Vayá Carlos, Rieta José J, Sánchez César, Moratal David
Department of Innovation in Bioengineering, Castilla-la Mancha University, Escuela Politécnica Superior de Cuenca, Camino del Pozuelo s/n, 16071 Cuenca, Spain.
IEEE Trans Biomed Eng. 2007 Aug;54(8):1530-3. doi: 10.1109/TBME.2006.889778.
The analysis of the surface electrocardiogram (ECG) is the most extended noninvasive technique in medical diagnosis of atrial fibrillation (AF). In order to use the ECG as a tool for the analysis of AF, we need to separate the atrial activity (AA) from other cardioelectric signals. In this matter, statistical signal processing techniques, like blind source separation (BSS), are able to perform a multilead statistical analysis with the aim to obtain the AA. Linear BSS techniques can be divided in two groups depending on the mixing model: algorithms where instantaneous mixing of sources is assumed, and convolutive BSS (CBSS) algorithms. In this work, a comparison of performance between one relevant CBSS algorithm, namely Infomax, and one of the most effective independent component analysis (ICA) algorithms, namely FastICA, is developed. To carry out the study, pseudoreal AF ECGs have been synthesized by adding fibrillation activity to normal sinus rhythm. The algorithm performances are expressed by two indexes: the signal to interference ratio (SIRAA) and the cross-correlation (RAA) between the original and the estimated AA. Results empirically prove that the instantaneous mixing model is the one that obtains the best results in the AA extraction, given that the mean SIRAA obtained by the FastICA algorithm (37.6 +/- 17.0 dB) is higher than the main SIRAA obtained by Infomax (28.5 +/- 14.2 dB). Also the RAA obtained by FastICA (0.92 +/- 0.13) is higher than the one obtained by Infomax (0.78 +/- 0.16).
体表心电图（ECG）分析是心房颤动（AF）医学诊断中应用最广泛的非侵入性技术。为了将ECG用作分析AF的工具，我们需要将心房活动（AA）与其他心电信号分离。在这方面，统计信号处理技术，如盲源分离（BSS），能够进行多导联统计分析以获取AA。线性BSS技术可根据混合模型分为两组：假设源信号瞬时混合的算法和卷积BSS（CBSS）算法。在这项工作中，对一种相关的CBSS算法即Infomax与最有效的独立成分分析（ICA）算法之一即FastICA的性能进行了比较。为开展该研究，通过将颤动活动添加到正常窦性心律中来合成伪真实AF ECG。算法性能由两个指标表示：信号干扰比（SIRAA）以及原始AA与估计AA之间的互相关（RAA）。结果通过实验证明，在AA提取方面，瞬时混合模型能取得最佳结果，因为FastICA算法获得的平均SIRAA（37.6 +/- 17.0 dB）高于Infomax获得的主要SIRAA（28.5 +/- 14.2 dB）。此外，FastICA获得的RAA（0.92 +/- 0.13）高于Infomax获得的RAA（0.78 +/- 0.16）。
