Key result
An oversampling version of the Cooley-Tukey fast-Fourier transform algorithm successfully filtered 50 Hz hum from electrocardiogram waves to uncover the QRS-complex with high resolution.
Why the study?
Current fast-Fourier transform algorithms based on the Cooley-Tukey paper and Danielson-Lanczos lemma do not deliver the metrological precision class required by contemporary technology.
Answers are grounded in this paper and cite their sources.
A novel oversampling Fourier transform algorithm improves the precision of filtering 50 Hz noise from ECG signals, enhancing QRS-complex resolution.
Current fast-Fourier transform algorithms are based on the Cooley-Tukey paper of 1965 and the Danielson-Lanczos lemma. From a practical point of view however, these algorithms (albeit correct) do not deliver the metrological precision class required by contemporary technology. We present an oversampling version of the Cooley-Tukey algorithm that delivers the required precision and apply it to filtering 50 Hz hum out from electrocardiogram (ECG) waves, uncovering with good resolution the QRS-complex. Such precision algorithms are crucial to parameter-integrity when neural-software methods use them in medicine.
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Pitina et al. (2020) studied Electrocardiogram (ECG) signal processing. Oversampling Cooley-Tukey fast-Fourier transform algorithm vs. Current fast-Fourier transform algorithms was evaluated on Filtering 50 Hz hum out from ECG waves. An oversampling version of the Cooley-Tukey fast-Fourier transform algorithm successfully filtered 50 Hz hum from electrocardiogram waves to uncover the QRS-complex with high resolution.
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