Table 3 Sketch out of the foremost methods
From: Detection and Processing Techniques of FECG Signal for Fetal Monitoring
Signal | Method | Advantage/disadvantage |
|---|---|---|
Detection | Fourier transform | When the FECG is obscured by noise and the peak detection algorithm fails, a transform method might still detect the FHR proficiently |
| Â | Â | SNR is averagely high |
| Â | Â | In the case of weak signals having small duty cycle, this tool might sometimes fail to detect the average periodicity because of small correlation between the signals |
| Â | Least mean square | Feasible for fetal heart tone signature identification and analysis in the presence of background acoustic noise. |
| Â | Complex continuous wavelet transform (CCWT) | Performs well and the accuracy of the method is high |
| Â | Â | Algorithm's parameters increase the system's efficacy |
| Â | Â | Computationally fast and excels in performance |
| Â | Â | Able to extract the MHR signal, which can be useful for parallel monitoring of the mother's health |
Extraction | Wavelet transform (WT) | Coherent average can get more accurate reference |
| Â | Â | Can be obtained to smooth the baseline drift |
| Â | Â | Requires only one abdominal signal for fetal QRS extraction and maternal QRS cancellation |
| Â | Â | More flexible and effective tool for FHR signals denoising than the traditional filtering techniques |
|  | Time–frequency analysis | Three leads are used for FECG extraction |
| Â | Â | Spectrum produced by Wigner-Ville distribution (WVD) distribution displays very good localization properties |
| Â | Â | The main drawback of the method is the difficulty to extract the fetal R peaks in noisy background or in cases where the FECG is not distinguishable |
| Â | Artificial neural networks (ANN) | Very fast and does not involve human efforts for categorization |
| Â | Â | Neural networks can offer the computational power of non-linear techniques |
| Â | Â | Sometimes it does not estimate the exact baseline value and its precision is limited by the number of classes |
| Â | ICA and BSS | Relatively, SNR is high |
| Â | Â | Efficient both in batch and on-line operation modes |
| Â | Â | Fast and efficient approach for the preprocessing of multiple signals of interest |
| Â | Â | No specific prior knowledge required in order to identify components generated from different sources |
| Â | Â | Often require a large number of recorded leads to reach reliable FECG extraction |