Key result
Adaptive hybrid filtering with empirical model decomposition denoises ECG signals across standard performance metrics.
Why the study?
ECG signals are often obscured by 50 or 60 Hz mains frequency noise and background noise.
Does an adaptive hybrid filtering method improve signal quality in ECG recordings?
Does an adaptive hybrid filtering method improve signal quality in ECG recordings?
An adaptive hybrid filtering method combined with empirical model decomposition provides a computational approach to denoise ECG signals and remove background noise.
Majority of the time, the signal from the electrocardiogram (ECG) is obscured by the frequency of the mains, which is either 50 or 60 hertz. A notch filter that is able to selectively filter individual frequencies can be used to cut down on the mains frequency portion of an ECG. The adaptive hybrid filter cleans up the ECG signals by removing background noise. The global MIT-BIH database provides the ECG data that is used in the simulation research. Our mathematical analysis makes use of a number of different real ECG recordings in addition to synthetic ECG that has been contaminated by a number of different kinds of noise. The performance of our suggested system is measured by the signal-to-noise and distortion ratio (SINAD), improvement in signal-to-noise ratio (SNR), approximation entropy, and fuzzy entropy. These metrics are used to assess the system's effectiveness.
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Kirubha et al. (2026) studied ECG signal noise. Adaptive hybrid filtering method and empirical model decomposition method was evaluated on Signal-to-noise and distortion ratio (SINAD), improvement in signal-to-noise ratio (SNR), approximation entropy, and fuzzy entropy. An adaptive hybrid filtering method combined with empirical model decomposition was developed to denoise ECG signals, with performance evaluated using SINAD, SNR, and entropy metrics.
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