access icon free Foetal ECG extraction by support vector regression

A new method to extract the foetal electrocardiogram (ECG) using an abdominal ECG signal and a thoracic ECG signal of the mother is presented. The nonlinear mapping of the maternal ECG component from thoracic signal to an abdominal signal is approximated by the support vector regression (SVR) method. The foetal ECG signal is then extracted by subtracting the mapped thoracic signal from an abdominal signal. In an SVR algorithm, the nonlinear regression problem in an original signal space is transformed into a linear regression problem in high-dimensional Hilbert space by kernel technology. It avoids the ‘overfitting’ problem and could obtain good results for the small sample training set. The validity of the proposed method is validated by the real ECG signals, and compared with the existing method.

Inspec keywords: medical signal processing; regression analysis; electrocardiography; Hilbert spaces; support vector machines

Other keywords: ECG signal; SVR method; thoracic signal; foetal electrocardiogram; abdominal ECG signal; foetal ECG extraction; support vector regression; linear regression problem; high-dimensional Hilbert space

Subjects: Other topics in statistics; Probability theory, stochastic processes, and statistics; Digital signal processing; Knowledge engineering techniques; Biology and medical computing; Other topics in statistics; Bioelectric signals; Signal processing and detection; Electrodiagnostics and other electrical measurement techniques

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