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 the detection of atrial fibrillation was made. At a specific point in the Glasgow 12-lead electrocardiographic interpretation program, a decision has to be made as to whether atrial fibrillation or sinus rhythm with 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 separate test set showed that the sensitivity of detecting atrial fibrillation could be improved using the best of the neural networks. On the other hand, it was felt that the original deterministic logic could be improved by considering adjustments in order that the presence of certain 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 concluded that the use of a neural network at a specific diagnostic decision point in a rhythm analysis program can be as effective as deterministic logic, which may take several years to perfect.