仪器引导呼吸对高血压性心脏病患者左心功能的恢复作用分析

为了提升高血压性心脏病的临床诊疗恢复效果,进行仪器引导呼吸对高血压性心脏病患者左心功能恢复作用研究。详细制定临床恢复性指标,确定当前治疗仪的准入量,连入引导呼吸仪,以患者左心房为中心利用稳态程序进入扫描序列(SSPF),并保证患者进入慢性恢复状态。采用LVEDD测量法,对左心功能进行测量,确定恢复周期,实现患者左心功能恢复。实验表明,应用所提方法后,左心室GH值升高,左尖瓣脉冲异常平均值为6&44,最长治疗周期为7、8个月,恢复周期短且效果较好。     

A new neural network structure for detection of coronary heart disease

We investigate the application of neural networks for the detection of Coronary Heart Disease (CHD). We have used a Neural Network (NN) on data from a self- applied questionnaire to implement a decision system designed to seek out high risk individuals in a large population. A Multi- Layered Perceptron (MLP) was trained with risk factors to distinguish CHD. We also describe a modification to the architecture of the neural network in which an extra layer of neurons is added at the input. We present possible interpretations of the weights of these neurons, and show how they can be used as a selection criteria for which questions to use as inputs. The technique is compared against other statistical methods. We go on to demonstrate the system's capability for detecting both the symptomatic and asymptomatic patient.     

面向普适健康监测的心脏病分析方法

为了提高普适健康监测服务的病人护理质量，采用贝叶斯网络对心脏病数据进行及时准确的分析，提出从样本数据集中学习网络节点顺序的方法，克服了传统算法需要领域专家给定网络中节点顺序的限制；另外，引入了并行优化方法进一步提高在大数据量情况下建立心脏病诊断分析模型的速度。实验证明提出的方法在一定程度上提高了模型分析的准确率，并且缩短了建模的时间。     

Intelligent heart disease prediction in cloud environment through ensembling

Cloud computing is the delivery of on‐demand computing resources. Cloud computing has numerous applications in fields of education, social networking, and medicine. But the benefit of cloud for medical purposes is seamless, particularly because of the enormous data generated by the health care industry. This colossal data can be managed through big data analytics, and hidden patterns can be extracted using machine learning procedures. In particular, the latest issue in the medical domain is the prediction of heart diseases, which can be resolved through culmination of machine learning and cloud computing. Hence, an attempt has been made to propose an intelligent decision support model that can aid medical experts in predicting heart disease based on the historical data of patients. Various machine learning algorithms have been implemented on the heart disease dataset to predict accuracy for heart disease. Naïve Bayes has been selected as an effective model because it provides the highest accuracy of 86.42% followed by AdaBoost and boosted tree. Further, these 3 models are being ensembled, which has increased the overall accuracy to 87.91%. The experimental results have also been evaluated using 10,082 instances that clearly validate the maximum accuracy through ensembling and minimum execution time in cloud environment.     

基于支持向量机的冠心病辅助诊断研究

支持向量机(SVM)是在统计学习理论基础上发展而来的一种新的通用学习方法,较好地解决了有限样本的学习分类问题。用支持向量机的分类算法,选取不同的核函数,构造了支持向量机的不同分类器,并将其应用于冠心病的预测诊断。仿真结果表明,非线性的支持向量机取得了较高的准确率,支持向量机在早期冠心病的诊断中有很大的应用潜力。     

Classification of congestive heart failure with different New York Heart Association functional classes based on heart rate variability indices and machine learning

This study aims to evaluate the effect of heart rate variability (HRV) indices on the New York Heart Association (NYHA) classification of patients with congestive heart failure and to test the effectiveness of different machine learning algorithms. Twenty‐nine long‐term RR interval recordings from subjects (aged 34 to 79) with congestive heart failure (NYHA classes I, II, and III) in MIT‐BIH Database were studied. We firstly removed the unreasonable RR intervals and segment the RR recordings with a 300‐RR interval length window. Then the multiple HRV indexes were calculated for each RR segment. Support vector machine (SVM) and classification and regression tree (CART) methods were then separately used to distinguish patients with different NYHA classes based on the selected HRV indices. Receiver operating characteristic curve analysis was finally employed as the evaluation indicator to compare the performance of the two classifiers. The SVM classifier achieved accuracy, sensitivity, and specificity of 84.0%, 71.2%, and 83.4%, respectively, whereas the CART classifier achieved 81.4%, 66.5%, and 81.6%, respectively. The area under the curve of receiver operating characteristic for the two classifiers was 86.4% and 84.7%, respectively. It is possible for accurately classifying the NYHA functional classes I, II, and III when using the combination of HRV indices and machine learning algorithms. The SVM classifier performed better in classification than the CART classifier using the same HRV indices.     

