Deep learning has achieved strong performance for electrocardiogram (ECG) classification within individual datasets, yet dependable generalization across heterogeneous acquisition settings remains a major obstacle to clinical deployment and longitudinal monitoring. A key limitation of many model architectures is the implicit entanglement of morphological waveform patterns and rhythm dynamics, which can promote shortcut learning and amplify sensitivity to distribution shifts. We propose ECG-RAMBA, a framework that separates morphology and rhythm and then re-integrates them through context-aware fusion. ECG-RAMBA combines: (i) deterministic morphological features extracted by MiniRocket, (ii) global rhythm descriptors computed from heart-rate variability (HRV), and (iii) long-range contextual modeling via a bi-directional Mamba backbone. To improve sensitivity to transient abnormalities under windowed inference, we introduce a numerically stable Power Mean pooling operator ($Q=3$) that emphasizes high-evidence segments while avoiding the brittleness of max pooling and the dilution of averaging. We evaluate under a protocol-faithful setting with subject-level cross-validation, a fixed decision threshold, and no test-time adaptation. On the Chapman--Shaoxing dataset, ECG-RAMBA achieves a macro ROC-AUC $\approx 0.85$. In zero-shot transfer, it attains PR-AUC $=0.708$ for atrial fibrillation detection on the external CPSC-2021 dataset, substantially outperforming a comparable raw-signal Mamba baseline, and shows consistent cross-dataset performance on PTB-XL. Ablation studies indicate that deterministic morphology provides a strong foundation, while explicit rhythm modeling and long-range context are critical drivers of cross-domain robustness.
LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification
School of Mechano-Electronic Engineering Xidian University Xi'an, China（机械电子工程学院 西安电子科技大学 西安中国）；School of Cyber Engineering Xidian University Xi'an, China（网络工程学院 西安电子科技大学 西安中国）；DCST, BNRist, RIIT, Institute of Internet Industry Tsinghua University Beijing, China（DCST、BNRist、RIIT、互联网产业研究院 清华大学 北京中国）
University of Moratuwa（莫拉图瓦大学）；Lady Ridgeway Hospital for Children（里奇韦夫人儿童医院）
Diagnosing pre-existing heart diseases early in life is important as it helps prevent complications such as pulmonary hypertension, heart rhythm problems, blood clots, heart failure and sudden cardiac arrest. To identify such diseases, phonocardiogram (PCG) and electrocardiogram (ECG) waveforms convey important information. Therefore, effectively using these two modalities of data has the potential to improve the disease screening process. We evaluate this hypothesis on a subset of the PhysioNet Challenge 2016 Dataset which contains simultaneously acquired PCG and ECG recordings. Our novel Dua…
Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；State Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；School of Artificial Intelligence, South China Normal University（华南师范大学人工智能学院）
Ufa State Aviation Technical University（乌法国立航空技术大学）；Bashkir State Pedagogical University（巴什基尔国立师范大学）；Ural Federal University（乌拉尔联邦大学）；Institute of Immunology and Physiology UrB RAS（俄罗斯科学院乌拉尔分院免疫学与生理学研究所）
Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effec…
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Rollins School of Public Health, Emory University（埃默里大学罗林斯公共卫生学院）；Georgia Institute of Technology & Emory University（佐治亚理工学院与埃默里大学）
Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morphologies and abnormalities specific to a given condition - T-wave Alternans (TWA) as a result of Post-Traumatic Stress Disorder, or PTSD - and significantly boost performance on a small database of rare individuals. Approach: Using a previously validated artificial ECG model, we generated 180,000 artificial ECGs with or without significant TWA, with varying heart rate, breathing rate, TWA amplitude, and ECG morphology…
AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings
The College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Antai College of Economics and Management, Shanghai Jiao Tong University（上海交通大学安泰经济与管理学院）；Information Hub, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息枢纽）；the School of Public Health and Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院公共卫生学院及第二附属医院）
Hangzhou Dianzi University（杭州电子科技大学）；University of Science and Technology of China（中国科学技术大学）
Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment. Classifying abnormal rhythms into exact categories is very challenging due to the broad taxonomy of rhythms, noises and lack of large-scale real-world annotated data. Different from previous methods that utilize hand-crafted features or learn features from the original signal domain, we propose a novel ECG classification method by learning deep time-frequency representation and progressive decision fusion at different temporal…
A Novel end-to-end Digital Health System Using Deep Learning-based ECG Analysis
KIOS Research and Innovation Center of Excellence (KIOS CoE)（KIOS研究与创新中心（KIOS CoE））；University of Cyprus（塞浦路斯大学）；MEDTL Medical Technologies Ltd.（MEDTL医疗技术有限公司）；Department of Computer Science, University of Cyprus（塞浦路斯大学计算机科学系）
National Heart, Lung, and Blood Institute（美国国家心肺血液研究所）
The electrocardiogram (ECG) is routinely used in hospitals to analyze cardiovascular status and health of an individual. Abnormal heart rhythms can be a precursor to more serious conditions including sudden cardiac death. Classifying abnormal rhythms is a laborious process prone to error. Therefore, tools that perform automated classification with high accuracy are highly desirable. The work presented classifies five different types of ECG arrhythmia based on AAMI EC57 standard and using the MIT-BIH data set. These include non-ectopic (normal), supraventricular, ventricular, fusion, and unknow…
