Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
PASSIO Laboratory, North Carolina A&T State University（北卡罗来纳阿塔州立大学PASSIO实验室）；Department of Computer Science, University of Manitoba（曼尼托巴大学计算机科学系）；Harvard T.H. Chan School of Public Health, Harvard University（哈佛大学T.H. Chan公共卫生学院）；Department of Medicine, Jordan University of Science and Technology（约旦科学与技术大学医学院）；Department of Biomedical Engineering, Duke University（达特茅斯大学生物医学工程系）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝塞斯达以色列德acons医疗中心）
Duc N. Do, Minh N. Do, Dang Nguyen, Khanh T. Q. Le, Khoa D. Pham, Hung N. Huynh, Phi Pham-Van-Hoang, Quan K. Huynh, Ramez M. Odat, Perisa Ashar, Ethan Philip Lo… 展开作者
Duc N. Do, Minh N. Do, Dang Nguyen, Khanh T. Q. Le, Khoa D. Pham, Hung N. Huynh, Phi Pham-Van-Hoang, Quan K. Huynh, Ramez M. Odat, Perisa Ashar, Ethan Philip Lowder, Minh H. N. Le, Hoang Le, Phat V. H. Nguyen, Quan Le, Jacques Kpodonu, Phat K. Huynh
Transthoracic echocardiography is the reference standard for confirming structural heart disease (SHD), but first-line screening is limited by cost, workflow burden, and specialist availability. We evaluated whether open pretrained electrocardiogram (ECG) foundation models can support echo-confirmed multi-label SHD detection using the public EchoNext Mini-Model benchmark. Six echocardiography-derived abnormalities were targeted: reduced left ventricular ejection fraction, increased left ventricular wall thickness, aortic stenosis, mitral regurgitation, tricuspid regurgitation, and right ventricular systolic dysfunction. Under a common pipeline, we compared engineered ECG features with gradient boosting, end-to-end waveform learning from scratch, and transfer from open ECG foundation models. We then applied in-domain self-supervised adaptation of an ECG foundation model (ECG-FM) on EchoNext waveforms followed by selective supervised fine-tuning, and evaluated trade-offs between discrimination and adaptation cost. Adapted ECG-FM models achieved the best overall performance: peak macro-AUROC 0.8509 and macro-AUPRC 0.4297, while a parameter-efficient operating point preserved AUROC (0.8501) and attained the highest fixed-threshold macro-F1 0.3691. Late fusion with covariates did not improve threshold-independent discrimination, and evaluated LoRA, alternative backbones, and mixture-of-foundations strategies did not surpass the best adapted single-backbone models. These results indicate that for ECG-based case finding and echocardiography triage, combining target-domain self-supervised adaptation with selective supervised updating of a pretrained ECG backbone is the most effective transfer strategy.
