In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ...In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.展开更多
INTRODUCTIONVasospastic angina （VSA） cardiac disorder that leads is an important functional to transient myocardial ischemia and is caused by sudden, intense and reversible coronary artery spasm resulting in subtota...INTRODUCTIONVasospastic angina （VSA） cardiac disorder that leads is an important functional to transient myocardial ischemia and is caused by sudden, intense and reversible coronary artery spasm resulting in subtotal or total occlusion.展开更多
Myocardial infarction(MI)is a severe heart disease requiring immediate and accurate detection for effective treatment.Deep learning(DL)algorithms have recently shown promise in enhancing MI diagnostic accuracy from el...Myocardial infarction(MI)is a severe heart disease requiring immediate and accurate detection for effective treatment.Deep learning(DL)algorithms have recently shown promise in enhancing MI diagnostic accuracy from electrocardiography(ECG)and echocardiogram(ECHO).This review presents a comprehensive literature overview focusing on recent innovative research on DL algorithms in ECG and ECHO analysis for MI identification.We examined relevant studies employing DL models,analyzing datasets,model architectures,preprocessing approaches,and performance measures.The findings reveal that DL-based algorithms substantially improve MI detection in terms of accuracy,sensitivity,specificity,and overall diagnostic performance.This is crucial for quicker,more reliable diagnoses and reducing the risk of complications.DL-based ECG and ECHO analyses emerge as pivotal tools for early and efficient MI identification.This review contributes to understanding the latest DL advancements in ECG and ECHO analysis for MI diagnosis,offering important directions for future research.展开更多
文摘In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.
文摘INTRODUCTIONVasospastic angina （VSA） cardiac disorder that leads is an important functional to transient myocardial ischemia and is caused by sudden, intense and reversible coronary artery spasm resulting in subtotal or total occlusion.
文摘Myocardial infarction(MI)is a severe heart disease requiring immediate and accurate detection for effective treatment.Deep learning(DL)algorithms have recently shown promise in enhancing MI diagnostic accuracy from electrocardiography(ECG)and echocardiogram(ECHO).This review presents a comprehensive literature overview focusing on recent innovative research on DL algorithms in ECG and ECHO analysis for MI identification.We examined relevant studies employing DL models,analyzing datasets,model architectures,preprocessing approaches,and performance measures.The findings reveal that DL-based algorithms substantially improve MI detection in terms of accuracy,sensitivity,specificity,and overall diagnostic performance.This is crucial for quicker,more reliable diagnoses and reducing the risk of complications.DL-based ECG and ECHO analyses emerge as pivotal tools for early and efficient MI identification.This review contributes to understanding the latest DL advancements in ECG and ECHO analysis for MI diagnosis,offering important directions for future research.