University of Isfahan（伊斯法罕大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；New Jersey Institute of Technology（新泽西理工学院）；Rutgers University（罗格斯大学）
Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to heart stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper in…
Beat-ssl: Capturing Local ECG Morphology through Heartbeat-level Contrastive Learning with Soft Targets
School of Computing Science, University of Glasgow, UK（Glasgow大学计算机科学学院）；Research Center for Smart Mechatronics, National Research and Innovation Agency, Indonesia（智能机电研究中心，国家研究与创新局）；School of Health & Wellbeing, University of Glasgow, UK（Glasgow大学健康与福祉学院）；Department of Information Technologies, University of Limassol, Cyprus（拉马索尔大学信息科技系）
Label-Efficient Deep Learning for ECG Delineation: A Multi-Dataset Benchmark against Widely Used Delineation Tools
VUNO Inc.（VUNO公司）；Ajou University（亚洲大学）；C&Thoth Co., Ltd.（C&Thoth有限公司）；Chonnam National University Graduate School（全南大学研究生院）；Chonnam National University Hospital（全南大学医院）；Chonnam National University Medical School（全南大学医学院）
Peking University（北京大学）；Georgia Institute of Technology（佐治亚理工学院）；IQVIA（艾昆纬）；IBM Research（IBM研究院）
Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however they are mostly black-box, thus lack interpretability needed for clinical usage. In this work, we propose MultIlevel kNowledge-guided Attention networks (MINA) that predict heart diseases from ECG signals with intuitive explanation aligned with medical knowledge. By extracting multilevel (beat-, rhythm- and frequency-level) domain knowledge features separately, MINA combines the medical knowledge and ECG data via a mu…
With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for the continuous monitoring of patients at risk. We utilise an annotated dataset of 12,186 single-lead ECG recordings to build a diverse ensemble of recurrent neural networks (RNNs) that is able to distinguish between normal sinus rhythms, atrial fibrillation, other types of arrhythmia and signals that are too noisy to interpret. In order to ease learning over the temporal dimension, we introduce a novel task formulation…
ECG-Expert-QA: A Benchmark for Evaluating Medical Large Language Models in Heart Disease Diagnosis
Zhejiang University School of Medicine（浙江大学医学院）；The First Affiliated Hospital（第一附属医院）；Liangzhu Laboratory（良渚实验室）；Shandong Jianzhu University（山东建筑大学）；FUXI AI Lab（伏羲人工智能实验室）；Beijing Normal University（北京师范大学）；Southern University of Science and Technology（南方科技大学）；China University of Geosciences (Wuhan)（中国地质大学（武汉））；Hangzhou Baorun Biotechnology Co., Ltd.（杭州宝润生物科技有限公司）
Swiss Center for Electronics and Microtechnology (CSEM)（瑞士电子与微技术中心）
Except for a few specific types, cardiac arrhythmias are not immediately life-threatening. However, if not treated appropriately, they can cause serious complications. In particular, atrial fibrillation, which is characterized by fast and irregular heart beats, increases the risk of stroke. We propose three neural network architectures to detect abnormal rhythms from single-lead ECG signals. These architectures combine convolutional layers to extract high-level features pertinent for arrhythmia detection from sliding windows and recurrent layers to aggregate these features over signals of vary…
Angular Gaussian Supervised Contrastive Learning for Long-Tailed Electrocardiogram Arrhythmia Diagnosis
School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）；Department of Neurology and Neurological Rehabilitation, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University（同济大学医学院附属上海养志康复医院神经科与神经康复科）
Institut für Medizinische Informatik der Universität zu Lübeck（吕贝克大学医学信息学研究所）；Universität zu Lübeck（吕贝克大学）；INSERM（法国国家健康与医学研究院）
For the weakly supervised task of electrocardiogram (ECG) rhythm classification, convolutional neural networks (CNNs) and long short-term memory (LSTM) networks are two increasingly popular classification models. This work investigates whether a combination of both architectures to so-called convolutional long short-term memory (ConvLSTM) networks can improve classification performances by explicitly capturing morphological as well as temporal features of raw ECG records. In addition, various attention mechanisms are studied to localize and visualize record sections of abnormal morphology and …
Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder associated with deadly and debilitating consequences including heart failure, stroke, poor mental health, reduced quality of life and death. Having an automatic system that diagnoses various types of cardiac arrhythmias would assist cardiologists to initiate appropriate preventive measures and to improve the analysis of cardiac disease. To this end, this paper introduces a new approach to detect and classify automatically cardiac arrhythmias in electrocardiograms (ECG) recordings. Methods: The proposed approach used a combi…
School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Peng Cheng Laboratory（鹏城实验室）；School of Physics and Astronomy, University of Manchester（曼彻斯特大学物理与天文学院）；Northeastern University（东北大学）
Supervised deep learning has been widely used in the studies of automatic ECG classification, which largely benefits from sufficient annotation of large datasets. However, most of the existing large ECG datasets are roughly annotated, so the classification model trained on them can only detect the existence of abnormalities in a whole recording, but cannot determine their exact occurrence time. In addition, it may take huge time and economic cost to construct a fine-annotated ECG dataset. Therefore, this study proposes weakly supervised deep learning models for detecting abnormal ECG events an…