Electrocardiogram (ECG) is a simple non-invasive measure to identify heart-related issues such as irregular heartbeats known as arrhythmias. While artificial intelligence and machine learning is being utilized in a wide range of healthcare related applications and datasets, many arrhythmia classifiers using deep learning methods have been proposed in recent years. However, sizes of the available datasets from which to build and assess machine learning models is often very small and the lack of well-annotated public ECG datasets is evident. In this paper, we propose a deep transfer learning fra…
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…
A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院查尔斯·布朗夫曼个性化医学研究所）；Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山临床智能中心）；Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院遗传学与基因组科学系）；The Hasso Plattner Institute for Digital Health at Mount Sinai（西奈山哈索·普拉特纳数字健康研究所）；Department of Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院医学系）；Mount Sinai Heart, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院西奈山心脏中心）；Department of Cardiology, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院心脏病学系）；Department of Medicine, NYU Langone Health（纽约大学朗格尼健康中心医学系）；The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院帕梅拉·斯克拉精神病基因组学部门）；Department of Psychiatry, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院精神病学系）；Department of Biomedical Engineering, Tel Aviv University（特拉维夫大学生物医学工程系）；Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院医学系肾脏病科）
The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction…
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）
Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions. We propose heartbeat feature classification based on a novel sparse representation using time-frequency joint distribution of ECG. Fundamental to this is a multi-layer perceptron, which incorporates these signatures to detect cardiac arrhythmia. This approach is validated with ECG data from MIT-BIH arrhythmia database. Results show that our approach has an average 95.7% accuracy, an improvement of 22% over state-of-the-art approaches. Additionally, ECG sparse distributed r…
Technion - Israel Institute of Technology（以色列理工学院）；Google Research（谷歌研究院）
Generating training examples for supervised tasks is a long sought after goal in AI. We study the problem of heart signal electrocardiogram (ECG) synthesis for improved heartbeat classification. ECG synthesis is challenging: the generation of training examples for such biological-physiological systems is not straightforward, due to their dynamic nature in which the various parts of the system interact in complex ways. However, an understanding of these dynamics has been developed for years in the form of mathematical process simulators. We study how to incorporate this knowledge into the gener…
HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
Faculty of Computer Engineering, K. N. Toosi University of Technology（法尔科夫计算机工程学院，卡·努·托西技术大学）；School of Electrical and Computer Engineering, University of Tehran（电气与计算机工程学院，德黑兰大学）
Personalized Heart Disease Detection via ECG Digital Twin Generation
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）；Guangdong Provincial People’s Hospital, Southern Medical University（南方医科大学广东省人民医院）；Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院广东省心血管病研究所、广东省医学科学院）；School of Public Health, Zhejiang University（浙江大学公共卫生学院）
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature extraction and classification and thus employ end to end deep learning. We perfo…
University of New South Wales（新南威尔士大学）；Harvard University（哈佛大学）
Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning. However, traditional machine learning models require large investment of time and effort for raw data preprocessing and feature extraction, as well as challenged by poor classification performance. Here, we propose a novel deep learning model, named Attention-Based Convolutional Neural Networks (ABCNN) that taking advantage of CNN and multi-head attention, to directly work on the raw ECG sig…
Arrhythmia is a cardiovascular disease that manifests irregular heartbeats. In arrhythmia detection, the electrocardiogram (ECG) signal is an important diagnostic technique. However, manually evaluating ECG signals is a complicated and time-consuming task. With the application of convolutional neural networks (CNNs), the evaluation process has been accelerated and the performance is improved. It is noteworthy that the performance of CNNs heavily depends on their architecture design, which is a complex process grounded on expert experience and trial-and-error. In this paper, we propose a novel …
Swiss Center for Electronics and Microtechnology (CSEM)（瑞士电子与微技术中心）
