Özet:
ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often su®er from the e®ects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to signi¯cantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the e®ectiveness of commonly used ¯ltering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the in°uence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of di®erent techniques. Our research uses both ¯ltering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most e®ectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scienti¯c community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based ¯ltering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the e®ectiveness of the Chebychev-II ¯lter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of e®ective techniques for removing BW noise within ECG signals.