Key result
Blackman windowing improves ECG classification accuracy vs Hamming and Hann windows by reducing spectral leakage.
Why the study?
The effectiveness of deep learning models for automated ECG arrhythmia diagnosis depends heavily on signal preprocessing quality, but the impact of different windowing techniques applied to Fourier transform-preprocessed signals remained unclear.
Does the Blackman windowing technique improve classification accuracy of deep learning models for ECG signals compared to Hamming and Hann windows?
Does the Blackman windowing technique improve classification accuracy of deep learning models for ECG signals compared to Hamming and Hann windows?
Answers are grounded in this paper and cite their sources.
The Blackman windowing technique optimizes Fourier transform-preprocessed ECG signals, improving the accuracy of deep learning models for arrhythmia classification.
(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends greatly on the quality of signal preprocessing. This study evaluated the impact of different windowing techniques applied to Fourier transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A one-dimensional convolutional neural network was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal. (3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.
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Martono et al. (2024) studied Arrhythmias. Blackman windowing technique vs. Hamming and Hann windows was evaluated on Classification accuracy of ECG signals into five arrhythmia categories. The Blackman windowing technique yielded the highest classification accuracy, improved signal-to-noise ratio, and reduced spectral leakage for ECG classification compared to Hamming and Hann windows.
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