基于支持向量机的室性早搏检测

Detection of PVCs with Support Vector Machine

心电信号分类是自动心电监护设备的基础。支持向量机 (SVM)在分类和模式识别方面展现出卓越的性能。本研究将支持向量机应用于心电信号室性早搏 (PVC)的检测。根据室性早搏的特点 ,从 ML II导联中提取心率、形态心及小波域能量 3大类共 9个特征。并使用 MIT- BIH的 Arrhythmia数据库的数据 ,根据 AAMI建议要求 ,对采用不同核函数的支持向量机的性能作了比较。

The classifiction of heart beats is the foundation for automated arrhythmia monitoring devices. Support vector machnies（SVMs） have meant a great advance in solving classification or pattern recognition. This sutdy describes SVM for the identification of premature ventricular contractions （PVCs） in surface ECGs. Features for the classification task are extracted by analyzing the heart rate, morphology and wavelet energy of the heart beasts from a single lead. The performance of different SVMs is evaluated on the MIT-BIH arrhythmia database following the association for the advancement of medical instrumentation （AAMI） recommendations.