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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Vydáno v:	BMC medical informatics and decision making Ročník 22; číslo 1; s. 314 - 15
Hlavní autoři:	Beco, Sofia C., Pinto, João Ribeiro, Cardoso, Jaime S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 29.11.2022 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23
ISSN:	1472-6947 1472-6947
DOI:	10.1186/s12911-022-02063-6