ECG Monitoring and Analysis

Últimos artículos

• Robust Transfer Learning for Paper ECG Recognition

  Yinghao Xie, Zhenbang Dai, Haojun Wang, Jinyu Cai, Fabio Bonassi, Hongwu Chen, Johan Sundstr\"om, Jiawei Li, Ant\^onio H. Ribeiro · 1 de octubre de 2026

  Paper ECG recognition is challenging because real-world ECG images vary in layout, physical artifacts, and label availability. We introduce RobECG-CL, a rank-aware contrastive learning framework for robust paper ECG representation learning. Starting from standard 12-lead ECG recordings, we construct…

• NeuroECG: ECGFounder-Based Deep ECG Representation for EEG-Free Neurological Prognostication After Cardiac Arrest

  Jiajun Gao, Yi Zhao, Chenyang Xu, Yuxi Zhou, Hao Wang · 30 de septiembre de 2026

  Neurological prognostication after cardiac arrest commonly relies on electroencephalography (EEG). However, EEG demands high clinical resources. Bedside electrocardiography (ECG) is standard and low-cost. Yet, its value for predicting neurological outcomes remains underexplored. In this study, we pr…

• TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care

  Lovely Yeswanth Panchumarthi, Andrew Lu, Saurabh Kataria, Delgersuren Bold, Minxiao Wang, Runze Yan, Patricia Dykes, Brian J. Gow, Tom J. Pollard, Jessica K. Z\`egre-Hemsey, Dillon J. Dzikowicz, Lekshmi Kumar, Xiao Hu, Ran Xiao · 29 de septiembre de 2026

  TRACE (Text-Reinforced Analysis of Cardio ECGs) is a multimodal electrocardiogram (ECG) representation model that learns clinically grounded signal embeddings for downstream cardiac classification. It is designed to address the limitations of existing CLIP-style training, which often struggles with …

• ECG-Scroll: A Long-Horizon, Streaming Benchmark and Agent Environment for Interpretation of Ambulatory Electrocardiograms

  Haitao Li, Chenglin Li, Zhengyao Ding, Ziyu Li, Yiheng Mao, Zhengxing Huang · 29 de septiembre de 2026

  Multimodal large language models (MLLMs) can now interpret a standard ten-second, twelve-lead electrocardiogram (ECG) with clinically grounded, reward-verified reasoning. Real cardiac monitoring is different. Ambulatory (Holter) and telemetry recordings span hours to days and are read as they stream…

• Logic Gate Networks and Lookup Table Networks as Lightweight Hardware Classifiers for Inter-patient ECG Arrhythmia Classification

  Wout Mommen, Lars Keuninckx, Siddharth Patil, Paul Detterer, Achiel Colpaert, Piet Wambacq · 29 de septiembre de 2026

  Deep Differentiable Logic Gate Networks (LGNs) and Lookup Table Networks (LUTNs) offer a promising approach for very low power inference due to their use of simple binary logic operations instead of arithmetic. In this work, we generalize the logic gates of LGNs to more than two input pins, naturall…

• BeatGraph: Self-Supervised Heartbeat Graphs for Infant ECG Representations from the Home Environment

  Mohammad Nur Hossain Khan, M. S. Krafczyk, Beverly G. Bolster, Nancy McElwain, Mark A. Hasegawa-Johnson, Bashima Islam · 28 de septiembre de 2026

  Electrocardiogram (ECG) foundation models typically tokenize the signal into fixed-length patches that ignore cardiac structure, so a patch may split a heartbeat and the number of beats in each patch shifts with heart rate. This matters most for infants, whose heart rates are higher and whose ECG di…

• TinyCardioUNet: IMU-to-ECG Translation with Graph-Encoded Inter-Axis Dependencies and Tensor Decomposition-Based Parameter Reduction

  Seungwoo Han, Ingon Chanpornpakdi, Motoi Noda, Puwadej Leelasiri, Ibuki Hiruma, Toshihisa Tanaka · 25 de septiembre de 2026

  Estimating electrocardiography (ECG) from a chest-worn inertial measurement unit (IMU) enables continuous heart rate (HR) monitoring without the discomfort of electrodes. We propose TinyCardioUNet, a lightweight UNet that uses all six IMU axes without prior channel selection, refines its bottleneck …

• A Hybrid CNN--State-Space--Attention Backbone with Joint-Embedding Predictive Pretraining for 12-Lead ECG Classification

