Li Hongqiang, An Zhixuan, Zuo Shasha, Zhu Wei, Cao Lu, Mu Yuxin, Song Wenchao, Mao Quanhua, Zhang Zhen, Li Enbang, García Juan Daniel Prades
Tianjin Key Laboratory of Optoelectronic Detection Technology and Systems, School of Electronics and Information Engineering, Tiangong University, Tianjin, China.
Textile Fiber Inspection Center, Tianjin Product Quality Inspection Technology Research Institute, Tianjin, China.
Med Biol Eng Comput. 2022 Jan;60(1):109-119. doi: 10.1007/s11517-021-02461-4. Epub 2021 Oct 30.
Electrocardiogram (ECG) indicates the occurrence of various cardiac diseases, and the accurate classification of ECG signals is important for the automatic diagnosis of arrhythmia. This paper presents a novel classification method based on multiple features by combining waveform morphology and frequency domain statistical analysis, which offer improved classification accuracy and minimise the time spent for classifying signals. A wavelet packet is used to decompose a denoised ECG signal, and the singular value, maximum value, and standard deviation of the decomposed wavelet packet coefficients are calculated to obtain the frequency domain feature space. The slope threshold method is applied to detect R peak and calculate RR intervals, and the first two RR intervals are extracted as time-domain features. The fusion feature space is composed of time and frequency domain features. A combination of support vector machine (SVM) with the help of grid search and waveform morphological analysis is applied to complete nine types of ECG signal classification. Computer simulations show that the accuracy of the proposed algorithm on multiple types of arrhythmia databases can reach 96.67%.
心电图（ECG）可指示各种心脏疾病的发生，而ECG信号的准确分类对于心律失常的自动诊断至关重要。本文提出了一种基于多种特征的新颖分类方法，该方法结合了波形形态和频域统计分析，提高了分类准确率并减少了信号分类所需的时间。使用小波包对去噪后的ECG信号进行分解，并计算分解后的小波包系数的奇异值、最大值和标准差，以获得频域特征空间。应用斜率阈值法检测R波峰并计算RR间期，提取前两个RR间期作为时域特征。融合特征空间由时域和频域特征组成。借助网格搜索的支持向量机（SVM）与波形形态分析相结合，用于完成九种类型的ECG信号分类。计算机仿真表明，该算法在多种心律失常数据库上的准确率可达96.67%。
