Matsuyama A, Jonkman M
School of Engineering and Logistics, Faculty of Technology, Charles Darwin University, Australia.
Australas Phys Eng Sci Med. 2006 Mar;29(1):13-7. doi: 10.1007/BF03178823.
The Electrocardiogram (ECG) is one of the most commonly known biological signals. Traditionally ECG recordings are analysed in the time-domain by skilled physicians. However, pathological conditions may not always be obvious in the original time-domain signal. Fourier analysis provides frequency information but has the disadvantage that time characteristics will be lost. Wavelet analysis, which provides both time and frequency information, can overcome this limitation. Here a new method, the combination of wavelet analysis and feature vectors, is applied with the intent to investigate its suitability as a diagnostic tool. ECG signals with normal and abnormal beats were examined. There were two stages in analysing ECG signals: feature extraction and feature classification. To extract features from ECG signals, wavelet decomposition was first applied and feature vectors of normalised energy and entropy were constructed. These feature vectors were used to classify signals. The results showed that normal beats and abnormal beats composed different clusters in most cases. In conclusion, the combination of wavelet transform and feature vectors has shown potential in detecting abnormalities in an ECG recording. It was also found that normalised energy and entropy are features, which are suitable for classification of ECG signals.
心电图（ECG）是最广为人知的生物信号之一。传统上，心电图记录由技术娴熟的医生在时域中进行分析。然而，病理状况在原始时域信号中可能并不总是显而易见的。傅里叶分析提供频率信息，但缺点是会丢失时间特征。小波分析既能提供时间信息又能提供频率信息，可克服这一局限性。在此，一种新方法，即小波分析与特征向量相结合的方法，被用于研究其作为诊断工具的适用性。对包含正常和异常搏动的心电图信号进行了检查。分析心电图信号有两个阶段：特征提取和特征分类。为了从心电图信号中提取特征，首先应用小波分解，并构建归一化能量和熵的特征向量。这些特征向量用于信号分类。结果表明，在大多数情况下，正常搏动和异常搏动构成不同的簇。总之，小波变换与特征向量相结合在检测心电图记录中的异常方面显示出潜力。还发现归一化能量和熵是适用于心电图信号分类的特征。
