【作者】 冯俊；

【导师】 莫智文；

【作者基本信息】 四川师范大学 ， 运筹学与控制论， 2005， 硕士

【摘要】 本文基于心电图特征提取与神经网络分类,提出了一种模拟现实特点的心电图分类方法:一方面,改进心电图分析中的射线拟合方法,避免了一般快速拟合法由于其确定的线段终点不能落在心电曲线上,从而容易出现逼近线段锯齿状摆动情况,提高了拟合的质量.并利用它对心电图进行识别分析,进而得到心电图多导联的特征.另一方面,根据心电信号中,各波分别处在不同的频率范围的特点,采用Mexican-hat 小波变换检测心电信号的特征点.根据Mexican-hat 小波的特点,心电图的各特征点对应于变换后的局部极值点,克服了一般的小波变换,特征点对应模极值对的过零点,从而需检测模极值对和过零点的困难,提高了特征点检测的准确率,对R 波的识别正确率达到99.9%. 在分别运用两种方法检测到心电信号的特征点后,根据临床情况下心电图疾病分析的原理与实际诊断的特点,根据径向基函数网络具有强分类能力的特性,应用一个径向基函数网络对心电图特征在高维空间进行分类.经MIT-BIH心电数据库部分波形试验证明,该方法通过对提取到的特征进行学习、分类,具有较好的分类准确率.在文献[6]分类正确率不到97%(学习波形)与54%(未学习波形)的基础上,对学习过的波形分类的正确率达到100%.对未学习过的波形,经分类网络试验,对通过射线拟合方法得到的特征,分类正确率为78.2%;对通过小波变换方法得到的特征,分类正确率达到86.6%.更多还原

【Abstract】 In this study, according to the feature extraction and neural network classification, an ECG classification method simulating the real world situation is presented: One hand, a modified approach of linear approximation distance thresholding (LADT) algorithm has been studied and complemented. This complement avoid the case that in the common LADT algorithm, as the endpoints of the approximating segment sometimes may not be properly determined on the ECG signals, the saw-tooth like approximation is sure to come out. Thus the quality of the approximation is enhanced. And then, through analyzing the segments, the QRS complexes are detected and then the features of the ECG signals are obtained. The other hand, as the ECG waves have different frequencies, the Mexican-hat wavelet-transform is adopted to detect the character points. According to the particularity of the Mexican-hat wavelet-transform, the ECG character points are just corresponding to the local extremes of the signals transformed. It overcomes the complexity that if transformed with spline wavelet, the character points are only corresponding to the zero-crossing points of the modulus maximum pairs. And thus the processes of detections can be simplified and this also improves the detection performance that the correct rate of R detection achieves to 99.9%. After detecting the character points of the ECG signals, according to the theory of the ECG analysis and the real world situation, with the splendid capability of classification of RBF network, the ECG signals are classified in a high dimensions space. The tests with some ECG signals of MIT-BIH show that after training by the features extracted first, the correct rates of classification are excellent. As the correct rates from the article [6] are less than 97% to the training waves and 54% to the untrained waves, the classification correct rate of the training waves is 100% in this study. As to the untrained waves, the correct rates are a little different. To the waves whose features are extracted by the LADT, the correct rate is 78.2%; to the waves whose features are extracted by the wavelet-transform, the correct rate is 86.6%.更多还原