Myocardial infarction (MI) can be defined from a number of different perspectives related to clinical, electrocardiographic (ECG), biochemical and pathologic characteristics. The term MI also has social and psychological implications, both as an indicator of a major health problem and as a measure of disease prevalence in population statistics and outcomes of clinical trials. In the distant past, a general consensus existed for the clinical entity designated as MI. In studies of disease prevalence by the World Health Organization (WHO), MI was defined by a combination of two of three characteristics: typical symptoms (i.e., chest discomfort), enzyme rise and a typical ECG pattern involving the development of Q waves. Biomedical sensors dedicated to acquire signals from cardiac instrumentation, even if sophisticated, cannot precisely reveal and help doctors to understand, at a glance, pathologies leading towards MI. This paper traces out an integrated algorithm based on a combination of level set evolution and variation approach according to Mumford-Shah model.
Accuracy Assessment of Sensed Biomedical Images for Myocardial Infarction Prediction
LAY EKUAKILLE, Aime;SGURA, Ivonne;
2008-01-01
Abstract
Myocardial infarction (MI) can be defined from a number of different perspectives related to clinical, electrocardiographic (ECG), biochemical and pathologic characteristics. The term MI also has social and psychological implications, both as an indicator of a major health problem and as a measure of disease prevalence in population statistics and outcomes of clinical trials. In the distant past, a general consensus existed for the clinical entity designated as MI. In studies of disease prevalence by the World Health Organization (WHO), MI was defined by a combination of two of three characteristics: typical symptoms (i.e., chest discomfort), enzyme rise and a typical ECG pattern involving the development of Q waves. Biomedical sensors dedicated to acquire signals from cardiac instrumentation, even if sophisticated, cannot precisely reveal and help doctors to understand, at a glance, pathologies leading towards MI. This paper traces out an integrated algorithm based on a combination of level set evolution and variation approach according to Mumford-Shah model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
