Cukurova University, Computer Engineering Department, Adana, Turkey.
Comput Biol Med. 2013 Oct;43(10):1556-62. doi: 10.1016/j.compbiomed.2013.07.015. Epub 2013 Jul 24.
This study proposes a new method, equal frequency in amplitude and equal width in time (EFiA-EWiT) discretization, to discriminate between congestive heart failure (CHF) and normal sinus rhythm (NSR) patterns in ECG signals. The ECG unit pattern concept was introduced to represent the standard RR interval, and our method extracted certain features from the unit patterns to classify by a primitive classifier. The proposed method was tested on two classification experiments by using ECG records in Physiobank databases and the results were compared to those from several previous studies. In the first experiment, an off-line classification was performed with unit patterns selected from long ECG segments. The method was also used to detect CHF by real-time ECG waveform analysis. In addition to demonstrating the success of the proposed method, the results showed that some unit patterns in a long ECG segment from a heart patient were more suggestive of disease than the others. These results indicate that the proposed approach merits additional research.
本研究提出了一种新的方法，即等幅等宽（EFiA-EWiT）离散化，用于区分心电图信号中的充血性心力衰竭（CHF）和正常窦性节律（NSR）模式。引入了 ECG 单元模式概念来表示标准 RR 间隔，我们的方法从单元模式中提取某些特征，然后由原始分类器进行分类。该方法在两个分类实验中进行了测试，使用了 Physiobank 数据库中的 ECG 记录，并将结果与之前的一些研究进行了比较。在第一个实验中，通过从长 ECG 段中选择单元模式进行离线分类。该方法还用于通过实时 ECG 波形分析检测 CHF。除了证明所提出方法的成功外，结果还表明，来自心脏病患者的长 ECG 段中的一些单元模式比其他模式更能提示疾病。这些结果表明，所提出的方法值得进一步研究。
