tumour

Abstract

Electroencephalography (EEG) is a clinical test which records neuro-electrical activities generated by brain structures. EEG test results used to monitor brain diseases such as epilepsy seizure, brain tumours, toxic encephalopathies infections and cerebrovascular disorders. Due to the extreme variation in the EEG morphologies, manual analysis of the EEG signal is laborious, time consuming and requires skilled interpreters, who by the nature of the task are prone to subjective judegment and error. Further, manual analysis of the EEG results often fails to detect and uncover subtle features. This paper proposes an automated EEG analysis method by combining digital signal processing and neural network techniques, which will remove error and subjectivity associated with manual analysis and identifies the existence of epilepsy seizure and brain tumour diseases. The system uses multi-wavelet transform for feature extraction in which an input EEG signal is decomposed in a sub-signal. Irregularities and unpredictable fluctuations present in the decomposed signal are measured using approximate entropy. A feed-forward neural network is used to classify the EEG signal as a normal, epilepsy or brain tumour signal. The proposed technique is implemented and tested on data of 500 EEG signals for each disease. Results are promising, with classification accuracy of 98% for normal, 93% for epilepsy and 87% for brain tumour. Along with classification, the paper also highlights the EEG abnormalities associated with brain tumour and epilepsy seizure.     

基于CNN算法的稳态体感诱发电位的特征识别

脑-机接口研究可为瘫痪病人的康复带来一种新的治疗方法。已有研究表明对手指或者正中神经施加一定频率的体感刺激,会引发相同频率且具有空间特异性的稳态体感诱发电位。为优化基于稳态体感诱发电位的脑-机接口的性能,通过快速傅里叶变换寻找12个健康被试的个人左手特定共振频率,采用事件相关谱扰动进行时频分析,检测其稳态体感诱发电位信号。基于共振频率对实验诱发的脑电信号进行1 Hz带通滤波,获得特定频带的数据,采用卷积神经网络(CNN)学习算法对其进行分类,并与采用共空间模式和支持向量机的特征提取及特征分类的方法(CSP+SVM)进行比较。所有被试的结果显示:基于共振频率滤波方法,采用CNN学习算法获得的离线分类准确率均高于85%,并且CNN学习算法的分类准确率显著性优于CSP+SVM的分类准确率(91.8%±5.9% vs 77.4%±8.5%,P<0.05)。因此,在基于稳态体感诱发电位的脑机接口的特征识别中,CNN学习算法相比传统使用的机器学习分类算法(如共空间模式+支持向量机)能够显著提升分类准确率,提高脑机接口的整体性能。