Design Strategies And Implementation Of Adaptive Filters on FPGA: Case Study : Electrocardiogram (ECG) Signals - Couverture souple

Singh, Prateek

 
9783659915680: Design Strategies And Implementation Of Adaptive Filters on FPGA: Case Study : Electrocardiogram (ECG) Signals
Couverture souple
ISBN 10 : 3659915688 ISBN 13 : 9783659915680
Editeur : LAP LAMBERT Academic Publishing, 2016

Synopsis

ECG is an electrical representation of blood pumping activity of the heart, which can be recorded easily with surface electrodes on the limbs or chest. We can filter high frequency and low frequency noises by implementing fixed filters, but motion artefacts and noises related to movement of muscles can not be filtered by fixed filters as we don’t have any prior information regarding the nature of these noises and electromyogram (EMG) signals are superimposed on ECG signals because of physical proximity of the body movement. A static filter is required to remove all the noise frequencies, which could overly abase the attribute of the ECG since the heartbeat would also likely have frequency components in the rejected range. To outfox this latent loss of information, an adaptive filter is used. Thus adaptive technique allows a filter with a greater acceptance ambit, which means our filter provide output signal quality dead on target for medical diagnoses. The adaptive filter here is trained using LMS algorithm and RLS algorithm.The algorithms are enforced using Simulink as a reference model where the blocks are taken from the Xilinx block set.

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

Présentation de l'éditeur

ECG is an electrical representation of blood pumping activity of the heart, which can be recorded easily with surface electrodes on the limbs or chest. We can filter high frequency and low frequency noises by implementing fixed filters, but motion artefacts and noises related to movement of muscles can not be filtered by fixed filters as we don’t have any prior information regarding the nature of these noises and electromyogram (EMG) signals are superimposed on ECG signals because of physical proximity of the body movement. A static filter is required to remove all the noise frequencies, which could overly abase the attribute of the ECG since the heartbeat would also likely have frequency components in the rejected range. To outfox this latent loss of information, an adaptive filter is used. Thus adaptive technique allows a filter with a greater acceptance ambit, which means our filter provide output signal quality dead on target for medical diagnoses. The adaptive filter here is trained using LMS algorithm and RLS algorithm.The algorithms are enforced using Simulink as a reference model where the blocks are taken from the Xilinx block set.

Biographie de l'auteur

Prateek Singh, Assistant Professor in Electrical Engineering Department. He had completed his Masters of Technology from College of Engineering, Pune in 2015 after his Bachelors of Technology in Electronics and Instrumentation. His interest of research is in Embedded Technologies and Biomedical Signal Processing.

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
2016
Langue
anglais
ISBN 10 
3659915688
ISBN 13 
9783659915680
Reliure
Broché
Nombre de pages
64
Coordonnées du fabricant
non disponible
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