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• W1969666747 abstract "This paper presents a predictive model for the prediction and modeling of nonlinear, chaotic, and non-stationary electrocardiogram signals. The model is based on the combined usage of Hilbert-Huang transform, False nearest neighbors, and a novel neural network architecture. This model is intended to increase the prediction accuracy by applying the Empirical Mode Decomposition over a signal, and to reconstruct the signal by adding each calculated Intrinsic Mode Function and its residue. The Intrinsic Mode Function that obtains the highest frequency oscillation is not considered during the reconstruction. The optimal embedding dimension space of the reconstructed signal is obtained by False Nearest Neighbors algorithm. Finally, for the prediction horizon, a neural network retraining technique is applied to the reconstructed signal. The method has been validated using the record 103 from MIT-BIH arrhythmia database. Results are very promising since the measured root mean squared errors are 0.031, 0.05, and 0.085 of the ECG amplitude, for the prediction horizons of 0.0028, 0.0056, 0.0083 seconds, respectively." @default.
• W1969666747 created "2016-06-24" @default.
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• W1969666747 date "2014-08-01" @default.
• W1969666747 modified "2023-10-01" @default.
• W1969666747 title "Hilbert-Huang transform and neural networks for electrocardiogram modeling and prediction" @default.
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• W1969666747 doi "https://doi.org/10.1109/icnc.2014.6975896" @default.
• W1969666747 hasPublicationYear "2014" @default.
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