Adaptive Linear Neuron for Fetal Electrocardiogram Extraction

Kahánková, Radana; Martínek, Radek; Mikolasova, Martina; Jaroš, René

doi:10.1109/healthcom.2018.8531135

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…Based on the research by Amin et al [115] and Reaz et al [116], the algorithm worked efficiently, if the high learning rate with the value of 0.8, the low momentum with the value of 0.5, and very small non-zero initial weights were used. Nevertheless, according to the results of Kahankova et al [51], the lower learning rate with the value of 0.07 performed better. The optimal value of the input space dimensionality lay in the interval 20-30.…”

Section: Artificial Neural Network In Noise Suppressionmentioning

confidence: 91%

“…The simplest model of feed-forward network for binary classification composed of a single neuron is called a perceptron or a more powerful adaptive linear neuron (ADALINE) [51,52]. Due to the limited use of perceptron, this model was expanded to a multilayer perceptron (MLP), which represents a special type of multilayer network where each layer is fully connected (each layer neuron is connected with all neurons in the preceding layer) [52].…”

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Barnova,

Martinek,

Vilimkova Kahankova

et al. 2024

Arch Computat Methods Eng

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Electronic fetal monitoring is used to evaluate fetal well-being by assessing fetal heart activity. The signals produced by the fetal heart carry valuable information about fetal health, but due to non-stationarity and present interference, their processing, analysis and interpretation is considered to be very challenging. Therefore, medical technologies equipped with Artificial Intelligence algorithms are rapidly evolving into clinical practice and provide solutions in the key application areas: noise suppression, feature detection and fetal state classification. The use of artificial intelligence and machine learning in the field of electronic fetal monitoring has demonstrated the efficiency and superiority of such techniques compared to conventional algorithms, especially due to their ability to predict, learn and efficiently handle dynamic Big data. Combining multiple algorithms and optimizing them for given purpose enables timely and accurate diagnosis of fetal health state. This review summarizes the currently used algorithms based on artificial intelligence and machine learning in the field of electronic fetal monitoring, outlines its advantages and limitations, as well as future challenges which remain to be solved.

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Section: Artificial Neural Network In Noise Suppressionmentioning

confidence: 91%

Section: Artificial Intelligence Methodsmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Barnova,

Martinek,

Vilimkova Kahankova

et al. 2024

Arch Computat Methods Eng

Self Cite

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“…The main parameters for ADALINE system and suggested settings according to [174]- [177] are: Fig. 9.…”

Section: R Step Sizementioning

confidence: 99%

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Kahánková

Martínek

Jaroš

et al. 2020

IEEE Rev. Biomed. Eng.

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Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

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“…The third baseline is adaptive linear network (ADALINE). ADALINE utilizes neural networks adaptable to the nonlinear time-varying properties of the ECG signal [29]. The fourth baseline is based on echo state network (ESN) [26].…”

Section: Fetal Ecg Extractionmentioning

confidence: 99%

Discovery of Single Independent Latent Variable

Shaham¹,

Svirsky²,

Katz³

et al. 2021

Preprint

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Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science. In this work, we consider data given as an invertible mixture of two statistically independent components, and assume that one of the components is observed while the other is hidden. Our goal is to recover the hidden component. For this purpose, we propose an autoencoder equipped with a discriminator. Unlike the standard nonlinear ICA problem, which was shown to be non-identifiable, in the special case of ICA we consider here, we show that our approach can recover the component of interest up to entropy-preserving transformation. We demonstrate the performance of the proposed approach on several datasets, including image synthesis, voice cloning, and fetal ECG extraction.

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Adaptive Linear Neuron for Fetal Electrocardiogram Extraction

Cited by 6 publications

References 11 publications

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Discovery of Single Independent Latent Variable

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