9783330317840 - Electrocardiography Signal Analysis Using Neural Networks on FPGA: System Design and Implementation di Egila, Mohamed G.; El-Moursy, Magdy; Zaki, Amal (9 risultati)

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Paperback. Condizione: Brand New. 124 pages. 8.66x5.91x0.28 inches. In Stock.

Taschenbuch. Condizione: Neu. Electrocardiography Signal Analysis Using Neural Networks on FPGA | System Design and Implementation | Mohamed G. Egila (u. a.) | Taschenbuch | 124 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330317840 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Electrocardiography (ECG) signal analysis is considered one of the core components of any integrated medical care systems. ECG diagnosis is one of the most valuable diagnostic tools. This book presents a proposed design for an integrated ECG diagnosing system. This system uses digital system processing techniques to analyze ECG signals. This methodology employs Highpass Least-Square Linear Phase Finite Impulse Response (FIR) filtering technique to remove the baseline wander noise embedded in the input ECG signal to the system or reduce the noise as much as possible. Discrete Wavelet Transform (DWT) was utilized as a feature extraction methodology to extract the reduced feature set from the input ECG signal. The design uses back propagation neural network as a classifier to determine whether the input ECG signal represents normal or abnormal ECG signal. The whole system is implemented on Field Programming Gate Array (FPGA) board. Necessary simulations for the implemented system have been done, indicating that the implemented system has a good accuracy compared to other designs, achieving total accuracy of 97.8%, and achieving reduction in resources on FPGA implementation. 124 pp. Englisch.

Condizione: New. Print on Demand.

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Egila Mohamed G.Mohamed Egila received his Bachelor degree, and Master degree in Electronics and Communications from Cairo University,Egypt, in 2003 and 2008 respectively, and PhD degree in Electronics and Communications from Ain Sha.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Electrocardiography (ECG) signal analysis is considered one of the core components of any integrated medical care systems. ECG diagnosis is one of the most valuable diagnostic tools. This book presents a proposed design for an integrated ECG diagnosing system. This system uses digital system processing techniques to analyze ECG signals. This methodology employs Highpass Least-Square Linear Phase Finite Impulse Response (FIR) filtering technique to remove the baseline wander noise embedded in the input ECG signal to the system or reduce the noise as much as possible. Discrete Wavelet Transform (DWT) was utilized as a feature extraction methodology to extract the reduced feature set from the input ECG signal. The design uses back propagation neural network as a classifier to determine whether the input ECG signal represents normal or abnormal ECG signal. The whole system is implemented on Field Programming Gate Array (FPGA) board. Necessary simulations for the implemented system have been done, indicating that the implemented system has a good accuracy compared to other designs, achieving total accuracy of 97.8%, and achieving reduction in resources on FPGA implementation.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Electrocardiography (ECG) signal analysis is considered one of the core components of any integrated medical care systems. ECG diagnosis is one of the most valuable diagnostic tools. This book presents a proposed design for an integrated ECG diagnosing system. This system uses digital system processing techniques to analyze ECG signals. This methodology employs Highpass Least-Square Linear Phase Finite Impulse Response (FIR) filtering technique to remove the baseline wander noise embedded in the input ECG signal to the system or reduce the noise as much as possible. Discrete Wavelet Transform (DWT) was utilized as a feature extraction methodology to extract the reduced feature set from the input ECG signal. The design uses back propagation neural network as a classifier to determine whether the input ECG signal represents normal or abnormal ECG signal. The whole system is implemented on Field Programming Gate Array (FPGA) board. Necessary simulations for the implemented system have been done, indicating that the implemented system has a good accuracy compared to other designs, achieving total accuracy of 97.8%, and achieving reduction in resources on FPGA implementation.