Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a burning research topic in biomedical signal processing. However, existing literature provides only binary assessment of stress, while multiple levels of assessment may be more beneficial for healthcare applications. Furthermore, in present research, ECG signal for stress analysis is examined independently in spatial domain or in transform domains but the advantage of fusing these domains has not been fully utilized. To get the maximum advantage of fusing diferent domains, we introduce a dataset with multiple stress levels and then classify these levels using a novel deep learning approach by converting ECG signal into signal images based on R-R peaks without any feature extraction. Moreover, We made signal images multimodal and multidomain by converting them into time-frequency and frequency domain using Gabor wavelet transform (GWT) and Discrete Fourier Transform (DFT) respectively. Convolutional Neural networks (CNNs) are used to extract features from different modalities and then decision level fusion is performed for improving the classification accuracy. The experimental results on an in-house dataset collected with 15 users show that with proposed fusion framework and using ECG signal to image conversion, we reach an average accuracy of 85.45%.
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Physikalisch-Technische Bundesanstalt（联邦物理技术研究院）；Technical University Berlin（柏林工业大学）；King’s College London（伦敦国王学院）
Electrocardiography is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by automatic interpretation algorithms. The progress in the field of automatic ECG interpretation has up to now been hampered by a lack of appropriate datasets for training as well as a lack of well-defined evaluation procedures to ensure comparability of different algorithms. To alleviate these issues, we put forward first benchmarking results for the recently published, freely accessible PTB-XL dataset, covering a variety of tasks from different ECG statement prediction ta…
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）
Fatigue is one of the key factors in the loss of work efficiency and health-related quality of life, and most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, we developed an automated system using wearable sensing and machine learning techniques for objective fatigue assessment. ECG/Actigraphy data were collected from subjects in free-living environments. Preprocessing and feature engineering methods were applied, before interpretable solution and deep learning solution were introduced. Specifically, for in…
Multimodal Latent Fusion of ECG Leads for Early Assessment of Pulmonary Hypertension
School of Computer Science, University of Sheffield（谢菲尔德大学计算机科学学院）；Centre for Machine Intelligence, University of Sheffield（谢菲尔德大学智能中心）；Department of Electrical & Computer Science Engineering, IITRAM Ahmedabad（印度阿赫迈德亚布德IITRAM电子与计算机科学工程系）；Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学数字环境研究院）；Nepal Applied Mathematics and Informatics Institute for research (NAAMII), Nepal（尼泊尔应用数学与信息技术研究所）；Department of Computing, Imperial College London（伦敦帝国学院计算机系）；School of Medicine and Population Health, University of Sheffield（谢菲尔德大学医学与人口健康学院）；Department of Clinical Radiology, Sheffield Teaching Hospitals（谢菲尔德教学医院放射科）；National Institute for Health and Care Research (NIHR), Sheffield Biomedical Research Centre（英国国家健康与护理研究所（NIHR）谢菲尔德生物医学研究中心）
Dual-Phase Cross-Modal Contrastive Learning for CMR-Guided ECG Representations for Cardiovascular Disease Assessment
Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；ETH Zurich（苏黎世联邦理工学院）；Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center（放射医学与核医学系，阿姆斯特丹大学医学中心）；Department of Radiology, Mayo Clinic, Rochester（放射医学系，梅奥诊所，罗切斯特）
ECGrecover: a Deep Learning Approach for Electrocardiogram Signal Completion
IRD（法国发展研究院）；Sorbonne Université（索邦大学）；University Paris-Saclay（巴黎-萨克雷大学）；INSERM（法国国家健康与医学研究院）；Pitié-Salpêtrière University Hospital（皮蒂-萨尔皮特里大学医院）；Vanderbilt University Medical Center（范德堡大学医学中心）；AP-HP（巴黎公共医疗集团）；Hôpital Pitié-Salpêtrière（皮蒂-萨尔皮特里医院）
Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Vascular Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院血管外科）；Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院心内科）；Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院放射科）；Department of Vascular Surgery, Quzhou People’s Hospital（衢州人民医院血管外科）；Department of Cardiology, The Second Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第二医院心内科）；China Ship Scientific Research Center（中国船舶科学研究院）；Guangdong Transtek Medical Electronics Co., Ltd.（广东 Transtek 医疗电子有限公司）
MELEP: A Novel Predictive Measure of Transferability in Multi-Label ECG Diagnosis
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；VinUni-Illinois Smart Health Center, VinUniversity（VinUniversity VinUni-伊利诺伊智能健康中心）
GReAT: leveraging geometric artery data to improve wall shear stress assessment
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Erasmus Medical Center（埃因霍温医学中心）；University of Twente（埃因霍温大学）；Imperial College London（伦敦帝国理工学院）
Opportunistic Cardiac Health Assessment: Estimating Phenotypes from Localizer MRI through Multi-Modal Representations
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital（人工智能在医疗与健康中的研究所，慕尼黑技术大学（TUM）和TUM大学医院）；Department of Diagnostic and Interventional Neuroradiology, School of Medicine, TUM University Hospital（诊断与介入神经放射科，医学院，TUM大学医院）；Department of Computing, Imperial College London（计算学院，伦敦帝国学院）；Munich Center for Machine Learning (MCML), Germany（慕尼黑机器学习中心（MCML），德国）
University of Birjand（比尔詹德大学）；Mazandaran University of Science and Technology（马赞达兰科技大学）；University of Qom（库姆大学）；University of Genova（热那亚大学）；Deakin University（迪肯大学）；Bu-Ali Sina University（布-阿里·西纳大学）；Maragheh Faculty of Medical Sciences（马拉盖医学院）；Obuda University（奥布达大学）；Singapore University of Social Sciences（新加坡社会科学大学）；National Heart Centre Singapore（新加坡国家心脏中心）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）
Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other bio…