Effective diagnosis of heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.     

Microprocessor-based heart monitoring

The application of microprocessors in designing a complete heart monitoring system is highlighted. Starting from the electrical analogue model of the heart expressing the arterial pressure in terms of the model parameters, a technique for determining the stroke volume, mean pressure, heart rate, cardiac output, etc is described. The model parameters are determined online during the diastole condition using actual patients' pressure profiles. The model dynamics are realized on a Z80 microprocessor and the results of the stroke volume, heart rate, cardiac output, mean pressure etc are printed out on every other beat. The technique suggested is more reliable and accurate than the other conventional methods for long term monitoring of the patient's clinical data and may be more suitable for use in intensive care units.     

A hybrid recurrent neural network‐logistic chaos‐based whale optimization framework for heart disease prediction with electronic health records

Heart disease, known interchangeably as “Cardio Vascular Disease,” blocks the blood vessels in the heart and causes heart attack, chest pain, and stroke. Heart disease is one of the leading causes of morbidity and mortality worldwide and it is one of the major causes of morbidity and mortality globally and a trending topic in clinical data analysis. Assessing risk factors related to heart disease is considered as an important step in diagnosing the disease at an early stage. Clinical data present in the form of electronic health records (EHR) can be extracted with the aid of machine learning (ML) algorithms to provide valuable decisions and predictions. ML approaches also play a vital role in early diagnosis and therapeutic monitoring of heart disease. Several research works have been carried out recently to predict heart disease. To this end, we propose a novel hybrid recurrent neural network (RNN)‐logistic chaos‐based whale optimization (LCBWO) structured hybrid framework for predicting heart disease within 5 years using EHR data. Meanwhile, in the hybrid model established multilayer bidirectional LSTM is used for feature selection, LCBWO algorithm for structural improvement and fast convergence, and LSTM for disease prediction. This research used 10 cross‐validations to obtain generalized accuracy and error values. The findings and observations provided here are focused on the knowledge obtained from the EHR report. The results show that the proposed novel hybrid RNN‐LCBWO framework achieves a higher accuracy of 98%, a specificity of 99%, precision of 96%, Mathews correlation coefficient of 91%, F‐measure of 0.9892, an area under the curve value of 98%, and a prediction time of 9.23 seconds. The accurate predictions obtained from the comparative analysis shows the significant performance of our proposed framework.     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

基于主动学习不平衡多分类AdaBoost算法的心脏病分类

针对不平衡分类中小类样本识别率低问题,提出一种基于主动学习不平衡多分类AdaBoost改进算法。首先,利用主动学习方法通过多次迭代抽样,选取少量的、对分类器最有价值的样本作为训练集;然后,基于不确定性动态间隔的样本选择策略,降低训练集的不平衡性;最后,利用代价敏感方法对多分类AdaBoost算法进行改进,对不同的类别给予不同的错分代价,调整样本权重更新速度,强迫弱分类器"关注"小类样本。在临床经胸超声心动图（TTE）测量数据集上的实验分析表明：与多分类支持向量机（SVM）相比,心脏病总体识别率提升了5.9%,G-mean指标提升了18.2%,瓣膜病（VHD）识别率提升了0.8%,感染性心内膜炎（IE）（小类）识别率提升了12.7%,冠心病（CAD）（小类）识别率提升了79.73%;与SMOTE-Boost相比,总体识别率提升了6.11%,G-mean指标提升了0.64%,VHD识别率提升了11.07%,先心病（CHD）识别率提升了3.69%。在TTE数据集和4个UCI数据集上的实验结果表明,该算法在不平稳多分类时能有效提高小类样本识别率,并且保证其他类别识别率不会大幅度降低,综合提升分类器性能。     

HILBERT包络法在心音身份识别中的应用

在分析心音信号特征的基础上,对心音信号进行预处理,再利用希尔伯特变换对心音信号进行心音信号包络提取,突出了心音信号的第一心音和第二心音.然后对心音包络进行分段,通过单周期心音包络的归一化能量实现了心音信号的身份识别.     

