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.
Computational Neural Models for Body Surface ECG Data Analysis
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Description
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.
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