Adaptive filter theory (2nd ed.)
Adaptive filter theory (2nd ed.)
Finite impulse response neural networks with applications in time series prediction
Finite impulse response neural networks with applications in time series prediction
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
An effective algorithm for enhancing a non-invasive fetal electrocardiogram
An effective algorithm for enhancing a non-invasive fetal electrocardiogram
Neural networks with dynamic synapses for time-series prediction
Neural networks with dynamic synapses for time-series prediction
Adaptation of memory depth in the gamma filter
ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Generalized feed-forward filters: some theoretical results
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: digital speech processing - Volume III
The gamma-filter-a new class of adaptive IIR filters withrestricted feedback
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Survey on the use of smart and adaptive engineering systems in medicine
Artificial Intelligence in Medicine
Using the Wavelet Transform for T-wave alternans detection
Mathematical and Computer Modelling: An International Journal
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Non-invasive electrocardiography has proven to be a very interesting method for obtaining information about the foetus state and thus to assure its well-being during pregnancy. One of the main applications in this field is foetal electrocardiogram (ECG) recovery by means of automatic methods. Evident problems found in the literature are the limited number of available registers, the lack of performance indicators, and the limited use of non-linear adaptive methods. In order to circumvent these problems, we first introduce the generation of synthetic registers and discuss the influence of different kinds of noise to the modelling. Second, a method which is based on numerical (correlation coefficient) and statistical (analysis of variance, ANOVA) measures allows us to select the best recovery model. Finally, finite impulse response (FIR) and gamma neural networks are included in the adaptive noise cancellation (ANC) scheme in order to provide highly non-linear, dynamic capabilities to the recovery model. Neural networks are benchmarked with classical adaptive methods such as the least mean squares (LMS) and the normalized LMS (NLMS) algorithms in simulated and real registers and some conclusions are drawn. For synthetic registers, the most determinant factor in the identification of the models is the foetal-maternal signal-to-noise ratio (SNR). In addition, as the electromyogram contribution becomes more relevant, neural networks clearly outperform the LMS-based algorithm. From the ANOVA test, we found statistical differences between LMS-based models and neural models when complex situations (high foetal-maternal and foetal-noise SNRs) were present. These conclusions were confirmed after doing robustness tests on synthetic registers, visual inspection of the recovered signals and calculation of the recognition rates of foetal R-peaks for real situations. Finally, the best compromise between model complexity and outcomes was provided by the FIR neural network. Both the methodology for selecting a model and the introduction of advanced neural models are the main contributions of this paper.