What is the clinical evidence from this study?

Study design: Systematic Review. Population: Cardiovascular issues (ECG signals) (n=29). Intervention: Filtering methods for ECG signals. Primary outcome: Noise reduction and signal preservation.

March 20, 2026WSEAS Transactions on Signal Processing archiveOpen Access

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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Population

29 studies on ECG signal filtering techniques published between 2020 and 2024

Design

Systematic_review

Key result

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.

Authors

JSJeshica Moyon S OPOrlando Pellicioni-Monrroy GVGuillermina Vivar-Estudillo

Discussion

Member takes

Overview

Supports clinical use of hybrid AI ECG filters; leaves open need for prospective validation in recent datasets.

Study Design

Type

Systematic Review (n=29)

Structured PICO

Population

29 high-quality studies published between 2020 and 2024 evaluating filtering techniques for ECG signals.

Intervention

ECG signal filtering techniques (including hybrid, time-frequency analysis, and AI-based approaches)

Outcome

Filtering method performance evaluated by signal-to-noise ratio (SNR), mean square error (MSE), and morphological integritysurrogate

AI-based and hybrid filtering techniques are increasingly prominent and effective for ECG noise reduction, though more validation with recent clinical data is needed.

Limitations

Limited use of recent hospital-based clinical data
limited use of recent hospital-based clinical data

Cite This Study

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.

synapsesocial.com/papers/6aae231e3dbe6ffe06992bf1 https://doi.org/10.37394/232014.2026.22.7

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March 20, 2026WSEAS Transactions on Signal Processing archiveOpen Access

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.

View Full Paper

Ask AI

Bookmark

Population

29 studies on ECG signal filtering techniques published between 2020 and 2024

Design

Systematic_review

Key result

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.

Authors

JSJeshica Moyon S OPOrlando Pellicioni-Monrroy GVGuillermina Vivar-Estudillo

Discussion

Member takes

Overview

Supports clinical use of hybrid AI ECG filters; leaves open need for prospective validation in recent datasets.

Study Design

Type

Systematic Review (n=29)

Structured PICO

Population

29 high-quality studies published between 2020 and 2024 evaluating filtering techniques for ECG signals.

Intervention

ECG signal filtering techniques (including hybrid, time-frequency analysis, and AI-based approaches)

Outcome

Filtering method performance evaluated by signal-to-noise ratio (SNR), mean square error (MSE), and morphological integritysurrogate

AI-based and hybrid filtering techniques are increasingly prominent and effective for ECG noise reduction, though more validation with recent clinical data is needed.

Limitations

Limited use of recent hospital-based clinical data
limited use of recent hospital-based clinical data

Cite This Study

synapsesocial.com/papers/6aae231e3dbe6ffe06992bf1 https://doi.org/10.37394/232014.2026.22.7

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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