基于粒子群和反向传播神经网络的近红外光无创血糖检测方法研究EI北大核心CSCD

现有的近红外无创血糖检测模型研究大多数关注的是近红外吸光度与血糖浓度之间的关系,但没有考虑人体生理状态对血糖浓度的影响。为了提升血糖预测模型性能,本文采用了粒子群优化算法(PSO)对反向传播(BP)神经网络的结构参数进行训练,并引入了收缩压、脉率、体温以及1550 nm吸光度作为血糖浓度预测模型的输入变量,采用BP神经网络作为预测模型。为解决传统BP神经网络容易陷入局部最优的问题,本文提出了一种基于PSO-BP的混合模型。结果表明,训练得到的PSO-BP模型预测效果优于传统的BP神经网络。十折交叉验证预测均方根误差和相关系数分别为0.95 mmol/L和0.74;克拉克误差网格分析结果表明,模型预测结果落入A区域的比例为84.39%,落入B区域的比例为15.61%,均满足临床要求。该模型可以快速地测量血糖浓度,且具相对较高的精度。     

An intelligent system for diagnosis of the heart valve diseases with wavelet packet neural networks

In this paper, an intelligent system is presented for interpretation of the Doppler signals of the heart valve diseases based on the pattern recognition. This paper especially deals with combination of the feature extraction and classification from measured Doppler signal waveforms at the heart valve using the Doppler Ultrasound. Because of this, a wavelet packet neural network model developed by us is used. The model consists of two layers: wavelet and multi-layer perceptron. The wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of wavelet packet decomposition and wavelet packet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the developed system has been evaluated in 215 samples. The test results showed that this system was effective in detecting Doppler heart sounds. The correct classification rate was about 94% for abnormal and normal subjects.     

Robust Nonlinear Autoregressive Moving Average Model Parameter Estimation Using Stochastic Recurrent Artificial Neural Networks

In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate model predictions. Furthermore, we compare the performance of the new approach to that of the deterministic recurrent neural network approach. Using this simple two-step procedure, we obtain more robust model predictions than with the deterministic recurrent neural network approach despite the presence of significant amounts of either dynamic or measurement noise in the output signal. The comparison between the deterministic and stochastic recurrent neural network approaches is furthered by applying both approaches to experimentally obtained renal blood pressure and flow signals. © 1999 Biomedical Engineering Society. PAC99: 8710+e, 8719Uv, 0705Mh     

基于人工神经网络的心脏功能评定研究

作者应用人工神经网络的方法,利用心脏收缩时间间期指标评定心脏功能。采用21个输入、3个输出和单个隐层的前馈网络,用反向传播算法进行训练。所用7个指标,将其进行编码后作为输入矢量,心脏功能分为3级,在人工神经网络学习由专家评定的结果后,对200位受试者的心脏功能进行评定。人工神经网络评定的正确率达93.5％,且具有自学习、容量扩充和较强的容错能力。     

基于一维卷积神经网络的糖尿病周围神经病变预测模型研究

为了实现对糖尿病周围神经病变(DPN)的早期预防,辅助医生进行早期诊断与决策,提出了一种基于一维卷积神经网络的DPN预测模型,对原始数据进行了一系列的预处理工作以提高数据的质量,此外数据集的特征维度较高,为了进一步提高预测模型的准确性,进行了主成分分析(PCA)降维处理,通过自主学习数据的特征信息,从中挖掘其有价值的医学信息与规律,来实现DPN的预测。通过支持向量机、BP神经网络和一维卷积神经网络分别建立了DPN预测模型。实验结果表明,一维卷积神经网络模型预测效果优于其他两个模型,其准确率、召回率、F1值、AUC值分别达到了0.983、0.916、0.923、0.98。     

