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Hybrid BSS techniques for fetal ECG extraction using a semi-synthetic database

机译:使用半合成数据库的胎儿ECG提取的混合BSS技术

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Noninvasive fetal ECG monitoring makes use of electrodes placed on the mother's abdomen with sophisticated algorithms to separate fetal and mother activity. In this task often Blind Source Separation is used as an intermediate step. To solve Blind Source Separation problem is generally employed PCA and ICA. COMBI and MULTI-COMBI algorithms offer novel schemes for combining PCA and ICA, enabling exploit the strengths of both techniques. In this work, the performance of the algorithms COMBI, MULTICOMBI, and traditional JADE algorithm are compared. We used a semi-synthetic database compound of 26 abdominal ECG records, where real 3-D ECG signals representing maternal and fetal cardiac components are mixed by volume conduction transfer matrices, in which, the fetal to mother ratio SIR and fetal to noise ratio SNR are controlled. As performance parameter, we used the signal to error relation SER, varying SIR between -5dB to -30dB, and SNR between 0dB to 30dB in all possible combinations. In all case, white noise or pink noise included, it is found that the COMBI and MULTICOMBI algorithms show better performance than the JADE algorithm.
机译:非侵入性胎儿ECG监测利用将母亲腹部的电极进行复杂的算法,以将胎儿和母亲活动分开。在此任务中,通常将盲源分离用作中间步骤。为了解决盲来源分离问题通常采用PCA和ICA。 Combi和Muld-Combi算法提供了用于组合PCA和ICA的新颖方案,使利用两种技术的优点。在这项工作中,比较了算法组合,多组织和传统玉石算法的性能。我们使用了26个腹部心电图记录的半合成数据库化合物,其中表示母体和胎儿心脏组分的真实3-D ECG信号由体积传导转移矩阵混合,其中胎儿与母比SIR和胎儿到噪声比SNR控制。作为性能参数,我们使用信号与错误关系SER,在-5db到-30db之间变化的SIR,以及所有可能的组合的SNR到30dB之间的SNR。在所有情况下,包括白色噪声或粉红色噪声,发现Combi和Multicombi算法比玉算法更好。

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