While most heart arrhythmias are not immediately harmful, they can lead to severe complications. In particular, atrial fibrillation, the most common arrhythmia, is characterized by fast and irregular heart beats and increases the risk of suffering a stroke. To detect such abnormal heart conditions, we propose a system composed of two main parts: a smart vest with two cooperative sensors to collect ECG data and a neural network architecture to classify heart rhythms. The smart vest uses two dry bi-electrodes to record a single lead ECG signal. The biopotential signal is then streamed via a gate…
ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening
ETH Zürich（苏黎世联邦理工学院）；Swiss Paraplegic Research（瑞士截瘫研究中心）；University of Campania “Luigi Vanvitelli”（坎帕尼亚“路易吉·万维泰利”大学）；University of Italian Switzerland, Cardiocentro Ticino Institute（意大利瑞士大学提契诺心脏中心研究所）
Heart disease is the number one killer, and ECGs can assist in the early diagnosis and prevention of deadly outcomes. Accurate ECG interpretation is critical in detecting heart diseases; however, they are often misinterpreted due to a lack of training or insufficient time spent to detect minute anomalies. Subsequently, researchers turned to machine learning to assist in the analysis. However, existing systems are not as accurate as skilled ECG readers, and black-box approaches to providing diagnosis result in a lack of trust by medical personnel in a given diagnosis. To address these issues, w…
The heart is one of the most vital organs in the human body. It supplies blood and nutrients in other parts of the body. Therefore, maintaining a healthy heart is essential. As a heart disorder, arrhythmia is a condition in which the heart's pumping mechanism becomes aberrant. The Electrocardiogram is used to analyze the arrhythmia problem from the ECG signals because of its fewer difficulties and cheapness. The heart peaks shown in the ECG graph are used to detect heart diseases, and the R peak is used to analyze arrhythmia disease. Arrhythmia is grouped into two groups - Tachycardia and Brad…
School of Electrical and Electronics Engineering（电气与电子工程学院）；University of Manchester（曼彻斯特大学）
Effective and powerful methods for denoising real electrocardiogram (ECG) signals are important for wearable sensors and devices. Deep Learning (DL) models have been used extensively in image processing and other domains with great success but only very recently have been used in processing ECG signals. This paper presents several DL models namely Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Restricted Boltzmann Machine (RBM) together with the more conventional filtering methods (low pass filtering, high pass filtering, Notch filtering) and the standard wavelet-based te…
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…
Predicting Cardiopulmonary Exercise Testing Outcomes in Congenital Heart Disease Through Multi-modal Data Integration and Geometric Learning
School of Computing Science at University of Glasgow（格拉斯哥大学计算科学学院）；Golden Jubilee National Hospital（金禧国家医院）；Helen DeVos Children’s Hospital, Corewell Health（科尔韦尔健康海伦·德沃斯儿童医院）
Atrial fibrillation(termed as AF/Afib henceforth) is a discrete and often rapid heart rhythm that can lead to clots near the heart. We can detect Afib by ECG signal by the absence of p and inconsistent intervals between R waves as shown in fig(1). Existing methods revolve around CNN that are used to detect afib but most of them work with 12 point lead ECG data where in our case the health gauge watch deals with single-point ECG data. Twelve-point lead ECG data is more accurate than a single point. Furthermore, the health gauge watch data is much noisier. Implementing a model to detect Afib for…
Heart disease is one of the significant challenges in today's world and one of the leading causes of many deaths worldwide. Recent advancement of machine learning (ML) application demonstrates that using electrocardiogram (ECG) and patient data, detecting heart disease during the early stage is feasible. However, both ECG and patient data are often imbalanced, which ultimately raises a challenge for the traditional ML to perform unbiasedly. Over the years, several data level and algorithm level solutions have been exposed by many researchers and practitioners. To provide a broader view of the …
University of Alberta（阿尔伯塔大学）；Canadian VIGOUR Centre（加拿大VIGOUR中心）；Alberta Machine Intelligence Institute（阿尔伯塔机器智能研究所）
Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient's first in-hospital …
The University of Tennessee（田纳西大学）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）
Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is of critical importance for timely medical treatment to save patients' lives. Routine use of electrocardiogram (ECG) is the most common method for physicians to assess the electrical activities of the heart and detect possible abnormal cardiac conditions. Fully utilizing the ECG data for reliable heart disease detection depends on developing effective analytical models. In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for automatic diag…
