基于心电图的异源数据库及诊断算法研究

发布时间：2018-04-19 09:03

本文选题：ECG + 异源心电数据库　；参考：《哈尔滨工业大学》2016年硕士论文

【摘要】：心电图(Electrocardiogram,ECG)从19世纪被应用于临床医学以来,一直在疾病诊断过程中扮演着重要角色。通过对前人研究的总结和分析,可以发现心电信号处理算法非常的丰富,但很少有涉及到针对异源心电数据库问题的研究。然而现实的应用往往针对异源数据库,所以异源数据库的研究是非常有必要和价值的。另外,神经网络被广泛应用于心电识别领域,尤其是BP神经网络。但通过深入研究和大量实验发现,当样本容量逐渐增大相应测试样本的容量也随之增大时,基于BPNN的分类中,测试样本输出结果的区分度会越来越小。这必然导致很多样本被错误的归类,所以解决BP神经网络的这一缺陷也是非常有必要的。针对上面出现的这两个问题,本文提出了解决异源数据库应用障碍的预处理方法和基于BP神经网络的多阶分类算法。通过对心电图诊断过程的充分调研,提出了去噪及采样频率转换的预处理方法。结合三个异源心电数据库,进行了异源数据库处理并通过熵值、标准差、峰度和偏度四个相似度指标对处理前后三个数据库信号的相似度做了对比。实验表明,提出的预处理方案具有较好的效果。另外,结合MIT-BIH心率失常数据库和PTB心电数据库,进行了异源数据库间的疾病诊断实验,实验表明本文提出的预处理方案可以解决异源数据库之间疾病诊断问题。为了解决BP神经网络分类区分度变小的问题,在对BP神经网络深入研究之后,本文通过增加多个假测试集进行多阶网络训练。得到多个训练网络,最后应用训练得到的多个网络对测试集进行分类,这样便解决了区分度下降的问题。然后针对MIT-BIH心率失常数据库中5类心搏进行了分类实验,结合波形特征提取方式识别率可以达到96.8%,比常规的BP神经网络算法识别率高出5%左右。另外,本文还利用SVM算法结合小波特征和波形特征以及调研中的较好文献方法,进行了分类实验,实验对比均显示本文提出的基于BP神经网络的多阶分类算法效果要好于其他方法。在完成前面研究工作之后,以此作为基础,本课题实现了基于心电图的心率失常诊断系统。诊断系统以普遍的Android智能手机作为客户端载体,将利用便携式设备采集到的心电数据通过无线网络传送到服务器,然后进行系统化的诊断,并将诊断结果反馈回手机供客户参考。
[Abstract]:Electrocardiogram (ECG) has played an important role in the diagnosis of diseases since it was used in clinical medicine in the 19th century.Through the summary and analysis of previous studies, we can find that ECG processing algorithms are very rich, but there are few researches on the problem of heterogeneous ECG database.However, the actual application is often aimed at heterologous database, so the research of heterologous database is very necessary and valuable.In addition, neural network is widely used in the field of ECG recognition, especially BP neural network.However, through in-depth research and a large number of experiments, it is found that when the sample size increases with the increase of the corresponding test sample capacity, the classification degree of the test sample output will become smaller and smaller in the classification based on BPNN.This will inevitably result in many samples being misclassified, so it is necessary to solve this defect of BP neural network.In view of the above two problems, this paper proposes a preprocessing method to solve the application obstacle of heterogeneous database and a multi-order classification algorithm based on BP neural network.A preprocessing method of denoising and sampling frequency conversion is put forward by investigating the diagnosis process of electrocardiogram (ECG).Combining with three different ECG databases, the paper makes a comparison of the similarity of the three database signals before and after processing through four similarity indexes: entropy, standard deviation, kurtosis and skewness.The experimental results show that the proposed pretreatment scheme has a better effect.In addition, combined with MIT-BIH arrhythmia database and PTB ECG database, the disease diagnosis experiment between heterologous databases is carried out. The experiment shows that the proposed preprocessing scheme can solve the problem of disease diagnosis between heterogeneous databases.In order to solve the problem that the classification degree of BP neural network becomes smaller, after the in-depth study of BP neural network, the multi-order network training is carried out by adding multiple false test sets.Finally, several training networks are used to classify the test set, which solves the problem of the degree of discrimination decreasing.Then, the classification experiments of 5 kinds of cardiac beats in MIT-BIH arrhythmia database are carried out. The recognition rate of the five types of cardiac beats based on waveform feature extraction can reach 96.8%, which is about 5% higher than the recognition rate of the conventional BP neural network algorithm.In addition, the SVM algorithm is used to combine wavelet features and waveform features, as well as the better literature methods in the research, and the classification experiments are carried out.The experimental results show that the proposed multi-order classification algorithm based on BP neural network is better than other methods.On the basis of the above research work, a ECG based diagnosis system for heart rate disorders is implemented in this paper.The diagnosis system takes the universal Android smart phone as the client carrier, transmits the ECG data collected by the portable device to the server through the wireless network, and then carries on the systematic diagnosis.And the results of the diagnosis back to the mobile phone for customer reference.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：R540.41;TP311.13

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