ArticleConference record. Asilomar Conference on Signals, Systems & Computers
A novel method for 12-lead ECG reconstruction.
Article in Conference record. Asilomar Conference on Signals, Systems & Computers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Reconstructing 12-lead ECG from reduced lead sets using an encoder-decoder convolutional neural network.Biomedical signal processing and control · 2025Article
- A graph-based cardiac arrhythmia classification methodology using one-lead ECG recordings.Intelligent systems with applications · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
This paper presents a novel approach to synthesize a standard 12-lead electrocardiogram (ECG) from any three independent ECG leads using a patient-specific encoder-decoder convolutional neural network. The objective is to decrease the number of recording locations required to obtain the same information as a 12-lead ECG, thereby enhancing patients' comfort during the recording process. We evaluate the proposed algorithm on a dataset comprising fifteen patients, as well as a randomly selected cohort of patients from the PTB diagnostic database. To evaluate the precision of the reconstructed ECG signals, we present two metrics: the correlation coefficient and root mean square error. Our proposed method achieves superior performance compared to most existing synthesis techniques, with an average correlation coefficient of 0.976 and 0.97 for datasets, respectively. These results demonstrate the potential of our approach to improve the efficiency and comfort of ECG recording for patients, while maintaining high diagnostic accuracy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.