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.
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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.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Cardiovascular heart disease (CHD) is a chief publichealth priority worldwide. The 12-Lead Electrocardiogram (ECG) is astandard procedure in diagnosing CHDs such as MyocardialInfarction (MI). Nevertheless, due to sparse spatial sampling, it islimited in identifying cardiac abnormalities. Alternatively, inBody Surface Cardiac Mapping (BSCM) a higher number of ECGs arerecorded. Hence, BSCM provides a more comprehensive picture of electrocardiographic information than is possiblewith the 12-lead ECG. This work has two main objectives. Firstly, todevelop a classification framework for an accurate and earlydiagnosis of acute MI. This decision support system encompassescomputational neural models with the input space based on BSCM.Secondly, since MI is localised on the torso surface, and due to thehigh number of electrocardiographic leads involved in BSCM, it isdesirable to find an optimal reduced lead set for acute MI detection.By building an additional layer of knowledge between thecardiologist and clinical practice, this work not only enhances final MIclassification performance but, allow the discovery of newelectrocardiographic MI markers. Seller Inventory # 9783639165791
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