Key result
A data-adaptive technique using empirical mode decomposition successfully separated high-frequency and low-frequency components of ECG signals to assist in cardiovascular disease diagnosis.
Empirical mode decomposition provides a data-adaptive method to separate high and low-frequency components of ECG signals, potentially improving cardiovascular disease diagnosis.
This paper presents a data-adaptive technique of cardiovascular disease diagnosis by analyzing electrocardiogram (ECG) signals. The separation of high-frequency (HF) and low-frequency (LF) components are performed by employing empirical mode decomposition (EMD) designed for analyzing nonstationary and non-linear signals. The EMD is used to decompose ECG signal into a finite set of band-limited AM–FM signals termed as intrinsic mode functions (IMFs). Then the LF and HF components of ECG signals are obtained by partial reconstruction based on the energy distribution of IMFs. The extracted HF and LF signals of the ECG are analyzed separately to make the remarks for better diagnosis of the cardiovascular diseases. The experimental results are also illustrated using some ECG signals of normal and abnormal subjects.
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Islam et al. (2012) studied Cardiovascular disease. Empirical mode decomposition (EMD) based data-adaptive time domain filtering was evaluated on Separation of high-frequency and low-frequency components of ECG signals. A data-adaptive technique using empirical mode decomposition successfully separated high-frequency and low-frequency components of ECG signals to assist in cardiovascular disease diagnosis.
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