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Computational Neural Models for Body Surface ECG Data Analysis: Body Surface Cardiac Mapping - Couverture souple

Lopez, Jesus

 
9783639165791: Computational Neural Models for Body Surface ECG Data Analysis: Body Surface Cardiac Mapping
Couverture souple
ISBN 10 : 3639165799 ISBN 13 : 9783639165791
Editeur : VDM Verlag, 2009

Synopsis

Cardiovascular heart disease (CHD) is a chief public health priority worldwide. The 12-Lead Electrocardiogram (ECG) is a standard procedure in diagnosing CHDs such as Myocardial Infarction (MI). Nevertheless, due to sparse spatial sampling, it is limited in identifying cardiac abnormalities. Alternatively, in Body Surface Cardiac Mapping (BSCM) a higher number of ECGs are recorded. Hence, BSCM provides a more comprehensive picture of electrocardiographic information than is possible with the 12-lead ECG. This work has two main objectives. Firstly, to develop a classification framework for an accurate and early diagnosis of acute MI. This decision support system encompasses computational neural models with the input space based on BSCM. Secondly, since MI is localised on the torso surface, and due to the high number of electrocardiographic leads involved in BSCM, it is desirable to find an optimal reduced lead set for acute MI detection. By building an additional layer of knowledge between the cardiologist and clinical practice, this work not only enhances final MI classification performance but, allow the discovery of new electrocardiographic MI markers.

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

Présentation de l'éditeur

Cardiovascular heart disease (CHD) is a chief public health priority worldwide. The 12-Lead Electrocardiogram (ECG) is a standard procedure in diagnosing CHDs such as Myocardial Infarction (MI). Nevertheless, due to sparse spatial sampling, it is limited in identifying cardiac abnormalities. Alternatively, in Body Surface Cardiac Mapping (BSCM) a higher number of ECGs are recorded. Hence, BSCM provides a more comprehensive picture of electrocardiographic information than is possible with the 12-lead ECG. This work has two main objectives. Firstly, to develop a classification framework for an accurate and early diagnosis of acute MI. This decision support system encompasses computational neural models with the input space based on BSCM. Secondly, since MI is localised on the torso surface, and due to the high number of electrocardiographic leads involved in BSCM, it is desirable to find an optimal reduced lead set for acute MI detection. By building an additional layer of knowledge between the cardiologist and clinical practice, this work not only enhances final MI classification performance but, allow the discovery of new electrocardiographic MI markers.

Biographie de l'auteur

Dr Jesus A Lopez is currently a medical student at Griffith University (AU) after being an A/Professor of Bioinformatics at USQ (AU). He received his BSc (Hons) in Electronics from USB (Venezuela) in 1997 and his Ph.D. in artificial intelligence from Ulster University (UK) in 2003. He has published extensively on bio and medical informatics.

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

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
3639165799
ISBN 13 
9783639165791
Reliure
Broché
Nombre de pages
204
Coordonnées du fabricant
non disponible
Personne responsable
non disponible