Key result
An artificial neural network detected atrial fibrillation in standard databases with a sensitivity of 92.86% and a positive predictive accuracy of 92.34%.
Why the study?
Can artificial neural networks accurately detect atrial fibrillation in electrocardiogram data?
Can artificial neural networks accurately detect atrial fibrillation in electrocardiogram data?
Artificial neural networks demonstrate high sensitivity and positive predictive accuracy for detecting atrial fibrillation in ECG data.
Answers are grounded in this paper and cite their sources.
Supports neural network development for AF detection; leaves open prospective clinical validation before use.
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%.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Artis et al. (2002) studied 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%.
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