Electrocardiogram lead conversion from single-lead blindly-segmented signals

Background The standard configuration’s set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient’s limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can recons...

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Veröffentlicht in:BMC medical informatics and decision making Jg. 22; H. 1; S. 314 - 15
Hauptverfasser: Beco, Sofia C., Pinto, João Ribeiro, Cardoso, Jaime S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 29.11.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Adaptation
Autoencoder
BMC Supplements Reviewed
Coders
Configurations
Conversion
Databases, Factual
Deep learning
EKG
Electrocardiogram
Electrocardiogram (ECG)
Electrocardiography
Electrodes
Encoders-Decoders
Feasibility studies
Health Informatics
Heart
Heart Diseases
Humans
Information Systems and Communication Service
Leads
Machine learning
Management of Computing and Information Systems
Medical diagnosis
Medicine
Medicine & Public Health
Methodology
Methods
Neural networks
Regression analysis
Signal processing
U-Net
Wearable computers
Wearable Electronic Devices
Wearable technology
Portugal
Deep learning
Leads
Electrocardiogram (ECG)
Autoencoder
Conversion
U-Net
ISSN:1472-6947, 1472-6947
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Zusammenfassung:Background The standard configuration’s set of twelve electrocardiogram (ECG) leads is optimal for the medical diagnosis of diverse cardiac conditions. However, it requires ten electrodes on the patient’s limbs and chest, which is uncomfortable and cumbersome. Interlead conversion methods can reconstruct missing leads and enable more comfortable acquisitions, including in wearable devices, while still allowing for adequate diagnoses. Currently, methodologies for interlead ECG conversion either require multiple reference (input) leads and/or require input signals to be temporally aligned considering the ECG landmarks. Methods Unlike the methods in the literature, this paper studies the possibility of converting ECG signals into all twelve standard configuration leads using signal segments from only one reference lead, without temporal alignment (blindly-segmented). The proposed methodology is based on a deep learning encoder-decoder U-Net architecture, which is compared with adaptations based on convolutional autoencoders and label refinement networks. Moreover, the method is explored for conversion with one single shared encoder or multiple individual encoders for each lead. Results Despite the more challenging settings, the proposed methodology was able to attain state-of-the-art level performance in multiple target leads, and both lead I and lead II seem especially suitable to convert certain sets of leads. In cross-database tests, the methodology offered promising results despite acquisition setup differences. Furthermore, results show that the presence of medical conditions does not have a considerable effect on the method’s performance. Conclusions This study shows the feasibility of converting ECG signals using single-lead blindly-segmented inputs. Although the results are promising, further efforts should be devoted towards the improvement of the methodologies, especially the robustness to diverse acquisition setups, in order to be applicable to cardiac health monitoring in wearable devices and less obtrusive clinical scenarios.
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ISSN:1472-6947
1472-6947
DOI:10.1186/s12911-022-02063-6