Key result
The adaptive unbiased finite impulse response (UFIR) smoother demonstrated higher accuracy in ECG signal denoising, feature extraction, and classification of normal rhythm and atrial fibrillation compared to standard wavelet-based and conventional filters.
Why the study?
High accuracy and efficiency in extracting heartbeat features from ECG records remain critical for clinical decision-making in heart disease.
Does the adaptive UFIR smoother improve denoising and feature extraction accuracy in ECG signals compared to standard filters?
Does the adaptive UFIR smoother improve denoising and feature extraction accuracy in ECG signals compared to standard filters?
The adaptive UFIR smoother provides superior denoising and feature extraction for ECG signals compared to traditional filters, which may improve the accuracy of automated arrhythmia detection systems.
Novel UFIR smoother may aid ECG feature extraction; leaves open clinical validation before practice adoption.
The electrocardiogram (ECG) signals bear fundamental information for making decisions about different kinds of heart diseases. Therefore, many efforts were made during decades to extract features of heartbeats via ECG records with high accuracy and efficiency using different strategies and methods. In this paper, we solve the problem in discrete-time state-space using a novel q-lag unbiased finite impulse response (UFIR) smoother, which we adapt to the ECG signal shape via the time-varying optimal averaging horizon. It is shown that the adaptive UFIR smoother performs better in applications to ECG signals than the standard techniques such as the Savitsky-Golay, wavelet-based, low-pass, band-pass, notch, and median filters. Applications are given for the PhysioBank data benchmark, which contains several records taken from different databases such as the MIT-BIH Arrhythmia (MITDB). A complete statistical analysis is provided via normalized histograms and statistical classifiers. It is shown in a comparison with other methods that the adaptive UFIR smoother has a higher accuracy in denoising, features extraction, and features classification for ECG records with normal rhythm and atrial fibrillation (AF).
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Lastre-Domínguez et al. (2019) studied Electrocardiogram (ECG) signal denoising and atrial fibrillation detection (n=29,266). Adaptive unbiased finite impulse response (UFIR) smoothing algorithm vs. Savitsky-Golay, wavelet-based, low-pass, band-pass, notch, and median filters was evaluated on Accuracy of ECG signal denoising, feature extraction, and classification. The adaptive unbiased finite impulse response (UFIR) smoother demonstrated higher accuracy in ECG signal denoising, feature extraction, and classification of normal rhythm and atrial fibrillation compared to standard wavelet-based and conventional filters.
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