Gurve Dharmendra, Pant Jeevan K, Krishnan Sridhar
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:2794-2797. doi: 10.1109/EMBC.2017.8037437.
An improved method for separation of fetal electrocardiogram (fECG) from abdominal electrocardiogram (abdECG) is proposed in this paper. Proposed method combines two widely used techniques i.e. compressive sensing (CS) and independent component analysis (ICA). Separation of fECG is carried out by applying ICA directly on the compressed signal. The efficient improved ℓ-regularized least-sqaures (ℓ-RLS) algorithm is used for signal reconstruction, which provides better reconstruction quality and less processing time in comparison with other existing methods. The proposed algorithm is evaluated and tested on Physionet datasets which contain 75 records in set-A, 100 records in set-B and 6 records in Silesia dataset. The obtained outcomes reveal that proposed algorithm shows promising results (Sensitivity S=92%, Positive predictivity P+ = 93%, F1 measure = 92.5% with average percentage root-mean-square difference PRD =6.89% and Execution time= 2.91 sec.). The results also indicate that there is a substantial improvement in quality of reconstructed signal which is achieved by maintaining lowest PRD.
本文提出了一种从腹部心电图(abdECG)中分离胎儿心电图(fECG)的改进方法。该方法结合了两种广泛使用的技术,即压缩感知(CS)和独立成分分析(ICA)。通过直接对压缩信号应用ICA来进行fECG的分离。采用高效改进的ℓ正则化最小二乘法(ℓ-RLS)算法进行信号重建,与其他现有方法相比,该算法具有更好的重建质量和更短的处理时间。所提出的算法在Physionet数据集上进行了评估和测试,该数据集在A组包含75条记录,B组包含100条记录,西里西亚数据集包含6条记录。获得的结果表明,所提出的算法显示出有前景的结果(灵敏度S = 92%,阳性预测值P+ = 93%,F1测量值 = 92.5%,平均百分比均方根差PRD = 6.89%,执行时间 = 2.91秒)。结果还表明,通过保持最低的PRD,重建信号的质量有了显著提高。