12-Lead ECG Reconstruction Based on Data From the First Limb Lead

Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system’s (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the body, and the need for specialized equipment make the ECG acquisition procedure complex and cumbersome. This raises the challenge of reducing the number of ECG leads by reconstructing missing leads based on available data.

Most existing methods for reconstructing missing ECG leads rely on utilizing signals simultaneously from multiple known leads. This study proposes a method for reconstructing ECG data in 12 leads using signal data from the first lead, lead I. Such an approach can significantly simplify the ECG registration procedure. The study demonstrates the effectiveness of using unique models with a developed architecture of artificial neural networks (ANNs) to generate the reconstructed ECG signals. Fragments of ECG from lead I, with a duration of 128 samples and a sampling frequency of 100 Hz, are input to the models. ECG fragments can be extracted from the original signal at arbitrary time points. Each model generates an ECG signal of the same length at its output for the corresponding lead.

Despite existing limitations, the proposed method surpasses known solutions regarding ECG generation quality when using a single lead. The study shows that introducing an additional feature of the direction of the electrical axis of the heart (EAH) as input to the ANN models enhances the generation quality. The quality of ECG generation by the proposed ANN models is found to be dependent on the presence of CVS diseases.

The developed ECG reconstruction method holds significant potential for use in portable registration devices, screening procedures, and providing support for medical decision-making by healthcare specialists.