  Yakoub Bazi, Sarah Aljuhani, Mohamad M. Al Rahhal, Mansour Zuair, Naif Alajlan · 25 de septiembre de 2026

  Automatic 12-lead electrocardiogram (ECG) classification requires representations that jointly capture local waveform morphology, long-range temporal dynamics, and cross-lead dependencies, yet integrating these properties within a single efficient architecture remains challenging. This paper introdu…

• Wearable ECG Quality Assessment: A Deep Learning and Ambulatory Context-Awareness Approach

  Xiaopeng Mao, Marike Weisbjerg, Sadasivan Puthusserypady · 25 de septiembre de 2026

  This paper presents and evaluates a Deep Learning-based (DL-based) Signal Quality Assessment (SQA) model to distinguish between clean and noisy ambulatory Electrocardiograms (ECG). The model is trained on Copenhagen Center for Health Technology-Contextualized Arrhythmia Database (CACHET-CADB), which…

• The Entropy Triangle Method (ETM): A novel framework for the prevention of cardiac arrhythmia with a review of more than 10,000 patients

  Arman daliri · 25 de septiembre de 2026

  One of the most important problems in medicine is to facilitate prediction. In this study, we propose entropy triangle method, a novel framework for predicting heart rhythms using a novel machine learning technique. This framework includes three steps: feature engineering, entropy triangle oversampl…

• Signal2Symbol: Neuro-Symbolic Temporal Reasoning for Explainable Physiological Time-Series Anomaly Detection

  Naser Mansour, Sidahmed Benabderrahmane, Ameer Rahwan · 24 de septiembre de 2026

  Physiological time series such as electrocardiograms (ECG) and electroencephalograms (EEG) exhibit complex temporal structure, substantial acquisition variability, and a strong need for transparent decision-making. Although deep models can achieve high detection performance, they often provide limit…

• From ECG Signals to Representative-Morphology Heatmaps for Biometric Recognition

  Athanasios Angelakis, Marta Gomez-Barrero · 24 de septiembre de 2026

  Electrocardiography (ECG) contains subject-specific morphology that supports biometric recognition, yet image-based performance depends on how the waveform is rendered. We introduce representative-morphology heatmaps, a deterministic ECG-to-image representation adapted from ECGXtractor. Within each …

• TRACE: Tractable Routing Autoencoder for Clinical ECG

  Shunbo Jia, Runze Ma, Haonan Lyu, Haijin Zhang, Qiang Yang, Caizhi Liao · 22 de septiembre de 2026

  Deep learning has advanced automated electrocardiogram (ECG) diagnosis, but the field's most accurate models, foundation models pretrained on millions of recordings, are not decision-pathway auditable: a clinician cannot trace a diagnosis to a physiological pathway or intervene on one. We propose TR…

• X-Beat: An Explainable Framework for ECG Image Classification

  Mohammad Sadman Tahsin, Haitham Y. Adarbah, Afzel Noore · 22 de septiembre de 2026

  Accurate automated interpretation of electrocardio- grams (ECGs) is essential for early detection of cardiac condi- tions such as myocardial infarction and rhythm abnormalities. However, many high-performing deep learning models remain difficult to deploy in clinical settings due to limited transpar…

• ECG-Mamba-V2: Architectural Refinements to a Bidirectional State Space Model for Multi-Label 12-Lead ECG Classification

  Huawei Jiang, Husna Mutahira, Shibo Wei, Gan Huang, Vladimir Shin, Dongryeol Ryu, Juneho Yi, Mannan Saeed Muhammad · 21 de septiembre de 2026

  State space models offer linear-time sequence modeling and are a promising backbone for multi-label 12-lead ECG classification, but the design choices that drive their accuracy remain unclear. This letter presents ECG-Mamba-V2, a set of empirical refinements to a bidirectional Vision Mamba encoder: …

• Learning Cardiac Features: ECG Biometrics Across Time and~Exercise

  Luca Thiebaud (AMU, AMU SCI, DIAPRO, LIS), Paul Chauchat (AMU SCI, AMU, LIS, DIAPRO), Mustapha Ouladsine (AMU SCI, AMU, LIS, DIAPRO), St\'ephane Delliaux (AMU, APHM, C2VN) · 21 de septiembre de 2026

  Electrocardiograms (ECGs) carry subject-specific patterns enabling reliable individual discrimination, forming the basis of ECG biometrics. Beyond authentication, this paradigm holds significant potential to secure sensitive cardiac data and to serve as a pretext task in self-supervised learning. Ye…

