多数现有的计算机辅助心电(ECG)诊断技术研究通常是基于常规心电导联展开的,而正交Frank心电导联比常规心电导联有着与解剖学更为密切的联系.基于Frank导联的心肌梗死(MI)心电特征提取和分类检测的研究,对MI ECG信号进行Hermite非线性展...多数现有的计算机辅助心电(ECG)诊断技术研究通常是基于常规心电导联展开的,而正交Frank心电导联比常规心电导联有着与解剖学更为密切的联系.基于Frank导联的心肌梗死(MI)心电特征提取和分类检测的研究,对MI ECG信号进行Hermite非线性展开,以Hermite系数为心电特征,并对其进行分类测试.与常规心电导联相比,此方法对早期MI和急性期MI进行分类,检测精度可分别提高30.06%和19.33%.
Abstract：
Most of existing computer assistanted diagnosis technique studies are performed based on standard electrocardiogram(ECG) leads. Orthogonal Frank leads are more eorrehted with anatomy compared with standard ECG leads. Myocardial infarction (MI) ECG feature extraction and detection based on Frank leads is proposed. The ECG signals are expressed by nonlinear Hermite expansion method, and Hermite coefficients are utilized as ECG features for the classification. The accuracy of detecting MI in early stage and MI in acute stage can be increased by 30. 06 and 19. 33 percentages compared with that of standard ECG leads using the proposed method.展开更多
文摘多数现有的计算机辅助心电(ECG)诊断技术研究通常是基于常规心电导联展开的,而正交Frank心电导联比常规心电导联有着与解剖学更为密切的联系.基于Frank导联的心肌梗死(MI)心电特征提取和分类检测的研究,对MI ECG信号进行Hermite非线性展开,以Hermite系数为心电特征,并对其进行分类测试.与常规心电导联相比,此方法对早期MI和急性期MI进行分类,检测精度可分别提高30.06%和19.33%.
Abstract：
Most of existing computer assistanted diagnosis technique studies are performed based on standard electrocardiogram(ECG) leads. Orthogonal Frank leads are more eorrehted with anatomy compared with standard ECG leads. Myocardial infarction (MI) ECG feature extraction and detection based on Frank leads is proposed. The ECG signals are expressed by nonlinear Hermite expansion method, and Hermite coefficients are utilized as ECG features for the classification. The accuracy of detecting MI in early stage and MI in acute stage can be increased by 30. 06 and 19. 33 percentages compared with that of standard ECG leads using the proposed method.
