Prototyping of Fetal QRS Complex Detection Algorithm: Hardware Prototyping of an Efficient Fetal QRS Complex Detection Algorithm for Fetal Heart Rate Monitoring - Couverture souple

Reaz, Md. Mamun Bin Ibne; Hasan, Muhammad Asraful

 
9783847379218: Prototyping of Fetal QRS Complex Detection Algorithm: Hardware Prototyping of an Efficient Fetal QRS Complex Detection Algorithm for Fetal Heart Rate Monitoring
Couverture souple
ISBN 10 : 3847379216 ISBN 13 : 9783847379218
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Fetal Electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. A Back-propagation Neural Network and Adaptive Linear Neural Network have been designed to extract the FECG from the abdominal ECG to assess the fetus during the pregnancy and labor. The neural network was trained to recognize the normal waveform and filtered out the unnecessary artifacts including noises in the ECG signal, including power line interference, motion artifacts, baseline drift, ECG amplitude modulation with respiration and other composite noises. The performance of the designed algorithm for FHR extraction is 93.75%. The algorithm has been modeled using VHDL for hardware modeling of FHR monitoring system, which has been synthesized and fitted into Altera’s Stratix II EP2S15F484C3 using the Quartus II version 7.2 Web Edition where the logic and DSP block utilization were 89% and 50% respectively. This research will open up a passage to biomedical researchers and physicians to advocate an excellent understanding of FECG signal and its analysis procedures for FHR monitoring system.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Fetal Electrocardiogram (FECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during labor. A Back-propagation Neural Network and Adaptive Linear Neural Network have been designed to extract the FECG from the abdominal ECG to assess the fetus during the pregnancy and labor. The neural network was trained to recognize the normal waveform and filtered out the unnecessary artifacts including noises in the ECG signal, including power line interference, motion artifacts, baseline drift, ECG amplitude modulation with respiration and other composite noises. The performance of the designed algorithm for FHR extraction is 93.75%. The algorithm has been modeled using VHDL for hardware modeling of FHR monitoring system, which has been synthesized and fitted into Altera’s Stratix II EP2S15F484C3 using the Quartus II version 7.2 Web Edition where the logic and DSP block utilization were 89% and 50% respectively. This research will open up a passage to biomedical researchers and physicians to advocate an excellent understanding of FECG signal and its analysis procedures for FHR monitoring system.

Biographie de l'auteur

Mamun Bin Ibne Reaz was born in Bangladesh, in December 1963. He received his Ph.D. in EE from Ibaraki University, Japan. He is currently an Associate Professor in Universiti Kebangsaan Malaysia, Malaysia. His research area is VLSI Design. He is a regular associate of the Abdus Salam International Center for Theoretical Physics.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3847379216
ISBN 13 
9783847379218
Reliure
Broché
Nombre de pages
108
Coordonnées du fabricant
non disponible
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