Comparing different wavelet transforms on removing electrocardiogram baseline wanders and special trends.

ABSTRACT:

Background

Electrocardiogram (ECG) signal, an important indicator for heart problems, is commonly corrupted by a low-frequency baseline wander (BW) artifact, which may cause interpretation difficulty or inaccurate analysis. Unlike current state-of-the-art approach using band-pass filters, wavelet transforms can accurately capture both time and frequency information of a signal. However, extant literature is limited in applying wavelet transforms (WTs) for baseline wander removal. In this study, we aimed to evaluate 5 wavelet families with a total of 14 wavelets for removing ECG baseline wanders from a semi-synthetic dataset.

Methods

We created a semi-synthetic ECG dataset based on a public QT Database on Physionet repository with ECG data from 105 patients. The semi-syntheti

SUBMITTER: Chen CC 

PROVIDER: S-EPMC7772919 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature