Laboratoire LAMPA, Université Mouloud Mammeri, Tizi-Ouzou, Algeria.
Comput Methods Programs Biomed. 2013 Sep;111(3):570-7. doi: 10.1016/j.cmpb.2013.05.011. Epub 2013 Jul 10.
In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat. An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost.
在本文中，我们介绍了一种新的基于支持向量机（SVM）分类器的 ECG 心拍分类系统，该分类器具有拒绝功能。在 ECG 预处理之后，检测并分段 QRS 复合体。利用一组特征，包括频率信息、RR 间隔、QRS 形态和 QRS 细节系数的交流功率，来描述每个心拍。然后使用 SVM 对特征向量进行分类。我们的决策规则使用动态拒绝阈值，该阈值根据误分类样本的代价和拒绝样本的代价进行调整。当使用麻省理工学院-贝斯以色列医院心律失常数据库对所提出的方法进行测试时，观察到显著的性能提升。所取得的结果表示为无拒绝的平均准确率为 97.2%，最小分类代价的准确率为 98.8%。