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
Walkky（沃基公司）；Chobanian & Avedisian School of Medicine, Boston University（波士顿大学乔巴尼亚与阿夫迪西安医学院）；Clemson University（克莱姆森大学）；Northwell Health Physician Partners（诺斯韦尔健康医师伙伴）；Lenox Hill Hospital（莱诺克斯山医院）
Zhuhai College of Jilin University（吉林大学珠海学院）；College of Communication Engineering（通信工程学院）；Jilin University（吉林大学）
Recently, the Bag-Of-Word (BOW) algorithm provides efficient features and promotes the accuracy of the ECG classification system. However, BOW algorithm has two shortcomings: (1). it has large quantization errors and poor reconstruction performance; (2). it loses heart beat's time information, and may provide confusing features for different kinds of heart beats. Furthermore, ECG classification system can be used for long time monitoring and analysis of cardiovascular patients, while a huge amount of data will be produced, so we urgently need an efficient compression algorithm. In view of the …
Medical diagnoses can shape and change the life of a person drastically. Therefore, it is always best advised to collect as much evidence as possible to be certain about the diagnosis. Unfortunately, in the case of the Brugada Syndrome (BrS), a rare and inherited heart disease, only one diagnostic criterion exists, namely, a typical pattern in the Electrocardiogram (ECG). In the following treatise, we question whether the investigation of ECG strips by the means of machine learning methods improves the detection of BrS positive cases and hence, the diagnostic process. We propose a pipeline tha…
Ingenuity Labs Research Institute（Ingenuity Labs 研究院）
Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an advers…
Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses
State Key Laboratory of Digital Medical Engineering, Southeast University（数字医疗工程国家重点实验室，东南大学）；School of Instrument Science and Engineering, Southeast University（仪器科学与工程学院，东南大学）；School of Computer Science and Engineering, Southeast University（计算机科学与工程学院，东南大学）
Many people are currently suffering from heart diseases that can lead to untimely death. The most common heart abnormality is arrhythmia, which is simply irregular beating of the heart. A prediction system for the early intervention and prevention of heart diseases, including cardiovascular diseases (CDVs) and arrhythmia, is important. This paper introduces the classification of electrocardiogram (ECG) heartbeats into normal or abnormal. The approach is based on the combination of swarm optimization algorithms with a modified PannTompkins algorithm (MPTA) and support vector machines (SVMs). Th…
The recent developments in wearable devices and the Internet of Medical Things (IoMT) allow real-time monitoring and recording of electrocardiogram (ECG) signals. However, continuous monitoring of ECG signals is challenging in low-power wearable devices due to energy and memory constraints. Therefore, in this paper, we present a novel and energy-efficient methodology for continuously monitoring the heart for low-power wearable devices. The proposed methodology is composed of three different layers: 1) a Noise/Artifact detection layer to grade the quality of the ECG signals; 2) a Normal/Abnorma…
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）
Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford（生物医学工程研究所，工程科学系，牛津大学）；Imperial College London（伦敦帝国学院）；University of Sheffield（谢菲尔德大学）；Novo Nordisk Research Centre Oxford (NNRCO)（牛津诺和硕研究中心（NNRCO））；Royal Society（皇家学会）
Stichting IMEC Nederland（IMEC荷兰基金会）；Eindhoven University of Technology（埃因霍温理工大学）；IMEC Leuven（IMEC鲁汶）；University of California, Irvine（加利福尼亚大学尔湾分校）
Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a sub…
RCE；East Atlanta Cardiology（东亚特兰大心脏病学中心）；Northside Hospital Gwinnett（格温内特北区医院）
Coronary artery disease(CAD) is the most common type of heart disease and the leading cause of death worldwide[1]. A progressive state of this disease marked by plaque rupture and clot formation in the coronary arteries, also known as an acute coronary syndrome (ACS), is a condition of the heart associated with sudden, reduced blood flow caused due to partial or full occlusion of coronary vasculature that normally perfuses the myocardium and nerve bundles, compromising the proper functioning of the heart. Often manifesting with pain or tightness in the chest as the second most common cause of …
The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and coronary angiography, an invasive tool, while could be probably identified in a non-invasive way by using parameters extracted from…
SHDB-AF: a Japanese Holter ECG database of atrial fibrillation
Teikyo University School of Medicine（帝京大学医学部）；Saitama Medical University International Medical Center（埼玉医科大学国际医疗中心）；Technion-IIT（以色列理工学院）；Centre hospitalier Universitaire de Nancy（南锡大学医院）；INSERM（法国国家健康与医学研究院）；Universite de Lorraine（洛林大学）