• ECG Mirage: Revealing and Mitigating the Underutilisation of ECGs in Vision-Language Models for Clinical Prediction

  Jinning Liang, Mingcheng Zhu, Tingting Zhu · 21 de septiembre de 2026

  Emergency department (ED) decision-making relies on heterogeneous clinical information, including patient history, vital signs, laboratory results, and electrocardiograms (ECGs). Vision--language models (VLMs) can jointly process these modalities, but strong predictive performance does not necessari…

• Decoder Design Matters for ECG Delineation

  Joseph Scharpf, William Han, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, Ding Zhao · 16 de septiembre de 2026

  Electrocardiogram (ECG) delineation identifies the boundaries of P waves, QRS complexes, and T waves, providing structural annotations that can guide AI models in learning to interpret ECGs. However, training accurate delineation models requires manual annotations that are scarce and time-consuming …

• On the role of the tokenizer in ECG transformer models

  Jiawei Li, Fabio Bonassi, Johan Sundstr\"om, Thomas B. Sch\"on, Ant\^onio H. Ribeiro · 16 de septiembre de 2026

  Tokenization determines both the physiological content presented to an ECG Transformer and the sequence over which attention operates. We compare eight tokenization strategies across Transformer, Informer, Reformer, and FEDformer on the nine-label CPSC2018 classification task. The input projection a…

• Certified AI Triage of ICU Alarms

  Mohammed Sameer Syed, Rozhin Yasaei · 14 de septiembre de 2026

  In the VTaC benchmark 71% of ventricular-tachycardia alarms are false, but silencing a real one can delay recognition of a dangerous arrhythmia. We reframe alarm reduction as three-way triage (retain, suppress, or defer) and bound the decision this analysis treats as harmful: among suppressed alarms…

• Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings

  Laura Alvarez-Florez, Daniel Uyterlinde, Samuel Ruip\'erez-Campillo, Lukas P. A. Arts, Folkert W. Asselbergs, Fleur V. Y. Tjong · 9 de septiembre de 2026

  Chagas disease is a major cause of cardiomyopathy in Latin America. Cardiac magnetic resonance (CMR) imaging can characterize its structural abnormalities, but scanners and expert readers remain scarce in endemic regions. Electrocardiography (ECG) is inexpensive and widely available, yet structural …

• Learning from VAE Errors to support ECG-based Differential Diagnosis of Myocardial Scar

  Shayan Sharifi, Riccardo Treu, Ilaria Gandin, Federico Garoia, Marco Merlo, Giulia Cisotto · 7 de septiembre de 2026

  Late Gadolinium Enhancement (LGE) on cardiac magnetic resonance is a key marker of myocardial scar, but its limited accessibility motivates routine ECG-based screening. We evaluated whether $\beta$-variational autoencoder (VAE)-derived ECG representations can discriminate LGE+ from LGE- cardiomyopat…

• ECGQuest: Benchmarking and Fine-Tuning Language Models for Electrocardiography

  Mohammadsina Hassannia, Matthew A. Reyna, Reza Sameni · 1 de septiembre de 2026

  Electrocardiogram (ECG) interpretation requires knowledge of cardiology, electrophysiology, clinical diagnosis, ECG waveforms, signal acquisition, and instrumentation. Existing language-model benchmarks, however, primarily assess broad medical knowledge or interpretation of individual ECG signals an…

• CardioFusion-AI: Robust ECG--PPG Fusion for Multimodal Physiological Monitoring Under Signal Degradation

  Navaneetha Krishnan Kamalakannan, Janakiraman Kamalakannan · 27 de agosto de 2026

  Wearable electrocardiogram (ECG) and photoplethysmogram (PPG) sensors are complementary but individually fragile: motion artifact, poor contact, and sensor dropout can degrade one or both signals. Fusion strategies that assume both modalities are equally trustworthy can become less reliable than a s…

• Holtercare-Bench: A Multimodal Benchmark for Evaluating Long-Term Dynamic ECG Analysis

  Yihan Xie, Hanwen Cui, Runze Ye, Juekai Lin, Haoyang Wang, Jinhao Mao, Bo Zhang, Wenqiao Zhang, Xiaogang Guo, Jun Xiao, Lei Zhang · 21 de agosto de 2026

  While multimodal large language models (MLLMs) excel in medical applications, most of them favor static images or short-term signals. In the critical field of dynamic electrocardiograms (ECG), models struggle with complex temporal reasoning and diagnostic report generation due to a lack of high-qual…