Use of an Artificial Neural Network for the Diagnosis of Myocardial Infarction

1991 ◽  
Vol 115 (11) ◽  
pp. 843 ◽  
Author(s):  
William G. Baxt
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Artificial Neural Network ◽  
Artificial Neural

Use of an Artificial Neural Network for Data Analysis in Clinical Decision-Making: The Diagnosis of Acute Coronary Occlusion

1990 ◽  
Vol 2 (4) ◽  
pp. 480-489 ◽  
Author(s):  
William G. Baxt
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Artificial Neural Network ◽  
Coronary Occlusion ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Acute Coronary Occlusion ◽  
Artificial Neural ◽  
Better Than

A nonlinear artificial neural network trained by backpropagation was applied to the diagnosis of acute myocardial infarction (coronary occlusion) in patients presenting to the emergency department with acute anterior chest pain. Three-hundred and fifty-six patients were retrospectively studied, of which 236 did not have acute myocardial infarction and 120 did have infarction. The network was trained on a randomly chosen set of half of the patients who had not sustained acute myocardial infarction and half of the patients who had sustained infarction. It was then tested on a set consisting of the remaining patients to which it had not been exposed. The network correctly identified 92% of the patients with acute myocardial infarction and 96% of the patients without infarction. When all patients with the electrocardiographic evidence of infarction were removed from the cohort, the network correctly identified 80% of the patients with infarction. This is substantially better than the performance reported for either physicians or any other analytical approach.

scholarly journals Profiling of Myocardial Infarction History from Electrocardiogram using Artificial Neural Network

2018 ◽  
Vol 7 (4.11) ◽  
pp. 236
Author(s):  
Amerah Hanis Hussin ◽  
Ahmad Syukri Abdul Aziz ◽  
Megat Syahirul Amin Megat Ali
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Normal Sinus Rhythm ◽  
Oxygen Deficiency ◽  
Power Ratio ◽  
K Nearest Neighbor ◽  
Discriminative Ability ◽  
Artificial Neural

Myocardial infarction is an irreversible damage of heart muscle caused by prolonged oxygen deficiency. As a result, the presence of damaged tissue will alter the normal sinus rhythm. Hence, the paper proposes to profile history of myocardial infarction from electrocardiogram using artificial neural network. Data for anterior and inferior myocardial infarction, as well as healthy control is acquired from PTB Diagnostic ECG Database. Subsequently, QRS power ratio features for different frequency zones are extracted from the pre-processed electrocardiogram. Discriminative ability of the features is assessed using k-nearest neighbor. The best combination of features with 99.7% testing accuracy is the power ratio composite that combines both low-frequency and mid-frequency information. An intelligent profiling model is successfully developed using the composite features and an optimized artificial neural network. The model was able to identify between different electrocardiogram groups with overall accuracy of 98.4% and mean squared error of less than 0.1. Conclusively, the proposed signal processing approach has provided an improved alternative to the established methods from literature.  

scholarly journals Profiling of Myocardial Infarction History from Electrocardiogram using Artificial Neural Network

2018 ◽  
Vol 7 (4.11) ◽  
pp. 276
Author(s):  
Amerah Hanis Hussin ◽  
Ahmad Syukri Abdul Aziz ◽  
Megat Syahirul Amin Megat Ali
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Normal Sinus Rhythm ◽  
Oxygen Deficiency ◽  
Power Ratio ◽  
K Nearest Neighbor ◽  
Discriminative Ability ◽  
Artificial Neural

Myocardial infarction is an irreversible damage of heart muscle caused by prolonged oxygen deficiency. As a result, the presence of damaged tissue will alter the normal sinus rhythm. Hence, the paper proposes to profile history of myocardial infarction from electrocardiogram using artificial neural network. Data for anterior and inferior myocardial infarction, as well as healthy control is acquired from PTB Diagnostic ECG Database. Subsequently, QRS power ratio features for different frequency zones are extracted from the pre-processed electrocardiogram. Discriminative ability of the features is assessed using k-nearest neighbor. The best combination of features with 99.7% testing accuracy is the power ratio composite that combines both low-frequency and mid-frequency information. An intelligent profiling model is successfully developed using the composite features and an optimized artificial neural network. The model was able to identify between different electrocardiogram groups with overall accuracy of 98.4% and mean squared error of less than 0.1. Conclusively, the proposed signal processing approach has provided an improved alternative to the established methods from literature.  

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