Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland.
Physiol Meas. 2018 Nov 30;39(11):115010. doi: 10.1088/1361-6579/aaecef.
Our aim was to develop an automated detection method, for prescreening purposes, of early repolarization (ER) pattern with slur/notch configuration in electrocardiogram (ECG) signals using a waveform prototype-based feature vector for supervised classification.
The feature vectors consist of fragments of the ECG signal where the ER pattern is located, instead of abstract descriptive variables of ECG waveforms. The tested classifiers included linear discriminant analysis, k-nearest neighbor algorithm, and support vector machine (SVM).
SVM showed the best performance in Friedman tests in our test data including 5676 subjects representing 45 408 leads. Accuracies of the different classifiers showed results well over 90%, indicating that the waveform prototype-based feature vector is an effective representation of the differences between ECG signals with and without the ER pattern. The accuracy of inferior ER was 92.74% and 92.21% for lateral ER. The sensitivity achieved was 91.80% and specificity was 92.73%.
The algorithm presented here showed good performance results, indicating that it could be used as a prescreening tool of ER, and it provides an additional identification of critical cases based on the distances to the classifier decision boundary, which are close to the 0.1 mV threshold and are difficult to label.
我们旨在开发一种自动化检测方法，用于通过基于波形原型的特征向量对心电图（ECG）信号中的早期复极（ER）模式进行预筛选，该方法采用基于监督分类的特征向量进行检测。
特征向量由 ER 模式所在的 ECG 信号片段组成，而不是 ECG 波形的抽象描述变量。所测试的分类器包括线性判别分析、k-最近邻算法和支持向量机（SVM）。
在包括 5676 名受试者和 45408 导心电图的测试数据中，SVM 在 Friedman 测试中的表现最好。不同分类器的准确率均超过 90%，表明基于波形原型的特征向量是 ECG 信号中存在和不存在 ER 模式之间差异的有效表示。下壁 ER 的准确率为 92.74%，侧壁 ER 的准确率为 92.21%。获得的灵敏度为 91.80%，特异性为 92.73%。
本文提出的算法表现出良好的性能结果，表明它可作为 ER 的预筛选工具，并基于与分类器决策边界的距离提供了对临界病例的额外识别，这些距离接近 0.1 mV 的阈值，难以标记。
