Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence-based algorithms in predicting left ventricular systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR). Methods and results This study is an external validation of a pre-existing deep-learning algorithm based on residual neural network architecture. Data were obtained from a prospective cohort of AF with RVR at a single centre between 2018 and 2023. Primary outcome was the detection of LVSD, defined as a LVEF ≤40%,assessed using 12-lead electrocardiography. Secondary outcome involved predicting LVSD using 1-lead electrocardiography (lead I). Among 423 patients, 241 with available echocardiography data within 2 months were evaluated, of whom 54 (22.4%) were confirmed to have LVSD. Deep-learning algorithm demonstrated fair performance in predicting LVSD (area under the curve [AUC] 0.78). Negative predictive value for excluding LVSD was 0.88. Deep-learning algorithm resulted competent performance in predicting LVSD compared to N-terminal prohormone of brain natriuretic peptide (AUC 0.78 vs. 0.70, p=0.12). Predictive performance of the deep-learning algorithm was lower in 1-lead electrocardiography (AUC 0.68), however, negative predictivevalue remained consistent (0.88). Conclusion Deep-learning algorithm demonstrated competent performance in predicting LVSD in patients with AF and RVR. In outpatient setting, use of artificial intelligence-based algorithm may facilitate prediction of LVSD and earlier choice of drug, enabling better symptom control in AF patients with RVR

Domain generalization involves training machine learning models to perform robustly on unseen samples from out-of-distribution datasets. Adversarial Data Augmentation (ADA) is a commonly used approach that enhances model adaptability by incorporating synthetic samples, designed to simulate potential unseen samples. While ADA effectively addresses amplitude-related distribution shifts, it falls short in managing temporal shifts, which are essential for time series data. To address this limitation, we propose the Temporal Adversarial Data Augmentation for time teries Data (TADA), which incorporates a time warping technique specifically targeting temporal shifts. Recognizing the challenge of non-differentiability in traditional time warping, we make it differentiable by leveraging phase shifts in the frequency domain. Our evaluations across diverse domains demonstrate that TADA significantly outperforms existing ADA variants,enhancing model performance across time series datasets with varied distributions.

Foundation models, enhanced by self-supervised learning (SSL) techniques, represent a cutting-edge frontier in biomedical signal analysis, particularly for electrocardiograms (ECGs), crucial for cardiac health monitoring and diagnosis. This study conducts a comprehensive analysis of foundation models for ECGs by employing and refining innovative SSL methodologies - namely, generative and contrastive learning - on a vast dataset of over 1.1 million ECG samples. By customizing these methods to align with the intricate characteristics of ECG signals, our research has successfully developed foundation models that significantly elevate the precision and reliability of cardiac diagnostics. These models are adept at representing the complex, subtle nuances of ECG data, thus markedly enhancing diagnostic capabilities. Theresults underscore the substantial potential of SSL-enhanced foundation models in clinical settings and pave the way for extensive future investigations into their scalable applications across a broader spectrum of medical diagnostics. This work sets a benchmark in the ECG field, demonstrating the profound impact of tailored, data-driven model training on the efficacy and accuracy of medical diagnostics.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.

Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department. Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board. This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments.

Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear.This study aimed to evaluate the effectiveness of a 12-lead electrocardiography–based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy.This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography–based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05.Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953–1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499–0.951), a sensitivity of 0.917 (95% confidence interval, 0.760–1.000), a specificity of 0.927 (95% confidence interval, 0.890–0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245–0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983–1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895–0.993), 0.520 (95% confidence interval, 0.319–0.801), 0.833 (95% confidence interval, 0.622–1.000), 0.880 (95% confidence interval, 0.834–0.926), 0.303 (95% confidence interval, 0.146–0.460), and 0.988 (95% confidence interval, 0.972–1.000), respectively.The 12-lead electrocardiography–based artificial intelligence/machine learning–based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.

Question answering (QA) in the field of healthcare has received much attention due to significant advancements in natural language processing. However, existing healthcare QA datasets primarily focus on medical images, clinical notes, or structured electronic health record tables. This leaves the vast potential of combining electrocardiogram (ECG) data with these systems largely untapped. To address this gap, we present ECG-QA, the first QA dataset specifically designed for ECG analysis. The dataset comprises a total of 70 question templates that cover a wide range of clinically relevant ECG topics, each validated by an ECG expert to ensure their clinical utility. As a result, our dataset includes diverse ECG interpretation questions, including those that require a comparative analysis of two different ECGs. In addition, we have conducted numerous experiments to provide valuable insights for future research directions. We believe that ECG-QA will serve as a valuable resource for the development of intelligent QA systems capable of assisting clinicians in ECG interpretations.

Electrocardiogram (ECG) synthesis is the area of research focused on generating realistic synthetic ECG signals for medical use without concerns over annotation costs or clinical data privacy restrictions. Traditional ECG generation models consider a single ECG lead and utilize GAN-based generative models. These models can only generate single lead samples and require separate training for each diagnosis class. The diagnosis classes of ECGs are insufficient to capture the intricate differences between ECGs depending on various features (e.g. patient demographic details, co-existing diagnosis classes, etc.). To alleviate these challenges, we present a text-to-ECG task, in which textual inputs are used to produce ECG outputs. Then we propose Auto-TTE, an autoregressive generative model conditioned on clinical text reports to synthesize 12-lead ECGs, for the first time to our knowledge. We compare the performance of our model with other representative models in text-to-speech and text-to-image. Experimental results show the superiority of our model in various quantitative evaluations and qualitative analysis. Finally, we conduct a user study with three board-certified cardiologists to confirm the fidelity and semantic alignment of generated samples. our code will be available at https://github.com/TClife/text_to_ecg

Electrocardiogram (ECG) is the first-line modality for identifying acute myocardial infarction (AMI) in patients with chest pain. However, the ECG can even be normal in AMI patients, thereby delaying diagnosis and adversely affecting prognosis. We aim to develop a deep-learning algorithm for detecting AMI using 12-lead ECG (DAMI-ECG).This study included retrospective cohorts from 2 separate hospitals. We developed and validated DAMI-ECG and estimated the diagnostic performance in patients who visited the emergency department (ED) for chest pain. Furthermore, we compared the accuracy of DAMI-ECG through interpretations of an ECG machine and experienced cardiologists.A total of 227,912 ECGs from 114,600 patients were used to develop and validate DAMI-ECG, and 2,274 ECGs from 1,765 patients who visited the ED for chest pain were used to estimate the diagnostic performance in a clinical setting. Among development and validation datasets, the area under the receiver operating characteristic curve of the DAMI-ECG for detecting AMI was 0.927 (95% confidence interval, 0.908–0.945) and 0.914 (0.899–0.929) during internal and external validations. The diagnostic accuracy of DAMI-ECG for detecting AMI in patients with chest pain at the ED was 86.1% (83.6–88.6%), which outperformed that of experienced cardiologists (78.6% [76.8–80.1%]) using ECG alone.In conclusion, the diagnostic performance of DAMI-ECG is excellent in detecting AMI in patients visiting the ED for chest pain and superior to that of cardiologists. This algorithm can be used in the ED or pre-hospital setting for early AMI diagnosis.

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. To evaluate whether the novel method can facilitate MI diagnosis by only 2-lead ECG. 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. 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. 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. 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. Novel ECG T2T algorithm demonstrated favorable performance in detecting MI using 12-lead ECG. The model is comprised of style, mapping, generative, and discriminative networks. Each network is built with residual blocks.The green line denotes the original signal, while the pink line represents the signals generated by ECGT2T.