Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 41349, Taiwan.
Sensors (Basel). 2013 Jan 9;13(1):813-28. doi: 10.3390/s130100813.
An automatic configuration that can detect the position of R-waves, classify the normal sinus rhythm (NSR) and other four arrhythmic types from the continuous ECG signals obtained from the MIT-BIH arrhythmia database is proposed. In this configuration, a support vector machine (SVM) was used to detect and mark the ECG heartbeats with raw signals and differential signals of a lead ECG. An algorithm based on the extracted markers segments waveforms of Lead II and V1 of the ECG as the pattern classification features. A self-constructing neural fuzzy inference network (SoNFIN) was used to classify NSR and four arrhythmia types, including premature ventricular contraction (PVC), premature atrium contraction (PAC), left bundle branch block (LBBB), and right bundle branch block (RBBB). In a real scenario, the classification results show the accuracy achieved is 96.4%. This performance is suitable for a portable ECG monitor system for home care purposes.
提出了一种能够从麻省理工学院-贝斯以色列女执事医疗中心（MIT-BIH）心律失常数据库中获取的连续心电图信号中自动检测 R 波位置、对正常窦性节律（NSR）和其他四种心律失常类型进行分类的配置。在该配置中，支持向量机（SVM）用于检测和标记原始信号和导联心电图差分信号中的心电图心跳。一种基于提取的标记算法，将心电图导联 II 和 V1 的波形分段作为模式分类特征。一种自构建神经模糊推理网络（SoNFIN）用于分类 NSR 和四种心律失常类型，包括室性早搏（PVC）、房性早搏（PAC）、左束支传导阻滞（LBBB）和右束支传导阻滞（RBBB）。在实际场景中，分类结果表明所达到的准确率为 96.4%。这种性能适用于用于家庭护理的便携式心电图监测系统。
