兰州大学学报（自然科学版）
蘭州大學學報（自然科學版）
란주대학학보（자연과학판）
JOURNAL OF LANZHOU UNIVERSITY（NATURAL SCIENCES）
2010年
5期
112-115
,共4页
分类%心脏早博%支持向量机%辅助诊断
分類%心髒早博%支持嚮量機%輔助診斷
분류%심장조박%지지향량궤%보조진단
classification%premature cardiac contraction%support vector machine%assisted diagnosis
临床上,由于心电图特征信息的交错而难以对患者的心脏早博类型进行正确识别.作为计算机辅助的一种方法,基于从临床收集到的82个患者的样本,建立了支持向量机模型.该模型的训练准确度为94.44%、测试准确度达到92.86%,其留一法交叉检验准确度为92.59%.满意的结果表明所建议的模型可以应用于临床辅助诊断.
臨床上,由于心電圖特徵信息的交錯而難以對患者的心髒早博類型進行正確識彆.作為計算機輔助的一種方法,基于從臨床收集到的82箇患者的樣本,建立瞭支持嚮量機模型.該模型的訓練準確度為94.44%、測試準確度達到92.86%,其留一法交扠檢驗準確度為92.59%.滿意的結果錶明所建議的模型可以應用于臨床輔助診斷.
림상상,유우심전도특정신식적교착이난이대환자적심장조박류형진행정학식별.작위계산궤보조적일충방법,기우종림상수집도적82개환자적양본,건립료지지향량궤모형.해모형적훈련준학도위94.44%、측시준학도체도92.86%,기류일법교차검험준학도위92.59%.만의적결과표명소건의적모형가이응용우림상보조진단.
It is difficult to determinate the premature cardiac contraction type of a case in clinic due to its vague signals in electrocardiogram.As an approach of computer-assisted diagnosis,a model for classification was proposed based on support vector machine(SVM).All samples data were derived from82 clinic cases.By means of our SVM model,the accuracies of classification were up to 94.44% for the training set and 92.86% for the testing set.The accuracy of leave-one-out cross-validation was 92.59%.The satisfactory results indicate that the proposed approach is effective and could be applied to assisted diagnosis in clinic practice.
