Abstract 13564: Novel Electrocardiogram Generating Technique Using Artificial Intelligence: From 2-lead to 12-lead

• Published in: Circulation (New York, N.Y.) Vol. 146; no. Suppl_1
• Main Authors: Lee, Hak Seung, Jo, Yong-Yeon, Kang, Sora, Jang, Jong-Hwan, Son, Jeong Min, Lee, Min Sung, Kwon, Joon-Myoung
• Format: Journal Article
• Language: English
• Published: 08.11.2022
• DOI: 10.1161/circ.146.suppl_1.13564
• ISSN: 0009-7322

Abstract only Introduction: Based on the development of AI (artificial intelligence) and big data, an emerging number of methods achieved outstanding performance in myocardial infarction (MI) diagnosis by an electrocardiogram (ECG). However, conventional interpretation methods have low reliability for detecting MI and are challenging to apply to 2 leads of wearable devices. Objectives: To evaluate whether the novel method can facilitate MI diagnosis by only 2-lead ECG. Methods: We propose T2T (from 2-lead to 12-lead), a deep generative model that simulates a standard 12-lead ECG from an input of two asynchronous leads by generating ten leads. ( Figure a ) We used and selected 15,012 ECGs (9,527 normal, 5,485 samples with any MI) from the Physikalisch-Technische Bundesanstalt dataset. This dataset was split into stratified training, validation, and test sets with a ratio of 7:1:2. Three models using 2-lead, T2T, and 12-lead ECG were developed and validated to predict a diagnosis of MI, and their accuracy was compared. Results: The area under the receiver operating characteristic curves of the 2-lead, T2T and 12-lead ECG were 0.937, 0.948, and 0.960, respectively. Figure b shows representative cases over a two-second window, where the green and pink lines are the signals from the original ECG and T2T. The generated signals have less noise (seen in aVR and aVL in 2a and V4, V5, and V6) and baseline wander. For the V1 and V5 leads generated by T2T, the differences in amplitude are 6.4% and 7.3%, respectively, and the missing positional errors are under 10 ms. Conclusions: Novel ECG T2T algorithm demonstrated favorable performance in detecting MI using 12-lead ECG. Figure a. ECGT2T model architecture. The model is comprised of style, mapping, generative, and discriminative networks. Each network is built with residual blocks. Figure b. Representative samples of ECG T2T. The green line denotes the original signal, while the pink line represents the signals generated by ECGT2T.