Panigrahy D, Sahu P K
Department of Electrical Engineering, NIT Rourkela, Rourkela, Odisha, India.
Australas Phys Eng Sci Med. 2017 Mar;40(1):191-207. doi: 10.1007/s13246-017-0527-5. Epub 2017 Feb 16.
This paper proposes a five-stage based methodology to extract the fetal electrocardiogram (FECG) from the single channel abdominal ECG using differential evolution (DE) algorithm, extended Kalman smoother (EKS) and adaptive neuro fuzzy inference system (ANFIS) framework. The heart rate of the fetus can easily be detected after estimation of the fetal ECG signal. The abdominal ECG signal contains fetal ECG signal, maternal ECG component, and noise. To estimate the fetal ECG signal from the abdominal ECG signal, removal of the noise and the maternal ECG component presented in it is necessary. The pre-processing stage is used to remove the noise from the abdominal ECG signal. The EKS framework is used to estimate the maternal ECG signal from the abdominal ECG signal. The optimized parameters of the maternal ECG components are required to develop the state and measurement equation of the EKS framework. These optimized maternal ECG parameters are selected by the differential evolution algorithm. The relationship between the maternal ECG signal and the available maternal ECG component in the abdominal ECG signal is nonlinear. To estimate the actual maternal ECG component present in the abdominal ECG signal and also to recognize this nonlinear relationship the ANFIS is used. Inputs to the ANFIS framework are the output of EKS and the pre-processed abdominal ECG signal. The fetal ECG signal is computed by subtracting the output of ANFIS from the pre-processed abdominal ECG signal. Non-invasive fetal ECG database and set A of 2013 physionet/computing in cardiology challenge database (PCDB) are used for validation of the proposed methodology. The proposed methodology shows a sensitivity of 94.21%, accuracy of 90.66%, and positive predictive value of 96.05% from the non-invasive fetal ECG database. The proposed methodology also shows a sensitivity of 91.47%, accuracy of 84.89%, and positive predictive value of 92.18% from the set A of PCDB.
本文提出了一种基于五个阶段的方法，用于使用差分进化（DE）算法、扩展卡尔曼平滑器（EKS）和自适应神经模糊推理系统（ANFIS）框架从单通道腹部心电图中提取胎儿心电图（FECG）。在估计胎儿心电图信号后，可以轻松检测到胎儿的心率。腹部心电图信号包含胎儿心电图信号、母体心电图成分和噪声。为了从腹部心电图信号中估计胎儿心电图信号，有必要去除其中存在的噪声和母体心电图成分。预处理阶段用于去除腹部心电图信号中的噪声。EKS框架用于从腹部心电图信号中估计母体心电图信号。开发EKS框架的状态方程和测量方程需要母体心电图成分的优化参数。这些优化的母体心电图参数由差分进化算法选择。母体心电图信号与腹部心电图信号中可用的母体心电图成分之间的关系是非线性的。为了估计腹部心电图信号中实际存在的母体心电图成分并识别这种非线性关系，使用了ANFIS。ANFIS框架的输入是EKS的输出和预处理后的腹部心电图信号。通过从预处理后的腹部心电图信号中减去ANFIS的输出，计算出胎儿心电图信号。使用无创胎儿心电图数据库和2013年生理网/心脏病学计算挑战数据库（PCDB）的A组进行所提方法的验证。所提方法在无创胎儿心电图数据库中的灵敏度为94.21%，准确率为90.66%，阳性预测值为96.05%。所提方法在PCDB的A组中的灵敏度也为91.47%，准确率为84.89%，阳性预测值为92.18%。
