RobustICA, Kurtosis- and Negentropy-Based FastICA in Maternal-Fetal ECG Separation

To separate maternal-fetal ECG is crucial for fetal ECG collection in non-invasive methods. Three ICA (Independent Component Analysis) methods, RobustICA, kurtosis-based and negentropy-based FastICA, are employed to extract fetal ECG in our contribution. Synthesized maternal-fetal ECG mixed by heart waves from the Physionet and real-world maternal-fetal ECG from the DaISy are employed to test the three algorithms. Test results show that the three ICA methods all effectively extract maternal and fetal ECG from the mixed signals. RobustICA has greater advantages in separation speed and accuracy, while its stability, indicated by larger standard deviation, is lower than the two FastICA algorithms. FastICA based on negentropy has higher accuracy and robustness than the kurtosis-based FastICA, but it is inferior in separation speed.

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