Use of an artificial neural network to differentiate between ECGs with IRBBB patterns of atrial septal defect and healthy subjects

Abstract

Atrial septal defect (ASD) is one of the most commonly recognized congenital cardiac anomalies in adults, but its diagnosis is easily missed because about half of the patients are asymptomatic early in life. Although an electrocardiogram (ECG) diagnosis with an incomplete right bundle branch block (IRBBB) pattern is of major importance for this disease, an RSR' complex similar to an IRBBB pattern is also found in some healthy individuals. A feed-forward artificial neural network was constructed to distinguish between ASD and healthy subjects using 12-lead ECGs with IRBBB. A total of 106 clinically validated subjects, including 58 with ASD and 48 healthy subjects were used in this study. QRS and T wave measurements from I, II, and all precordial leads were used as the input parameters to a back propagation network. The leave-one-out method revealed that in the test data (106 cases), the overall accuracy, sensitivity and specificity of the artificial neural network were 91.5, 91.4, and 91.7%, respectively. This study demonstrates that the neural network technique may offer higher accuracy than computerized ECG diagnosis of IRBBB from the viewpoint of ASD.

• Artificial Neural Network
• Computerized Electrocardiography
• Atrial Septal
• Defect
• Leave-ONE-OUT Method