Table 3.
Sketch out of the foremost methods
| 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 |
| SNR is averagely high | ||
| 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 | ||
| Least mean square | Feasible for fetal heart tone signature identification and analysis in the presence of background acoustic noise. | |
| Complex continuous wavelet transform (CCWT) | Performs well and the accuracy of the method is high | |
| Algorithm's parameters increase the system's efficacy | ||
| Computationally fast and excels in performance | ||
| 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 |
| Can be obtained to smooth the baseline drift | ||
| Requires only one abdominal signal for fetal QRS extraction and maternal QRS cancellation | ||
| More flexible and effective tool for FHR signals denoising than the traditional filtering techniques | ||
| Time–frequency analysis | Three leads are used for FECG extraction | |
| Spectrum produced by Wigner-Ville distribution (WVD) distribution displays very good localization properties | ||
| 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 | ||
| Artificial neural networks (ANN) | Very fast and does not involve human efforts for categorization | |
| Neural networks can offer the computational power of non-linear techniques | ||
| Sometimes it does not estimate the exact baseline value and its precision is limited by the number of classes | ||
| ICA and BSS | Relatively, SNR is high | |
| Efficient both in batch and on-line operation modes | ||
| Fast and efficient approach for the preprocessing of multiple signals of interest | ||
| No specific prior knowledge required in order to identify components generated from different sources | ||
| Often require a large number of recorded leads to reach reliable FECG extraction |