Queiroz Vinícius, Luz Eduardo, Moreira Gladston, Guarda Álvaro, Menotti David
Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:5203-6. doi: 10.1109/EMBC.2015.7319564.
This paper intends to bring new insights in the methods for extracting features for cardiac arrhythmia detection and classification systems. We explore the possibility for utilizing vectorcardiograms (VCG) along with electrocardiograms (ECG) to get relevant informations from the heartbeats on the MIT-BIH database. For this purpose, we apply complex networks to extract features from the VCG. We follow the ANSI/AAMI EC57:1998 standard, for classifying the beats into 5 classes (N, V, S, F and Q), and de Chazal's scheme for dataset division into training and test set, with 22 folds validation setup for each set. We used the Support Vector Machinhe (SVM) classifier and the best result we chose had a global accuracy of 84.1%, while still obtaining relatively high Sensitivities and Positive Predictive Value and low False Positive Rates, when compared to other papers that follows the same evaluation methodology that we do.
本文旨在为心律失常检测与分类系统的特征提取方法带来新的见解。我们探索了利用向量心电图（VCG）和心电图（ECG）从麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）数据库中的心跳获取相关信息的可能性。为此，我们应用复杂网络从VCG中提取特征。我们遵循ANSI/AAMI EC57:1998标准，将心跳分为5类（N、V、S、F和Q），并采用德查扎尔（de Chazal）的方案将数据集划分为训练集和测试集，并为每个集合设置22折交叉验证。我们使用支持向量机（SVM）分类器，我们选择的最佳结果全局准确率为84.1%，与遵循相同评估方法的其他论文相比，仍具有相对较高的灵敏度和阳性预测值以及较低的假阳性率。
