Key result
Hybrid and AI-based techniques emerge as effective methods for ECG signal filtering and noise reduction.
Why the study?
Technological advancements in ECG signal filtering have progressed, but limitations remain such as limited use of recent hospital-based clinical data.
Systematic Review (n=29)
Answers are grounded in this paper and cite their sources.
AI-based and hybrid filtering techniques are increasingly prominent and effective for ECG noise reduction, though more validation with recent clinical data is needed.
An electrocardiogram (ECG) is a non-invasive method that records the heart's electrical activity and is essential for diagnosing cardiovascular issues. This systematic review examines current filtering techniques for ECG signals. Researchers conducted a comprehensive search across three databases—ScienceDirect, PubMed, and IEEE Xplore—covering the period from 2020 to 2024 and selected 29 studies that met specific inclusion criteria. These studies demonstrated high methodological quality, with impact factors exceeding three and publications in Q1 or Q2 journals, thereby ensuring scientific rigor. The review focused on filtering methods, evaluation metrics, and key findings. Results reveal a trend toward hybrid and time-frequency analysis techniques, favored for their effectiveness in noise reduction and signal preservation. Common performance metrics included signal-to-noise ratio (SNR), mean square error (MSE), and morphological integrity. Notably, AI-based approaches have become increasingly prominent, offering robust solutions for noisy signals with minimal preprocessing. At the same time, these innovations demonstrate significant progress; however, limitations remain, such as the limited use of recent hospital-based clinical data. Addressing this gap offers opportunities for future research to improve the clinical relevance and efficiency of ECG filtering. Overall, the review highlights technological advancements and supports the development of better diagnostic tools in clinical cardiology.
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S et al. (2026) conducted a systematic review in Cardiovascular issues (ECG signals) (n=29). Filtering methods for ECG signals was evaluated on Noise reduction and signal preservation. A systematic review of 29 studies highlights a trend toward hybrid, time-frequency, and AI-based techniques for effective ECG signal filtering and noise reduction.
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