DETERMINISTIC LOGIC VERSUS SOFTWARE-BASED ARTIFICIAL NEURAL NETWORKS IN THE DIAGNOSIS OF ATRIAL-FIBRILLATION

An investigation into the use of software-based neural networks for th e detection of atrial fibrillation was made. At a specific point in th e Glasgow 12-lead electrocardiographic interpretation program, a decis ion has to be made as to whether atrial fibrillation or sinus rhythm w ith supraventricular or ventricular extrasystoles is present. The same input parameters used for the deterministic logic at that point were also utilized to train a variety of neural networks. Results from a se parate test set showed that the sensitivity of detecting atrial fibril lation could be improved using the best of the neural networks. On the other hand, it was felt that the original deterministic logic could b e improved by considering adjustments in order that the presence of ce rtain combinations of findings not previously regarded as representing atrial fibrillation would now do so. When the deterministic logic was upgraded in this way, it was found, again using a separate test set, that the revised logic was improved compared to the original, and also gave a performance similar to that of the neural network. It is concl uded that the use of a neural network at a specific diagnostic decisio n point in a rhythm analysis program can be as effective as determinis tic logic, which may take several years to perfect.