Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG

Abstract

Background: We developed and validated an artificial intelligence (AI)-enabled smartwatch ECG to detect heart failure-reduced ejection fraction (HFrEF).

Methods: This was a cohort study involving two hospitals (A and B). We developed the AI in two steps. First, we developed an AI model (ECGT2T) to synthesize ten-lead ECG from the asynchronized 2-lead ECG (Lead I and II). ECGT2T is a deep learning model based on a generative adversarial network, which translates source ECGs to reference ECGs by learning styles of the reference ECGs. For this, we included adult patients aged ≥18 years from hospital A with at least one digitally stored 12-lead ECG. Second, we developed an AI model to detect HFrEF using a 10 s 12-lead ECG. The AI model was based on convolutional neural network. For this, we included adult patients who underwent ECG and echocardiography within 14 days. To validate the AI, we included adult patients from hospital B who underwent two-lead smartwatch ECG and echocardiography on the same day. The AI model generates a 10 s 12-lead ECG from a two-lead smartwatch ECG using ECGT2T and detects HFrEF using the generated 12-lead ECG.

Results: We included 137,673 patients with 458,745 ECGs and 38,643 patients with 88,900 ECGs from hospital A for developing the ECGT2T and HFrEF detection models, respectively. The area under the receiver operating characteristic curve of AI for detecting HFrEF using smartwatch ECG was 0.934 (95% confidence interval 0.913-0.955) with 755 patients from hospital B. The sensitivity, specificity, positive predictive value, and negative predictive value of AI were 0.897, 0.860, 0.258, and 0.994, respectively.

Conclusions: An AI-enabled smartwatch 2-lead ECG could detect HFrEF with reasonable performance.

Keywords: artificial intelligence; deep learning; electrocardiography; heart failure.

Conflict of interest statement

K.-H.K. and S.Y.L. declare that they have no competing interests. J.-m.K. is co-founder and Y.-Y.J., MS Lee, S K., and S.-Y.L. are researchers of Medical AI Inc., a medical artificial intelligence company. J.-m.K. is a researcher of Body friend Co., Ltd. There are no products in development or marketed products to declare. This does not alter our adherence to this journal.

Figures

Figure 1

Study flowchart. Legend: AI denotes artificial intelligence, ECG electrocardiography, ECGT2T ECG synthesis from two-lead to ten-lead, HF heart failure, HFmrEF heart failure with mildly reduced ejection fraction, and HFrEF heart failure with reduced ejection fraction.

Figure 2

Architecture of deep learning model for detecting heart failure. Legend: ECG denotes electrocardiography, ECGT2T ECG synthesis from two-lead to ten-lead, and HF heart failure. (A) Asynchronous two lead ECGs from smart watch. (B) ECGT2T for synthesizing ten lead ECG from two lead ECG. (C) Generated twelve lead ECG which input to final AI model. (D) Deep learning model for detecting heart failure with reduced ejection fraction using generated twelve lead ECG.

Figure 3

12-lead ECG generation using smartwatch ECG based on ECGT2T. Legend: ECG denotes electrocardiography and ECGT2T ECG synthesis from two-lead to ten-lead.

Figure 4

Performance of artificial intelligence for detecting heart failure using smart watch ECG. Legend: AUC denotes area under the receiver operating characteristic curve, CI confidence interval, ECG electrocardiography, NPV negative predictive value, PPV positive predictive value, SEN sensitivity, and SPE specificity.