Soydan REDIF

Fetal electrocardiogram estimation using polynomial eigenvalue decomposition

In this paper, we propose the application of polynomial matrix eigenvalue decomposition (PEVD) to the problem of fetal electrocardiogram (ECG) extraction from real ECG recordings obtained from abdominal leads. We model the fetal ECG extraction problem as a broadband sensor array signal processing problem in order to account for the broadband nature of the ECG noise present in the recordings. An algorithm for providing an approximate PEVD is used in order to estimate the broadband noise subspace. Suppression of the broadband noise and maternal ECG is achieved by carrying out an orthonormal projection of the recordings onto the estimated fetal subspace. The proposed scheme was evaluated with multichannel synthetic ECG signals and real ECG data from the PhysioNet Challenge database and is shown to perform favorably as compared to prior-art methods. Results indicate that our method is more robust than the prior-art ones for the task of fetal ECG estimation.

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