机器学习在肺癌VMAT计划中对危及器官剂量预测的可行性

目的:探讨机器学习在肺癌容积旋转调强（VMAT）治疗计划对心脏和肺的剂量体积直方图（DVH）预测的可行性。方法:选取51例肺癌VMAT计划,随机选取其中43例为训练组,剩余8例为验证组。分析训练组中患者的解剖信息与两侧肺V5、V20和心脏V30、V40的相关性。采用机器学习方法,以解剖信息为输入、危及器官（OAR）的DVH为输出,分别构建并训练关于两侧肺以及心脏的人工神经网络模型。将验证组中8例VMAT计划中的解剖信息分别输入到已经构建好的人工神经网络模型,分别预测OAR的DVH。结果:两侧肺V5、V20和心脏V30、V40受自身体积大小影响可忽略,受OAR与靶区的空间相对位置关系影响较大。患侧肺、对侧肺、心脏的人工神经网络结构模型中隐藏层分别含有41、38、34个神经结点,线性回归系数分别为0.994、0.975、0.986。对验证组中患侧肺和对侧肺的V5、V20的预测误差分别为2.70%[±]1.83%、2.84[%±]1.97%和13.7%[±]7.8%、0.72[%±]0.75%,对心脏V30、V40的预测误差分别为3.20[%±]0.63%、2.1[%±]1.5%,仅对侧肺V5的预测值和实际值差异有统计学意义（P<0.05）。结论:采用人工神经网络方法可以对肺癌VMAT计划中解剖信息与OAR的DVH数据进行学习,构建的人工神经网络模型可预测出患侧肺、心脏V25[~]V60和对侧肺V20的DVH数据,可为临床计划设计提供参考。     

Prediction of forced expiratory volume in normal and restrictive respiratory functions using spirometry and self-organizing map

This work presents a prediction of forced expiratory volume in pulmonary function testing, using spirometry and neural networks. The pulmonary function data were recorded (n = 110) from volunteers using flow–volume spirometer with a standard acquisition protocol. From the recorded flow–volume curves, the acquired data are then used to predict forced expiratory volume in one second (FEV1) using a self-organizing map (SOM) and radial basis function neural networks. The SOM is used to determine the cluster centres of the hidden layer of radial basis function neural networks. The optimal widths of the Gaussian function of radial basis function neural networks were obtained from these centres and this network is then used to predict FEV1. The performance of the neural network model was evaluated by computing their prediction error statistics of average value, standard deviation, root mean square and their correlation with the true data for normal and abnormal cases. The correlation between measured and predicted values of FEV1 for normal subjects was found to be 0.9. The prediction error for normal subjects is lower than that of restrictive subjects. Results show that the adopted neural networks are capable of predicting FEV1 in both normal and abnormal cases.     

Prediction of influenza vaccination outcome by neural networks and logistic regression

The major challenge in influenza vaccination is to predict vaccine efficacy. The purpose of this study was to design a model to enable successful prediction of the outcome of influenza vaccination based on real historical medical data. A non-linear neural network approach was used, and its performance compared to logistic regression. The three neural network algorithms were tested: multilayer perceptron, radial basis and probabilistic in conjunction with parameter optimization and regularization techniques in order to create an influenza vaccination model that could be used for prediction purposes in the medical practice of primary health care physicians, where the vaccine is usually dispensed. The selection of input variables was based on a model of the vaccine strain which has frequently been changed and on which a poor influenza vaccine response is expected. The performance of models was measured by the average hit rate of negative and positive vaccine outcome. In order to test the generalization ability of the models, a 10-fold cross-validation procedure revealed that the model obtained by multilayer perceptron produced the highest average hit rate among neural network algorithms, and also outperformed the logistic regression model with regard to sensitivity and specificity. Sensitivity analysis was performed on the best model and the importance of input variables was discussed. Further research should focus on improving the performance of the model by combining neural networks with other intelligent methods in this field.     

Similarity classifier with generalized mean applied to medical data

A new approach based on fuzzy similarity was presented for the detection of erythemato-squamous diseases, diabetes, liver disorders, breast cancer and thyroid. The domain contained records of patients with known diagnoses. The results were very promising with all data sets and some conclusions can be drawn that a fuzzy similarity model can be used for the diagnosis of patients taking into consideration the error rate. A fuzzy similarity classifier was used to detect the six erythemato-squamous diseases when 34 features defining six disease indications were used as inputs. The results confirmed that the proposed model has potential in detecting erythemato-squamous diseases. The fuzzy similarity model achieved accuracy rates (over 97%) which were higher than that of the stand-alone neural network model or the ANFIS model suggested in [E.D. Ubeyli, I. Güler, Comput. Biol. Med. 35(5) (2005) 421-433]. With PIMA Indian diabetes, the detection model has an error rate of about 25% which is much better than the overall rate of 33% for diabetes. The model was also tested with other data sets: thyroid and two breast cancer data sets where the average detection accuracy was over 96% for all cases, which is quite good. Also, the liver disorder data set gave promising results.     

