## 3 Experimental Data and Evaluation of Results
### Source of Experimental Data
#### 3.1.1 Synthetic Data  
In the literature [28], a standard heart model based on a single dipole vector was utilised to simulate MECGs and FECGs. Gaussian white noise was added to synthesise an abdominal signal. The open source toolkit FECGSYN [29; 30] in the Open-Source Electrophysiological Toolbox (OSET) can also be used to simulate MECGs, FECGs, or non-invasive FECGs. Although the toolkit can be used to simulate signals of any parameters, it cannot fully simulate the nonlinear and nonstationary characteristics of the complex human body system. In this paper, we chose existing real databases to provide the basic data, such as abdominal and direct fetal ECG database (ADFECGDB) [31], non-invasive fetal ECG database (NIFECGDB) [32], database for the identification of systems (DaISy) [33], and HIT-BIH arrhythmia database (MIT-BIH) [34]. The direct scalp FECGs, thoracic MECGs and adult ECGs are preprocessed by filtering and eliminating baseline wandering before they can be used for synthesising abdominal signals. Table 1 lists the signals selected for this paper. Figure 4 shows the processed results of the direct scalp FECGs in the ADFECGDB database. The red line represents the original scalp FECG and the black line represents the processed FECG. The FECGs, after being preprocessed, should have complete QRS waveforms and accurate positions and amplitudes of R peaks, with smooth waveforms and clear P and T waves. In addition, the sampling frequency of ECG signals from the DaISy and MIT-BIH databases was different to that from ADFECGDB and NIFECGDB--250 and 360 Hz, respectively. For data synthesis, the sampling frequency should be uniformly increased to  
Figure 3: Flow chart of the proposed method. AECG is abdominal ECG and MECG is maternal ECG.  
1 kHz. Figure 5 shows a comparison of the waveform and spectrum before and after resampling of Ch8 in the DaISy database. Figure 6 shows the waveform and spectrum comparison before and after resampling of a103 in the MIT-BIH database. It can be seen that the waveform and the spectrum are consistent before and after resampling.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ECG** & **Database** & **Electrode Position** & **Number and Channel** & **Average Heart Rate (ppm)** & **Sampling Rate (Hz)** & **Data Length (s)** & **Waveform Similarity (C\({}_{\text{R}}\))** \\ \hline \multirow{4}{*}{FECG} & \multirow{4}{*}{ADFECGDB} & \multirow{4}{*}{Scalp} & r01 & 128 & & & 0.962 \\  & & & r04 & 124 & & & 0.949 \\  & & & r07 & 127 & 1000 & 10 & 0.950 \\  & & & r08 & 128 & & & 0.981 \\  & & & r10 & 117 & & & 0.919 \\ \hline \multirow{4}{*}{MECG} & \multirow{4}{*}{DaISy} & \multirow{4}{*}{Chest} & Ch6 & 82 & & & 0.995 \\  & & & Ch7 & 82 & 250 & 10 & 0.997 \\  & & & Ch8 & 82 & & & 0.996 \\ \hline \multirow{4}{*}{MECG} & \multirow{4}{*}{NIFECGDB} & \multirow{4}{*}{Chest} & ca473 & 89 & & & 0.993 \\  & & & ca585 & 89 & & & 0.992 \\  & & & ca649 & 96 & & & 0.996 \\  & & & ca748 & 91 & & & 0.991 \\  & & & ca771 & 87 & & & 0.992 \\  & & & ca840 & 85 & & & 0.992 \\  & & & ca886 & 92 & & & 0.995 \\  & & & ca902 & 90 & & & 0.991 \\ \hline \multirow{4}{*}{Adult ECG} & \multirow{4}{*}{MIT-BIH} & \multirow{4}{*}{Chest} & a100 & 73 & & & 0.998 \\  & & & a101 & 68 & & & 0.998 \\  & & & a103 & 67 & & & 0.999 \\  & & & a112 & 87 & & & 0.996 \\  & & & a113 & 58 & 360 & 10 & 0.999 \\  & & & a115 & 63 & & & 0.999 \\  & & & a117 & 50 & & & 0.997 \\  & & & a119 & 65 & & & 0.998 \\  & & & a122 & 87 & & & 0.998 \\  & & & a123 & 45 & & & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 1: FECGs and MECGs used for synthesis signals.  
Figure 4: Direct scalp FECGs after filtering and eliminating baseline wandering in ADFECGDB.