Microprocessor-based portable system for heart condition diagnosis

Early diagnosis of heart disease is typically based on a cassette recording of the electrocardiogram (ECG) signal which is then studied and analysed using a microcomputer. The system is bulky, unreliable and prone to mechanical failure. This paper presents the design and implementation of a compact microprocessor-based portable system used for heart condition diagnosis over a long period. The system reads, stores and analyses the ECG signals repetitively in real time for a specified period. The diagnostic data and samples of ECG signals are stored throughout the test period. The system hardware and software design are oriented towards a single-chip microcomputer-based system, hence minimizing size. The operating algorithm is based on a logical approach to ECG signal diagnosis and hence requires little memory.     

心音身份识别技术的研究

文中研究心音身份识别的基本原理和实现方法.首先分析心音信号的特性和作为生物识别的可行性;然后建立基于心音子波族的心音信号合成模型,并且用特征向量分布相图形象地比较两个心音的特征,用倒谱减法消除听诊器的类型和位置变化所产生的影响;最后,采用心音线性频带倒谱(HS-LBFC)提取心音特征参数,用相似距离等实现心音的身份识别.为了突出心音在时、频域上存在的差异,重点研究了构建心音子波的方法,合成模型中各参数的计算方法,以及心音特征参数的确定和对应的数据处理技术.实际实验结果表明,该方法具有很好的识别率和实用性.     

运动心率采集技术的发展

心脏是维持人体生命的重要器官之一,心率携带了大量反映人体健康状况的信息.运动中通过采集和分析心率能够判断运动强度是否合理,并了解运动对人体生理的影响.40年来,无线心率采集技术经历了6个发展阶段,在元件、频率、稳定性等诸多领域取得了卓越的突破,现已广泛地应用于专业运动训练和大众健身中.同时,这项技术还保持着快速的发展势头.     

基于虚拟仪器的心音采集与分析系统设计

开发了一种新型的电子心音信号采集与分析系统,该系统以心音传感器和计算机自带声卡为基础,实现了心脏听诊从传统单一的"听"转变为可视、可听的多角度分析,结合LabVIEW和Matlab强大的数据分析能力实现了心音信号的采集、去噪、保存、分析等功能,可作为临床心脏诊断的辅助设备。     

Use of secondary data to estimate instantaneous model parameters of diabetic heart disease: Lemonade Method

With the increasing burden of chronic diseases on the health care system, Markov-type models are becoming popular to predict the long-term outcomes of early intervention and to guide disease management. However, statisticians have not been actively involved in the development of these models. Typically, the models are developed by using secondary data analysis to find a single “best” study to estimate each transition in the model. However, due to the nature of secondary data analysis, there frequently are discrepancies between the theoretical model and the design of the studies being used. This paper illustrates a likelihood approach to correctly model the design of clinical studies under the conditions where (1) the theoretical model may include an instantaneous state of distinct interest to the researchers and (2) the study design may be such that study data cannot be used to estimate a single parameter in the theoretical model of interest. For example, a study may ignore intermediary stages of disease. Using our approach, not only can we accommodate the two conditions above, but more than one study may be used to estimate model parameters. In the spirit of “If life gives you lemon, make lemonade”, we call this method “Lemonade Method”. Simulation studies are carried out to evaluate the finite sample property of this method. In addition, the method is demonstrated through application to a model of heart disease in diabetes.     

CCD显微摄像法测量孔心距

通过对19J型万能工具显微镜进行数码改造,建立了工件孔心距测量的CCD数码显微摄像系统。讨论了CCD摄像法测量孔心距的基本原理,简要介绍了系统的标定,给出了测量结果及准确度分析。实验表明,测量系统可快速、精确地测量工件孔心距。