Test-Time Adaptation (TTA) methods have been widely used to enhance model robustness by continuously updating pre-trained models with unlabeled target data. However, in real-world biosignal applications-where factors such as age, lifestyle, and comorbidities induce significant variability—traditional TTA often falls short in addressing personalization needs. To satisfy such needs, we introduce a novel Test-Time Calibration (TTC) framework that integrates continuous self supervised adaptation on unlabeled samples with periodic supervised calibration using the sporadically available ground-truth labels. Our approach leverages a model equipped with dual heads for supervised learning (SL) and self-supervised learning (SSL), and further incorporates a dual buffer along with a weighted batch sampling strategy to effectively manage and utilize both data types during the test phase. We evaluate our framework on two distinct datasets: the publicly available PulseDB, a benchmark for cuff-less blood pressure estimation, and a private ICU dataset collected from critically ill patients. Experimental results demonstrate that our approach improves blood pressure prediction accuracy and robustness, highlighting its suitability for dynamic, personalized biosignal applications.

We present a new perspective on scaling neural networks for electrocardiograms (ECG). Although ResNet-based models are widely used in ECG classification, the potential benefits of network scaling remain unexplored. Our research investigates the impact of changes in the depth of layers, the number of channels, and the dimensions of the convolution kernels on performance. Contrary to computer vision practices, we found that shallower networks, with more channels and smaller kernels, lead to better performance for ECG classifications. Based on these findings, we provide insights that can guide the efficient development of models in practice. Finally, we explore why scaling hyperparameters affects ECG and computer vision differently. Our findings suggest that the inherent periodicity of the ECG signals plays a crucial role in this difference.

Online Test-Time Adaptation (OTTA) enhances model robustness by updating pretrained models with unlabeled data during testing. In healthcare, OTTA is vital for realtime tasks like predicting blood pressure from biosignals, which demand continuous adaptation. We introduce a new test-time scenario with streams of unlabeled samples and occasional labeled samples. Our framework combines supervised and self-supervised learning, employing a dual-queue buffer and weighted batch sampling to balance data types. Experiments show improved accuracy and adaptability under real-world conditions.

The real-world effectiveness of the artificial intelligence model based on electrocardiogram (AI-ECG) signals from portable devices for detection of left ventricular systolic dysfunction (LVSD) requires further exploration. In this prospective, single-centre study, we assessed the diagnostic performance of AI-ECG for detecting LVSD using a six-lead hand-held portable device (AliveCor KardiaMobile 6L). We retrained the AI-ECG model, previously validated with 12-lead ECG, to interpret the 6-lead ECG inputs. Patients aged 19 years or older underwent six-lead ECG recording during transthoracic echocardiography. The primary outcome was the area under the receiver operating characteristic curve (AUROC) for detecting LVSD, defined as an ejection fraction below 40%. Of the 1716 patients recruited prospectively, 1635 were included for the final analysis (mean age 60.6 years, 50% male), among whom 163 had LVSD on echocardiography. The AI-ECG model based on the six-lead portable device demonstrated an AUROC of 0.924 [95% confidence interval (CI) 0.903–0.944], with 83.4% sensitivity (95% CI 77.8–89.0%) and 88.7% specificity (95% CI 87.1–90.4%). Of the 1079 patients evaluated using the AI-ECG model based on the conventional 12-lead ECG, the AUROC was 0.962 (95% CI 0.947–0.977), with 90.1% sensitivity (95% CI 85.0–95.2%) and 91.1% specificity (95% CI 89.3–92.9%). The AI-ECG model constructed with the six-lead hand-held portable ECG device effectively identifies LVSD, demonstrating comparable accuracy to that of the conventional 12-lead ECG. This highlights the potential of hand-held portable ECG devices leveraged with AI as efficient tools for early LVSD screening.

Despite the proliferation of heart failure (HF) mortality prediction models, their practical utility is limited. Addressing this, we utilized a significant dataset to develop and validate a deep learning artificial intelligence (AI) model for predicting one-year mortality in heart failure with reduced ejection fraction (HFrEF) patients. The study’s focus was to assess the effectiveness of an AI algorithm, trained on an extensive collection of ECG data, in predicting one-year mortality in HFrEF patients.

Emerging evidence supports artificial intelligence–enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI), but real-world validation is needed. The aim of this study was to evaluate the performance of AI-ECG in detecting AMI in the emergency department (ED). The Rule-Out acute Myocardial Infarction using Artificial intelligence Electrocardiogram analysis (ROMIAE) study is a prospective cohort study conducted in the Republic of Korea from March 2022 to October 2023, involving 18 university-level teaching hospitals. Adult patients presenting to the ED within 24 h of symptom onset concerning for AMI were assessed. Exposure included AI-ECG score, HEART score, GRACE 2.0 score, high-sensitivity troponin level, and Physician AMI score. The primary outcome was diagnosis of AMI during index admission, and the secondary outcome was 30 day major adverse cardiovascular event (MACE). The study population comprised 8493 adults, of whom 1586 (18.6%) were diagnosed with AMI. The area under the receiver operating characteristic curve for AI-ECG was 0.878 (95% CI, 0.868–0.888), comparable with the HEART score (0.877; 95% CI, 0.869–0.886) and superior to the GRACE 2.0 score, high-sensitivity troponin level, and Physician AMI score. For predicting 30 day MACE, AI-ECG (area under the receiver operating characteristic, 0.866; 95% CI, 0.856–0.877) performed comparably with the HEART score (0.858; 95% CI, 0.848–0.868). The integration of the AI-ECG improved risk stratification and AMI discrimination, with a net reclassification improvement of 19.6% (95% CI, 17.38–21.89) and a C-index of 0.926 (95% CI, 0.919–0.933), compared with the HEART score alone. In this multicentre prospective study, the AI-ECG demonstrated diagnostic accuracy and predictive power for AMI and 30 day MACE, which was similar to or better than that of traditional risk stratification methods and ED physicians.

This study explores the use of artificial intelligence-enabled electrocardiogram (AI-ECG) technology combined with smartwatch-based daily monitoring to predict heart failure (HF) rehospitalization by detecting early signs of heart failure exacerbation, such as left ventricular systolic dysfunction (LVSD), left ventricular diastolic dysfunction (LVDD), and myocardial infarction (MI). Traditional monitoring methods have limitations, and AI-ECG offers a scalable, noninvasive, cost-effective solution. The study will evaluate the impact of this technology on reducing rehospitalization rates in a prospective, multicenter trial involving 220 adult patients recently discharged after HF hospitalization. The primary endpoint is a reduction in HF rehospitalization rates within 90 days, with secondary endpoints including time to clinical intervention, unplanned hospitalizations, and improvements in mortality and quality of life. This approach represents a promising, patient-friendly solution for better HF management.

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.