Key result
The Hilbert-Huang Transform and wavelet transform were successfully implemented to extract features from simulated normal and abnormal ECG signals, demonstrating efficiency in analyzing nonlinear data.
The Hilbert-Huang Transform provides an adaptive mathematical model for extracting features from non-linear and non-stationary ECG signals without requiring an a priori functional basis.
This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform.ECG signal for an individual human being is different due to unique heart structure.The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder.Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on.Therefore, we need a strong mathematical model to extract such useful parameter.Here an adaptive mathematical analysis model is Hilbert-Huang transform (HHT).This new approach, the Hilbert-Huang transform, is implemented to analyze the non-linear and nonstationary data.It is unique and different from the existing methods of data analysis and does not require an a priori functional basis.The effectiveness of the proposed scheme is verified through the simulation.
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Soorma et al. (2014) studied Arrhythmia (simulated ECG signals). Hilbert-Huang Transform (HHT) and Wavelet Transform was evaluated on Feature extraction of ECG signal. The Hilbert-Huang Transform and wavelet transform were successfully implemented to extract features from simulated normal and abnormal ECG signals, demonstrating efficiency in analyzing nonlinear data.
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