Artificial intelligence (AI) has demonstrated significant potential in ECG analysis and cardiovascular disease assessment. Recently, foundation models have played a remarkable role in advancing medical AI. The development of an ECG foundation model holds the promise of elevating AI-ECG research to new heights. However, building such a model faces several challenges, including insufficient database sample sizes and inadequate generalization across multiple domains. Additionally, there is a notable performance gap between single-lead and multi-lead ECG analyses. We introduced an ECG Foundation Model (ECGFounder), a general-purpose model that leverages real-world ECG annotations from cardiology experts to broaden the diagnostic capabilities of ECG analysis. ECGFounder was trained on over 10 million ECGs with 150 label categories from the Harvard-Emory ECG Database, enabling comprehensive cardiovascular disease diagnosis through ECG analysis. The model is designed to be both an effective out-of-the-box solution, and a to be fine-tunable for downstream tasks, maximizing usability. Importantly, we extended its application to lower rank ECGs, and arbitrary single-lead ECGs in particular. ECGFounder is applicable to supporting various downstream tasks in mobile monitoring scenarios. Experimental results demonstrate that ECGFounder achieves expert-level performance on internal validation sets, with AUROC exceeding 0.95 for eighty diagnoses. It also shows strong classification performance and generalization across various diagnoses on external validation sets. When fine-tuned, ECGFounder outperforms baseline models in demographic analysis, clinical event detection, and cross-modality cardiac rhythm diagnosis. The trained model and data will be publicly released upon publication through the bdsp.io. Our code is available at https://github.com/bdsp-core/ECGFounder

In the context of cardiovascular diseases (CVD) that exhibit an elevated prevalence and mortality, the electrocardiogram (ECG) is a popular and standard diagnostic tool for doctors, commonly utilizing a 12-lead configuration in clinical practice. However, the 10 electrodes placed on the surface would cause a lot of inconvenience and discomfort, while the rapidly advancing wearable devices adopt the reduced-lead or single-lead ECG to reduce discomfort as a solution in long-term monitoring. Since the single-lead ECG is a subset of 12-lead ECG, it provides insufficient cardiac health information and plays a substandard role in real-world healthcare applications. Hence, it is necessary to utilize signal generation technologies to reduce their clinical importance gap by reconstructing 12-lead ECG from the real single-lead ECG. Specifically, this study proposes a multi-channel masked autoencoder (MCMA) for this goal. In the experimental results, the visualized results between the generated and real signals can demonstrate the effectiveness of the proposed framework. At the same time, this study introduces a comprehensive evaluation benchmark named ECGGenEval, encompassing the signal-level, feature-level, and diagnostic-level evaluations, providing a holistic assessment of 12-lead ECG signals and generative model. Further, the quantitative experimental results are as follows, the mean square errors of 0.0178 and 0.0658, correlation coefficients of 0.7698 and 0.7237 in the signal-level evaluation, the average F1-score with two generated 12-lead ECG is 0.8319 and 0.7824 in the diagnostic-level evaluation, achieving the state-of-the-art performance. The open-source code is publicly available at \url{https://github.com/CHENJIAR3/MCMA}.

Introduction: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term, ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. Results: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91--0.94 in RBDB and 0.93 in SHDB, compared to 0.89--0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.

The use of artificial intelligence has been a hot topic in cardiology for the past few years. For example, deep neural networks can be used to analyze ECG tracings and may be more accurate than human experts. Even with these advantages, there may be some hesitation on completely relying on this technology. In a recent research letter published in Nature Medicine, researchers developed a way to integrate smoothed adversarial examples for single-lead ECGs. Researchers found that when subtle adversarial perturbations that are indistinguishable to the human eye were added to ECG tracings, the misdiagnosis rate of the deep learning algorithm was 74%.