R-HessELM with inclined entropy features predicts CHF from ECG signals with 98.49% accuracy.
Measurement46(9), 3238– 3246 (2013)
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A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
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Regularized HessELM and Inclined Entropy Measurement for Congestive Heart Failure Prediction
R-HessELM with inclined entropy features predicts CHF from ECG signals with 98.49% accuracy.
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Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.