Denoising of the fetal heart rate signal with non-linear filtering of the wavelet transform maxima.

The fetal heart rate (FHR) signal provides valuable information for fetal development and well-being. However, the FHR traces derived from present-day ultrasound cardiotocographs are not of the desired quality. The paper applies the wavelet transform (WT) in order to denoise effectively the FHR signal. The denoising procedure analyses the evolution of the WT maxima across scales. The singularities of the signal create wavelet maxima with different properties from those of the induced noise. Since it is difficult to formulate precise rules that distinguish between the wavelet maxima of the FHR signal from those of the noise we have trained a neural network for this classification task. The neural network draws out successfully the noise induced wavelet maxima. An improved FHR signal can be obtained from the coarser wavelet approximation signal component and the filtered wavelet maxima by means of the inverse dyadic wavelet transform. Also, feature extraction and processing algorithms can be defined on the denoised wavelet coefficients (instead of on the original signal).

The performance and reliability of wavelet denoising for Doppler ultrasound fetal heart rate signal preprocessing.

The present paper deals with the performance and the reliability of a Wavelet Denoising method for Doppler ultrasound Fetal Heart Rate (FHR) recordings. It displays strong evidence that the denoising process extracts the actual noise components. The analysis is approached with three methods. First, the power spectrum of the denoised FHR displays more clearly an 1/fa scaling law, i.e. the characteristic of fractal time series. Second, the rescaled scale analysis technique reveals a Hurst exponent at the range of 0.7-0.8 that corresponds to a long memory persistent process. Moreover, the variance of the Hurst exponent across time scales is smaller at the denoised signal. Third, a chaotic attractor reconstructed with the embedding dimension technique becomes evident at the denoised signals, while it is completely obscured at the unfiltered ones.