Innovation in Bioengineering Research Group, University of Castilla-La Mancha, Spain.
Comput Biol Med. 2013 Feb;43(2):154-63. doi: 10.1016/j.compbiomed.2012.11.007. Epub 2012 Dec 8.
The most extended noninvasive technique for medical diagnosis and analysis of atrial fibrillation (AF) relies on the surface elctrocardiogram (ECG). In order to take optimal profit of the ECG in the study of AF, it is mandatory to separate the atrial activity (AA) from other cardioelectric signals. Traditionally, template matching and subtraction (TMS) has been the most widely used technique for single-lead ECGs, whereas multi-lead ECGs have been addressed through statistical signal processing techniques, like independent component analysis. In this contribution, a new QRST cancellation method based on a radial basis function (RBF) neural network is proposed. The system is able to provide efficient QRST cancellation and can be applied both to single and multi-lead ECG recordings. The learning algorithm used for training the RBF makes use of a special class of network, known as cosine RBF, by updating selected adjustable parameters to minimize the class-conditional variances at the outputs of the network. The experiments verify that RBFs trained by the proposed learning algorithm are capable of reducing the QRST complex dramatically, a property that is not shared by other methods and conventional feed-forward neural networks. Average Results (mean ± std) for the RBF method in cross-correlation (CC) between original and estimated AA are CC=0.95±0.038 being the mean square error (MSE) for the same signals, MSE=0.311±0.078. Regarding spectral parameters, the dominant amplitude (DA) and the mean power spectral (MP) were DA=1.15±0.18 and MP=0.31±0.07, respectively. In contrast, traditional TMS-based methods yielded, for the best case, CC=0.864±0.041, MSE=0.577±0.097, DA=0.84±0.25 and MP=0.24±0.07. The results prove that the RBF based method is able to obtain a remarkable reduction of ventricular activity and a very accurate preservation of the AA, thus providing high quality dissociation between atrial and ventricular activities in AF recordings.
用于心房颤动 (AF) 医学诊断和分析的最广泛的非侵入性技术依赖于体表心电图 (ECG)。为了在 AF 的研究中充分利用 ECG，必须将心房活动 (AA) 与其他心电信号分离。传统上，模板匹配和减法 (TMS) 一直是单导联 ECG 最广泛使用的技术，而多导联 ECG 则通过统计信号处理技术（如独立成分分析）来解决。在本贡献中，提出了一种基于径向基函数 (RBF) 神经网络的新的 QRST 消除方法。该系统能够提供有效的 QRST 消除，可应用于单导联和多导联 ECG 记录。用于训练 RBF 的学习算法利用了一种特殊的网络，称为余弦 RBF，通过更新选定的可调参数来最小化网络输出处的类条件方差。实验验证了由所提出的学习算法训练的 RBF 能够显著降低 QRST 复杂度，这是其他方法和传统前馈神经网络所不具备的特性。RBF 方法在原始和估计 AA 之间的互相关 (CC) 中的平均结果（均值±标准差）为 CC=0.95±0.038，相同信号的均方误差 (MSE) 为 MSE=0.311±0.078。关于谱参数，主导幅度 (DA) 和平均功率谱 (MP) 分别为 DA=1.15±0.18 和 MP=0.31±0.07。相比之下，基于传统 TMS 的方法在最佳情况下产生的 CC=0.864±0.041，MSE=0.577±0.097，DA=0.84±0.25 和 MP=0.24±0.07。结果证明，基于 RBF 的方法能够显著降低心室活动，并非常准确地保留 AA，从而在 AF 记录中提供心房和心室活动的高质量分离。
