基于非参数相关系数的心肌病自动诊断

针对肥厚型心肌病和扩张型心肌病患者的心电图导联间的相关性，提出心肌病自动诊断的一种方法。该研究从12导联ECG信号中分割出来的单个心跳片段进行识别，以健康人群为对照识别出DCM和HCM的片段。从片段中提取264个非参数相关系数特征并通过变量筛选得到12个特征，输入到支持向量机中进行建模，采用10折交叉验证评价模型。模型的总准确率为99.88%±0.08%。模型使用的特征少，运行速度快，准确率高，有助于临床心肌病的自动化诊断，节约医疗资源。

Automatic diagnosis of cardiomyopathy based on nonparametric correlation coefficient

Based on the correlation between electrocardiogram(ECG)leads of patients with hypertrophic cardiomyopathy(HCM)and dilated cardiomyopathy(DCM),a method of automatic diagnosis of cardiomyopathy is proposed.In this study,a single heart beat segment separated from 12-Lead ECG signals is identified,and DCM and HCM segments are identified from healthy people.After calculation,264 nonparametric correlation coefficient features are extracted from the fragments,and 12 features are obtained through variable selection.Then,these 12 features are inputted to support vector machine for modeling.10-fold cross-validation is used to evaluate the model.The accuracy of the model is 99.88%±0.08%.With fewer features,higher speed and higher accuracy,the model is helpful to the automatic diagnosis of clinical cardiomyopathy and saves medical resources.