An artificial neural network detected atrial fibrillation in standard databases with a sensitivity of 92.86% and a positive predictive accuracy of 92.34%.
Artificial neural networks (ANNs) were used as pattern detectors to detect atrial fibrillation (AF) in the MIT-BIH arrythmia database. Electrocardiogram data were represented using generalized interval transition matrices, as in Markov model AF detectors (G.B. Moody and R.G. Mark, 1983). A training file was developed, using these transition matrices, for a backpropagation ANN. This file consisted of approximately 15 minutes each of AF and non-AF data. The ANN was successfully trained using these data. Three standard databases were used to test network performance. Post-processing of the ANN output yielded an AF sensitivity of 92.86% and an AF positive predictive accuracy of 92.34%.>
Artis et al. (Mon,) conducted a other in Atrial fibrillation. Artificial neural networks (ANNs) was evaluated on Atrial fibrillation detection. An artificial neural network detected atrial fibrillation in standard databases with a sensitivity of 92.86% and a positive predictive accuracy of 92.34%.