Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

In order to design an efficient resonant power supply for ozone gas generator, it is necessary to accurately determine the parameters of the ozone chamber. In the conventional method, the information from Lissajous plot is used to estimate the values of these parameters. However, the experimental setup for this purpose can only predict the parameters at one operating frequency and there is no guarantee that it results in the highest ozone gas yield. This paper proposes a new approach to determine the parameters using a search and optimization technique known as Differential Evolution (DE). The desired objective function of DE is set at the resonance condition and the chamber parameter values can be searched regardless of experimental constraints. The chamber parameters obtained from the DE technique are validated by experiment.

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“…The equivalent parallel capacitance C p can be calculated by combining (14) and (18), resulting in the following equation:…”

Section: Proposed Modelling Methods Using Dementioning

confidence: 99%

A Model Parameter Extraction Method for Dielectric Barrier Discharge Ozone Chamber using Differential Evolution

Amjad

Salam

Ishaque

2014

Measurement Science Review

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“…Chebyshev neural network is employed to approximate the soft fouling and hard fouling, due to its excellent generalization performance and dynamic prediction ability [13]- [14], the structure is shown in Fig.2. Chebyshev neural network is a multi input single output system, all the neural-weights between input-layer and hidden-layer are set to be 1, and the neural-weights between hidden-layer and output-layer are j W , ( 0,1,2, , -1)…”

Section: F B Chebyshev Neural Networkmentioning

confidence: 99%

Prediction of Fouling in Condenser Based on Fuzzy Stage Identification and Chebyshev Neural Network

Zhong¹

2013

Measurement Science Review

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The prediction of fouling in condenser is heavily influenced by the periodic fouling process and dynamics change of the operational parameters, to deal with this problem, a novel approach based on fuzzy stage identification and Chebyshev neural network is proposed. In the approach, the overall fouling is separated into hard fouling and soft fouling, the variation trends of these two kinds of fouling are approximated by using Chebyshev neural network, respectively, in order to make the prediction model more accurate and robust, a fuzzy stage identification method and adaptive algorithm considering external disturbance are introduced, based on the approach, a prediction model is constructed and experiment on an actual condenser is carried out, the results show the proposed approach is more effective than asymptotic fouling model and adaptive parameter optimization prediction model.

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“…FHR monitoring is a routine work for obtaining the significant information about the condition of a fetus during the pregnancy and labor period of a mother (HASAN et al, 2007). FECG characteristics (heart rate, dynamic behavior, and waveform) are convenient in determining the fetal life, fetal maturity, fetal development, and existence of fetal distress or congenital heart disease (HASAN et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…FECG characteristics (heart rate, dynamic behavior, and waveform) are convenient in determining the fetal life, fetal maturity, fetal development, and existence of fetal distress or congenital heart disease (HASAN et al, 2007). FHR variations observed during pregnancy and labors have commonly been used as indirect indications of fetal conditions.…”

Section: Introductionmentioning

confidence: 99%

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BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring

Hasan

Mamun

2013

Acta Sci. Technol.

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ABSTRACT. Fetal monitoring may help with possible recognition of problems in the fetus. This research work focuses on the design of the Back-propagation Neural Network (BPNN) and Adaptive Linear Neural Network (ADALINE) to extract the Fetal Electrocardiogram (FECG) from the Abdominal ECG (AECG). FECG is extracted to assess the fetus well-being during the pregnancy period of a mother to overcome some existing difficulties regarding the fetal heart rate (FHR) monitoring system. Different sets of ECG signal has been tested to validate the algorithm performance. The accuracy of the QRS detection using the designed algorithm is 99%. This research work further made a comparison study between various methods' performance and accuracy and found that the developed algorithm gives the highest accuracy. This paper opens up a passage to biomedical scientists, researchers, and end users to advocate to extract the FECG signal from the AECG signal for FHR monitoring system by providing valuable information to help them for developing more dominant, flexible and resourceful applications.Keywords: fetal electrocardiogram, QRS complex, neural network, artificial intelligence, fetal heart rate.ECG materno baseado em rede neural backpropagation a partir de sinal abdominal para monitoramento fetal contínuo RESUMO. O monitoramento fetal pode auxiliar o reconhecimento de problemas no feto. Este estudo objetivou a concepção da Rede Neural Backpropagation (RNBP) para obter o eletrocardiograma fetal (ECGF) a partir do ECG abdominal (ECGA). O ECGF é obtido para avaliar o bem estar fetal durante a gravidez a fim de superar alguma dificuldade durante o monitoramento da frequência cardíaca do feto (FCF). Diferentes séries de sinais de ECG foram testados para avaliar o desempenho do algoritmo. A precisão da detecção de QRS usando o algoritmo concebido é de 99%. O presente estudo também comparou o desempenho e precisão de vários métodos e observou que o algoritmo projetado fornece a maior acurácia. Este trabalho permite que cientistas biomédicos, pesquisadores e usuários finais defendam o uso do sinal do ECGF ao invés do sinal do ECGA para monitorar a FCF, fornecendo valiosas informações para o desenvolvimento de aplicações mais dominantes, flexíveis e criativas.Palavras-chave: eletrocardiograma fetal, complexo QRS, rede neural, inteligência artificial, frequência cardíaca fetal.

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Hardware Prototyping of Neural Network based Fetal Electrocardiogram Extraction

Cited by 18 publications

References 15 publications

A Model Parameter Extraction Method for Dielectric Barrier Discharge Ozone Chamber using Differential Evolution

A Model Parameter Extraction Method for Dielectric Barrier Discharge Ozone Chamber using Differential Evolution

Prediction of Fouling in Condenser Based on Fuzzy Stage Identification and Chebyshev Neural Network

BPNN based MECG elimination from the abdominal signal to extract fetal signal for continuous fetal monitoring

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