Derivation of chest‐lead ECG from limb‐lead using temporal convolutional network in variational mode decomposition domain

Standard 12‐lead electrocardiography (ECG) is the primary tool for detection and diagnosis of cardiovascular diseases (CVDs). Most wearable ECG devices only provide single limb‐lead measurement, limiting their practical use in CVD diagnosis. This study proposes a method of chest‐lead ECG reconstruct...

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Bibliographic Details
Published in:Electronics letters 2022-09, Vol.58 (19), p.740-742
Main Authors: Chuang, Yu‐Hung, Huang, Yu‐Chieh, Chang, Wen‐Whei, Chien, Jen‐Tzung
Format: Article
Language:English
Subjects:
Datasets
Decomposition
Diagnosis
Electrocardiography
Morphology
Spectrum allocation
Wearable technology
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Summary:Standard 12‐lead electrocardiography (ECG) is the primary tool for detection and diagnosis of cardiovascular diseases (CVDs). Most wearable ECG devices only provide single limb‐lead measurement, limiting their practical use in CVD diagnosis. This study proposes a method of chest‐lead ECG reconstruction from a single limb lead using a temporal convolutional network (TCN). The TCN is learned in the variational mode decomposition domain to reduce the non‐stationary characteristics of ECG data. Experiments on two public databases suggested that automated diagnosis of CVDs in wearable ECG devices is likely to achieve through the proposed approach.
ISSN:0013-5194
1350-911X
DOI:10.1049/ell2.12588