Key result
Hilbert-Huang Transform outperforms standard techniques for detecting cardiac abnormalities on ECG.
Why the study?
The feasibility of using Hilbert-Huang Transform to enhance diagnostic procedures in cardiac abnormalities was investigated.
Does Hilbert-Huang Transform improve the analysis of cardiac abnormalities in ECG signals compared to other techniques?
Does Hilbert-Huang Transform improve the analysis of cardiac abnormalities in ECG signals compared to other techniques?
The Hilbert-Huang Transform is a feasible and superior adaptive signal analysis method for detecting cardiac abnormalities in ECG signals.
This paper investigates the feasibility of using Hilbert-Huang Transform (HHT) so as to enhance the diagnostic procedures in cardiac abnormalities. HHT which embraces empirical mode decomposition and Hilbert spectral analysis is a newly developed adaptive signal analysis method and has been extensively used in the analysis of non-stationary signals. Empirical Mode Decomposition (EMD) algorithm, the key feature of HHT, decomposes a signal into so-called Intrinsic Mode Functions (IMFs). Furthermore, the Hilbert spectral analysis of IMFs provide frequency information evolving with time and quantises the amount of variation due to oscillation at different time scales and time locations. Normal and abnormal ECG signals from MIT-BIH data base are used to verify the effectiveness of the proposed method and the results demonstrate the superiority of proposed technique compared to other wellknown techniques for the analysis of cardiac abnormalities.
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Sikkandar et al. (2013) studied Cardiac abnormalities. Hilbert-Huang Transform (HHT) vs. Other well-known techniques was evaluated on Effectiveness in the analysis of cardiac abnormalities. The Hilbert-Huang Transform demonstrated superiority compared to other well-known techniques for the analysis of cardiac abnormalities using ECG signals.
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