Assessment of Granger Causality by Nonlinear Model Identification: Application to Short-term Cardiovascular Variability

A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of the PI was assessed using a surrogate data technique. The proposed method was tested with simulation examples involving short realizations of linear stochastic processes and nonlinear deterministic signals in which either unidirectional or bidirectional coupling and varying strengths of interactions were imposed. It was found that the OPS-based NARX model was accurate and sensitive in detecting imposed Granger causality conditions. In addition, the OPS-based NARX model was more accurate than the least squares method. Application to the systolic blood pressure and heart rate variability signals demonstrated the feasibility of the method. In particular, we found a bilateral causal relationship between the two signals as evidenced by the significant reduction in the PI values with the NARX model prediction compared to the NAR model prediction, which was also confirmed by the surrogate data analysis. Furthermore, we found significant reduction in the complexity of the dynamics of the two causal pathways of the two signals as the body position was changed from the supine to upright. The proposed is a general method, thus, it can be applied to a wide variety of physiological signals to better understand causality and coupling that may be different between normal and diseased conditions.     

Prediction on lengths of stay in the postanesthesia care unit following general anesthesia: preliminary study of the neural network and logistic regression modelling

The length of stay in the postanesthesia care unit (PACU) following general anesthesia in adults is an important issue. A model, which can predict the results of PACU stays, could improve the utilization of PACU and operating room resources through a more efficient arrangement. The purpose of study was to compare the performance of neural network to logistic regression analysis using clinical sets of data from adult patients undergoing general anesthesia. An artificial neural network was trained with 409 clinical sets using backward error propagation and validated through independent testing of 183 records. Twenty-two inputs were used to find determinants and to predict categorical values. Logistic regression analysis was performed to provide a comparison. The neural network correctly predicted in 81.4% of situations and identified discriminating variables (intubated state, sex, neuromuscular blocker and intraoperative use of opioid), whereas the figure was 65.0% in logistic regression analysis. We concluded that the neural network could provide a useful predictive model for the optimization of limited resources. The neural network is a new alternative classifying method for developing a predictive paradigm, and it has a higher classifying performance compared to the logistic regression model.     

Does preprocessing change nonlinear measures of heart rate variability?

This work investigated if methods used to produce a uniformly sampled heart rate variability (HRV) time series significantly change the deterministic signature underlying the dynamics of such signals and some nonlinear measures of HRV. Two methods of preprocessing were used: the convolution of inverse interval function values with a rectangular window and the cubic polynomial interpolation. The HRV time series were obtained from 33 Wistar rats submitted to autonomic blockade protocols and from 17 healthy adults. The analysis of determinism was carried out by the method of surrogate data sets and nonlinear autoregressive moving average modelling and prediction. The scaling exponents , ₁ and ₂ derived from the detrended fluctuation analysis were calculated from raw HRV time series and respective preprocessed signals. It was shown that the technique of cubic interpolation of HRV time series did not significantly change any nonlinear characteristic studied in this work, while the method of convolution only affected the ₁ index. The results suggested that preprocessed time series may be used to study HRV in the field of nonlinear dynamics.     

Error bounds for data-driven models of dynamical systems

This work provides a technique for estimating error bounds about the predictions of data-driven models of dynamical systems. The bootstrap technique is applied to predictions from a set of dynamical system models, rather than from the time-series data, to estimate the reliability (in the form of prediction intervals) for each prediction. The technique is illustrated using human core temperature data, modeled by a hybrid (autoregressive plus first principles) approach. The temperature prediction intervals obtained are in agreement with those from the Camp-Meidell inequality. Moreover, as expected, the prediction intervals increase with the prediction horizon, time-series data variability, and model inaccuracy.     

Wavelet-based neural network analysis of ophthalmic artery Doppler signals

In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.     

Detection of ophthalmic arterial doppler signals with Behcet disease using multilayer perceptron neural network

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 106 subjects, 54 of whom suffered from ocular Behcet disease while the rest were healthy subjects. Multilayer perceptron neural network (MLPNN) employing delta-bar-delta training algorithm was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by least squares (LS) autoregressive (AR) method for determining the MLPNN inputs. The MLPNN was trained with training set, cross validated with cross validation set and tested with testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the MLPNN. The correct classification rate was 96.43% for healthy subjects and 93.75% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the MLPNN employing delta-bar-delta training algorithm was effective to detect the ophthalmic arterial Doppler signals with Behcet disease.     

