Using an artificial neural network to detect activations during ventricular fibrillation

Ventricular fibrillation is a cardiac arrhythmia that can result in sudden death. Understanding and treatment of this disorder would be improved if pa tterns of electrical activation could be accurately identified and studied during fibrillation. A feedforward artificial neural network using backprop agation was trained with the Rule-Based Method and the Current Source Densi ty Method to identify cardiac tissue activation during fibrillation. Anothe r feedforward artificial neural network that used backpropagation was train ed with data preprocessed by those methods and the Transmembrane Current Me thod. Staged training. a new method that uses different sets of training ex amples in different stages, was used to improve the ability of the artifici al neural networks to detect activation. Both artificial neural networks we re able to correctly classify more than 92% of new test examples. The perfo rmance of both artificial neural networks improved when staged training was used. Thus, artificial neural networks may be useful for identifying activ ation during ventricular fibrillation. (C) 2000 Academic Press.