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. 2023 Oct-Nov:2023:1054-1058.
doi: 10.1109/ieeeconf59524.2023.10476822.

A novel method for 12-lead ECG reconstruction

Dorsa EPMoghaddam  1 , Anton Banta  1 , Allison Post  2 , Mehdi Razavi  3 , Behnaam Aazhang  1
Affiliations

A novel method for 12-lead ECG reconstruction

Dorsa EPMoghaddam et al. Conf Rec Asilomar Conf Signals Syst Comput. 2023 Oct-Nov.

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.

Keywords: Signal reconstruction; cardiovascular diseases; convolutional neural network; electrocardiogram (ECG); encoder-decoder.

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Figures

Fig. 1.
Fig. 1.
Different components of an ECG waveform. This heartbeat is sampled from patient 098 in PTB database.
Fig. 2.
Fig. 2.
An overview of the proposed method. Note that FC stands for fully-connected layer.
Fig. 3.
Fig. 3.
A reconstruction example. The original ECGs are in blue, and the synthesis signals are in orange.
See this image and copyright information in PMC

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