Identification of ischemic heart disease via machine learning analysis on magnetocardiograms

Ischemic heart disease (IHD) is predominantly the leading cause of death worldwide. Early detection of IHD may effectively prevent severity and reduce mortality rate. Recently, magnetocardiography (MCG) has been developed for the detection of heart malfunction. Although MCG is capable of monitoring the abnormal patterns of magnetic field as emitted by physiologically defective heart, data interpretation is time-consuming and requires highly trained professional. Hence, we propose an automatic method for the interpretation of IHD pattern of MCG recordings using machine learning approaches. Two types of machine learning techniques, namely back-propagation neural network (BNN) and direct kernel self-organizing map (DK-SOM), were applied to explore the IHD pattern recorded by MCG. Data sets were obtained by sequential measurement of magnetic field emitted by cardiac muscle of 125 individuals. Data were divided into training set and testing set of 74 cases and 51 cases, respectively. Predictive performance was obtained by both machine learning approaches. The BNN exhibited sensitivity of 89.7%, specificity of 54.5% and accuracy of 74.5%, while the DK-SOM provided relatively higher prediction performance with a sensitivity, specificity and accuracy of 86.2%, 72.7% and 80.4%, respectively. This finding suggests a high potential of applying machine learning approaches for high-throughput detection of IHD from MCG data.     

基于BP神经网络的左心室心肌组织参数反演方法的研究

因为蕴含着心肌组织特性变化等病理特征,人体左心室的变形和动力学特性已成为心脏疾病临床诊断的重要依据.本研究基于BP神经网络方法,通过对左心室临床诊断数据的反演,开展左心室心肌组织参数识别研究.首先,使用Matlab语言编写图像识别程序提取人体左心室CT影像中内外膜位置点,在SolidWorks软件中建立左心室的真实几何...     

Markov链模型在蛋白质可溶性预测中的应用

利用Markov链模型对蛋白质可溶性特性进行统计建模，按照蛋白质序列中残基的相对可溶性，将其分为两类（表面／内部）和三类（表面／中间／内部）进行预测。选择不同MCM阶数和分类阈值对数据进行训练和预测，以确保得到最好的分类效果。对两种数据集在不同分类阈值下进行分类预测，并将结果同其他已有方法如神经网络、信息论和支持向量机法等进行比较。该方法对蛋白质可溶性的预测精度和相关系数普遍好于或接近其他预测方法，其中对两类分类问题和三类分类问题的最优分类结果分别达到78．9％和67．7％。同时，该方法具有运算复杂度低、耗时短等优点。     

Large-Scale Optimization-Based Classification Models in Medicine and Biology

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.     

应用随机森林与神经网络算法的足底软组织 本构参数反演方法

目的 基于随机森林（random forest, RM）算法和反向传播（back propagation,BP）神经网络算法实现对足底软组织超弹性模型本构参数的预测,以提升本构参数获取方式的效率和准确性。方法 首先建立足底软组织球形压痕实验的有限元模型,并对球形压痕实验过程进行仿真,得到具有非线性关系的位移和压痕力的数据集。将数据集进行划分,得到训练集和测试集,分别对搭建好的RF和BP神经网络（BP neural network,BPNN）模型进行训练,通过实验数据对足底软组织本构参数进行预测。最后,引入均方误差（mean square error,MSE）和决定系数（R2）对模型的预测准确性进行评估,同时对比实验曲线验证模型的有效性。结果 利用RF和BPNN模型结合有限元仿真是确定足底软组织超弹性本构参数的有效、准确的方法。训练后的RF模型MSE达到1.370 2×10-3,R2为0.982 9;BPNN模型MSE达到4.858 1×10-5,R2为0.999 3。反求得到适用于仿真的足底软组织的超弹性本构参数,预测得到的两组本构参数的计算响应曲线与实验曲线吻合较好。结论 基于人工智能算法模型对足底软组织超弹性本构参数的预测精度很高,相关研究成果也可以应用于足底软组织其他力学特性的研究。同时,研究结果为足底软组织本构参数的获取提供新方法,有助于快速诊断足底软组织病变等临床问题。