Detection of T-wave Alternans in ECG Signals Using FRFT and Tensor Decomposition

T-wave alternans(TWA)refers to the periodic beat-to-beat variation in the amplitude of T-wave in the electrocardiogram(ECG)signal in an ABAB-pattern.TWA has been proven to be a very important indicator of malignant arrhythmia risk stratification.A new method to detect TWA by combining fractional Fourier transform(FRFT)and tensor decomposition is proposed.First,the T-wave vector is extracted from the ECG of each heartbeat,and its FRFT amplitudes at multiple orders are arranged to form a T-wave matrix.Then,a third-order tensor is composed of T-wave matrices of several consecutive heart beats.After tensor decomposition,projection matrices are obtained in three dimensions.The complexity of the projection matrix is measured by Shannon entropy to obtain feature vector to detect the presence of TWA.Results show that the sensitivity,specificity,and accuracy of the algorithm on the MIT-BIH database are 91.16%,94.25%,and 92.68%,respectively.This method effectively utilizes the fractional domain information of ECG,and shows the promising potential of the FRFT in ECG signal processing.

基于时频分析的T波交替检测算法的研究

心电的T波交替现象与室性心律失常有关,已成为预测心脏猝死的一个无创的临床指标。当前T波交替分析常用谱分析方法,为弥补其不具备时间分辨率的不足,本研究提出一种鲁棒的基于时频分析的微伏级T波交替检测算法:通过心电信号的短时傅里叶变换,提取时域T波序列,计算其在选定时频区域的能量谱;然后运用Wilcoxon秩和检验统计分析方法,检测微伏级T波交替现象。经仿真实验、欧洲ST-T数据及临床检测实验,本算法对T波交替检测的平均灵敏性达90.4%,正确预测率达92.0%,在30 dB及以上信噪比情况下,实现了100%的正确检测。仿真实验还表明本算法也支持短时心电数据的TWA准确检测。