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Identifying Noisy ECG Signals in Large Datasets Using a Temporal Convolutional Neural Network Trained to Estimate Pseudo-SNR

  • Background: Electrocardiogram (ECG) signals are often contaminated by noise. Manual review of large ECG databases to identify noisy signals is time-consuming. Traditional signal quality assessment algorithms often do not generalize well or are computationally expensive. This study developed a Temporal Convolutional Neural Network (TCNN) to estimate the signal-to-noise ratio (SNR) of ECG signals. Method: We trained a TCNN on a proprietary database of 134,019 12-lead ECGs without any machine or human-added noise labels. Assuming that this data had high SNR, we randomly selected a single lead from each ECG and added random Gaussian noise. We then scaled the signals and added noise to give a negatively skewed normal distribution of true SNR values. We trained a TCNN to regress low- and high-frequency pseudo-SNR values from the raw noisy input signals. Results: On the testing dataset, the TCNN achieved a mean error of 0.31±1.80 dB and a Pearson correlation coefficient of 0.96 for low-frequency pseudo-SNR. Similarly, for high-frequencyBackground: Electrocardiogram (ECG) signals are often contaminated by noise. Manual review of large ECG databases to identify noisy signals is time-consuming. Traditional signal quality assessment algorithms often do not generalize well or are computationally expensive. This study developed a Temporal Convolutional Neural Network (TCNN) to estimate the signal-to-noise ratio (SNR) of ECG signals. Method: We trained a TCNN on a proprietary database of 134,019 12-lead ECGs without any machine or human-added noise labels. Assuming that this data had high SNR, we randomly selected a single lead from each ECG and added random Gaussian noise. We then scaled the signals and added noise to give a negatively skewed normal distribution of true SNR values. We trained a TCNN to regress low- and high-frequency pseudo-SNR values from the raw noisy input signals. Results: On the testing dataset, the TCNN achieved a mean error of 0.31±1.80 dB and a Pearson correlation coefficient of 0.96 for low-frequency pseudo-SNR. Similarly, for high-frequency pseudo-SNR, the mean error was 0.29±1.63 dB and the Pearson correlation coefficient was 0.97. Conclusion: A Temporal Convolutional Neural Network can accurately estimate the SNR of unseen ECGs.show moreshow less

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Metadaten
Author:Daniel GüldenringORCiD, Peter DoggartORCiD, Alan Kennedy, Raymond BondORCiD, Dewar FinlayORCiD
DOI:https://doi.org/10.22489/CinC.2023.103
Identifier:979-8-3503-8252-5 OPAC HS OPAC extern
Identifier:2325-887X OPAC HS OPAC extern
Parent Title (English):Computing in Cardiology 2023
Publisher:IEEE
Place of publication:Piscataway
Document Type:conference proceeding (article)
Conference:2023 Computing in Cardiology (CinC) 01-04. Oct. 2023, Atlanta, Georgia, USA
Language:English
Date of Publication (online):2024/02/16
Year of first Publication:2023
Tag:Biomedizinische Signalverarbeitung
ECG
GND Keyword:Künstliche Intelligenz; Elektrokardiogramm
Volume:2023
Issue:50
Number of pages:4 Seiten
First Page:1
Last Page:4
Institutes:Fakultät Elektrotechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 000 Informatik, Informationswissenschaft, allgemeine Werke
Open Access:open_access
Research focus:Eigenständige Forschungsfelder
Publication Lists:Güldenring, Daniel
Publication reviewed:begutachtet
Licence (German):Es gilt das deutsche Urheberrecht
Release Date:2024/02/16
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