Lin Chao, Mailhes Corinne, Tourneret Jean-Yves
TéSA Laboratory, University of Toulouse, Toulouse 31071, France.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5868-71. doi: 10.1109/IEMBS.2011.6091451.
The problem of detecting T-wave alternans (TWA) in ECG signals has received considerable attention in the biomedical community. This paper introduces a Bayesian model for the T waves contained in ECG signals. A block Gibbs sampler was recently studied to estimate the parameters of this Bayesian model (including wave locations, amplitudes and shapes). This paper shows that the samples generated by this Gibbs sampler can be used efficiently for TWA detection via different statistical tests constructed from odd and even T-wave amplitude samples. The proposed algorithm is evaluated on real ECG signals subjected to synthetic TWA and compared with two classical algorithms.
在生物医学领域，心电图（ECG）信号中T波交替（TWA）的检测问题受到了广泛关注。本文介绍了一种针对ECG信号中T波的贝叶斯模型。最近研究了一种块吉布斯采样器来估计该贝叶斯模型的参数（包括波峰位置、振幅和形状）。本文表明，通过由奇数和偶数T波振幅样本构建的不同统计检验，该吉布斯采样器生成的样本可有效地用于TWA检测。该算法在添加了合成TWA的真实ECG信号上进行了评估，并与两种经典算法进行了比较。
