Design of a wavelet interpolation filter for enhancement of the ST-segment

Abstract

A wavelet interpolation filter (WIF) is designed for the removal of motion artifacts in the ST-segment of stress ECGs. The WIF consists of two parts. One part is a wavelet transform that decomposes the stress ECG signal into several frequency bands using a Haar wavelet. The other part is an interpolation method, such as the spline technique, that is used to enhance the reconstruction performance of the signal decomposed by the wavelet transform. To evaluate the performance of the WIF, three indices are used: signal-to-noise ratio (SNR), reconstruction square error (RSE) and standard deviation (SD). The MIT/BIH arrhythmia database, the European ST-T database and the triangular wave are used for evaluation. A noisy ECG signal, corrupted by motion artifacts, is simulated by the addition of two types of random noise to the original ECG signal. For comparison, three indices for the other methods are also computed: mean, median and hard thresholding. The performance of the WIF shows that RSE, SNR and SD are 392.7, 18.3dB and 2.6, respectively, in the case of a noisy signal with an SNR of 7.1 dB. This result is much better than those for the other methods.

Wavelet transforms and the ECG: a review

“…Other work has been undertaken by Park et al (1998) using a wavelet adaptive filter to minimize the distortion of the ST-segment due to baseline wanderings. In a subsequent paper by Park et al (2001), a wavelet interpolation filter (WAF) is described for the removal of motion artefacts in the ST-segment of stress ECGs. A noise reduction method for ECG signals using the dyadic wavelet transform has been proposed by Inoue and Miyazaki (1998) and Tikkanen (1999) has evaluated the performance of different wavelet-based and wavelet packet-based thresholding methods for removing noise from the ECG.…”

Wavelet transform for analysis of heart rate variability preceding ventricular arrhythmias in patients with ischemic heart disease

“…The usefulness of wavelet transform has been increasingly recognized over the last 10 years, and is being currently employed in a variety of fields such as communication technology, image processing and geophysics. Recently, this method has been applied to cardiology for removal of motion artefacts from stress ECGs, [12] analysis of signal-averaged ECGs, [13 -15] automatic detection of atrial fibrillation on Holter recordings [16] and morphology discrimination of ICD electrograms [17]. The ability of this technique to give simultaneous time and frequency resolution makes it an ideal tool for studying HRV.…”

The Research of Pulse Wave Signal Denosing Based on EMD and ICA

“…IMF7 mode represents the long-wave pulse which is caused by some interference, such as respiration, etc [14]. Usually, the signal-noise ratio and mean square error is the main index to determine the effect of denoising.…”