基于CNN网络的孕妇腹电信号质量评估算法

Algorithm for Quality Evaluation of Pregnant Women’s Abdominal Electrical Signals Based on CNN Networks

胎儿心率(fetal heart rate, FHR)能够反映母亲子宫内胎儿的健康情况,也是胎儿监护的重要指标。无创胎儿心电监测是将电极置于孕妇腹部来采集腹部心电信号(abdomen electrocardiogram, AECG),经过信号处理可从AECG获取出胎儿心电信号(fetal electrocardiogram, FECG),根据获得的FECG可计算得到FHR。目前FECG提取存在着一些问题,如在AECG有母亲心电信号(maternal electrocardiogram, MECG)、基线漂移、工频干扰、采集噪声等,这些噪声会使AECG的信号质量差,最终影响FECG的提取效果。因此本文提出了一种基于CNN网络的AECG质量评估算法,通过该算法可以对AECG质量评估,筛选出信号质量比较好的AECG,从而提高FECG提取的准确率。通过对测试集的AECG质量评估,本文提出算法的灵敏度(SE)、阳性预测值(PPV)和F1值达到了97.76%、97.00%、97.38%,证明本文提出的方法可有效地对AECG质量评估。

Fetal heart rate (FHR) can reflect the health of the foetus in the mother’s womb and is an important indicator of foetal monitoring. Non-invasive fetal electrocardiography is based on placing electrodes on the abdomen of a pregnant woman to collect abdominal electrocardiogram (AECG), and after signal processing, fetal electrocardiogram (FECG) can be obtained from the AECG signals, and FHR can be calculated based on the FECG signals obtained. Currently there are some problems in FECG separation, such as maternal electrocardiogram (MECG) in AECG, baseline drift, IF interference, acquisition noise, etc., which will make the signal quality of AECG poor and ultimately affect the separation effect of FECG. Therefore, this paper proposes an AECG quality assessment algorithm based on deep learning CNN network, by which the AECG quality can be assessed and the AECGs with better signal quality can be screened out, so as to improve the accuracy of FECG extraction. By evaluating the AECG quality of the test set, the sensitivity (SE), positive predictive value (PPV), and F1 value of the proposed algorithm in this paper reached 97.76%, 97.00%, and 97.38%, which proves that the method proposed in this paper can be effective for AECG quality evaluation.