Key result
Hybrid filtering denoising improves ECG signal quality, achieving a ~39-dB signal-to-noise ratio.
Why the study?
ECG signals are highly sensitive to various noise types that obscure critical diagnostic features, necessitating effective denoising methods that preserve signal integrity for accurate cardiovascular diagnosis.
Answers are grounded in this paper and cite their sources.
A novel hybrid elliptic-notch-Savitzky-Golay filter framework effectively removes baseline wander, powerline interference, and electromyogram noise from ECG signals while preserving critical morphological features for accurate diagnosis.
One of the most important testing tools for the assessment and diagnosis of cardiovascular diseases is the electrocardiogram (ECG) signal. However, the ECG signal is very sensitive to different types of noise, such as baseline drift, powerline interference and electromyogram (EMG) noise. Therefore, these noises need to be removed from the ECG signals due to their significance in detection and diagnosis of cardiovascular disease. In this paper, a novel method for simultaneously removing these different types of noise from the ECG signals by using a hybrid filtering framework is proposed. This hybrid approach assures efficient noise reduction, and the integrity of the ECG signal is preserved for accurate diagnosis. The proposed method consists of two stages. In the initial stage, the three filters that make up the hybrid filtering are applied: an elliptic high-pass filter to remove the baseline wander, a notch filter to reduce the 50/60 Hz power line interference and a Savitzky–Golay filter to reduce the high-frequency EMG noise. Second, the preprocessed ECG signal is divided into segments of different durations by segmentation to facilitate practical analysis and feature extraction in later stages. The effectiveness of the proposed method is shown by the tests and proven results using MATLAB R2023a. The method is evaluated using a large collection of real ECG signals from the MIT-BIH arrhythmia database. The proposed method outperforms other methods in signal-to-noise ratio (39.10 dB) and mean square error (0.06014*10⁻³); it can remove different types of noise without distorting the ECG signal characteristics. The study’s contribution to reliable noise removal of ECG signals for clinical applications is significant.
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Abdullah et al. (2026) studied Arrhythmia (n=45). Hybrid-Elliptic-Notch-Savitzky-Golay Filter vs. Other traditional filtering methods (e.g., Gaussian, Mittag-Leffler, CEEMD, EMD) was evaluated on Signal-to-noise ratio (SNR) and Mean Square Error (MSE). The hybrid filtering framework efficiently removed noise from ECG signals, achieving a signal-to-noise ratio of 39.10 dB and a mean square error of 0.06014x10⁻³ while preserving signal integrity.
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