s3 and s4 are FECG and MECG with super-Gaussian distribution, respectively.
This means that both methods successfully extract the FECG and MECG with the same low IPI.
Accordingly, the FECG, respiratory motion artifacts, and the MECG were visible in these recordings.
The impulse series whose occurrence time is the same as the peaks of any channel signal were used as reference for extracting the MECG.
The reference signal and the MECG signal extracted by both methods are shown in Figure 7, where the reference signal is also described as Ref, the MECG extracted by the proposed method is denoted as A, the MECG extracted by the previous method is denoted as B, and both extracted MECG signals are redescribed in C with different color curves.
Caption: Figure 4: Reference signal and extracted artificial MECG signal.
According to the concept of TDL, the input signal MECG enters and passes through the N-1 delays and the output of the TDL is an N-dimensional vector, made up of the input signal at the current time, the previous signal, which is feed to the ADALINE.
However, the best the network can predict is the MECG in AECG of pregnant woman due to the correlation between two signals.
The QRS detection in the MECG was done by using the Backpropogation Neural Network approach.
After detecting, the QRS complex of MECG in the AECG, the next task would be extracted the FECG.