Prediction of highly non-stationary time series using higher-order neural units
R Rodríguez Jorge, E Martínez García… - Advances on P2P …, 2018 - Springer
Adaptive predictive models can use conventional and nonconventional neural networks for
highly non-stationary time series prediction. However, conventional neural networks present …
highly non-stationary time series prediction. However, conventional neural networks present …
Adaptive Threshold, Wavelet and Hilbert Transform for QRS Detection in Electrocardiogram Signals
R Rodriguez Jorge, EM García, RT Córdoba… - Advances on P2P …, 2018 - Springer
This paper combines Hilbert and Wavelet transforms and an adaptive threshold technique to
detect the QRS complex of electrocardiogram signals. The method is performed in a window …
detect the QRS complex of electrocardiogram signals. The method is performed in a window …
Cardiac Arrhythmias Detection Based on Sequential and Linguistic Analysis
A Menlitdinov, A Korobeynikov… - 2021 International …, 2021 - ieeexplore.ieee.org
The paper considers cardiac arrhythmias analysis methods starting with electrocardiogram
(ECG) signal splitting into cycles, and finishing with the formation of the arrhythmias types …
(ECG) signal splitting into cycles, and finishing with the formation of the arrhythmias types …
[PDF][PDF] Prediction of Highly Non-stationary Time Series Using Higher-Order Neural Units.
Adaptive predictive models can use conventional and nonconventional neural networks for
highly non-stationary time series prediction. However, conventional neural networks present …
highly non-stationary time series prediction. However, conventional neural networks present …