We train an enhanced deep convolutional neural network in order to identify eight cardiac abnormalities from the standard 12-lead electrocardiograms (ECGs) using the dataset of 14000 ECGs. Instead of straightforwardly applying an end-to-end deep learning approach, we find that deep convolutional neural networks enhanced with sophisticated hand crafted features show advantages in reducing generalization errors. Additionally, data preprocessing and augmentation are essential since the distribution of eight cardiac abnormalities are highly biased in the given dataset. Our approach achieves promising generalization performance in the First China ECG Intelligent Competition; an empirical evaluation is also provided to validate the efficacy of our design on the competition ECG dataset.
Zhejiang University（浙江大学）；RealDoctor AI Research Centre（RealDoctor人工智能研究中心）；Shanghai Polytechnic University（上海第二工业大学）；University of Notre Dame（圣母大学）
A large number of people suffer from life-threatening cardiac abnormalities, and electrocardiogram (ECG) analysis is beneficial to determining whether an individual is at risk of such abnormalities. Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis and help early prevention of cardiovascular diseases. However, the predictions of the known neural networks still do not satisfactorily meet the needs of clinicians, and this phenomenon suggests that…
Electrocardiogram (ECG) signals are beneficial in diagnosing cardiovascular diseases, which are one of the leading causes of death. However, they are often contaminated by noise artifacts and affect the automatic and manual diagnosis process. Automatic deep learning-based examination of ECG signals can lead to inaccurate diagnosis, and manual analysis involves rejection of noisy ECG samples by clinicians, which might cost extra time. To address this limitation, we present a two-stage deep learning-based framework to automatically detect the noisy ECG samples. Through extensive experiments and …
Stanford University（斯坦福大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；Harvard Medical School（哈佛医学院）
We propose 3KG, a physiologically-inspired contrastive learning approach that generates views using 3D augmentations of the 12-lead electrocardiogram. We evaluate representation quality by fine-tuning a linear layer for the downstream task of 23-class diagnosis on the PhysioNet 2020 challenge training data and find that 3KG achieves a $9.1\%$ increase in mean AUC over the best self-supervised baseline when trained on $1\%$ of labeled data. Our empirical analysis shows that combining spatial and temporal augmentations produces the strongest representations. In addition, we investigate the effec…
University of Alberta（阿尔伯塔大学）；Canadian VIGOUR Centre（加拿大活力中心）
The 12-lead electrocardiogram (ECG) is a commonly used tool for detecting cardiac abnormalities such as atrial fibrillation, blocks, and irregular complexes. For the PhysioNet/CinC 2020 Challenge, we built an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis. For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal waveform. We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to ea…
We report Lobachevsky University Database (LUDB) of ECG signals, an open tool for validating ECG delineation algorithms, that is superior to the existing publicly available data bases in several aspects. LUDB contains 200 recordings of 10-second 12-lead electrocardiograms (ECG) from different subjects, representative of a variety of signal morphologies. The boundaries and peaks of QRS complexes and P and T waves are manually annotated by cardiologists for all recordings and independently for each lead, and all records received an expert classification by abnormalities. We present a case study …
HeartVoice Medical Technology（心音医疗科技）；University of Science and Technology of China（中国科学技术大学）
Electrocardiogram (ECG) is one of the most convenient and non-invasive tools for monitoring peoples' heart condition, which can use for diagnosing a wide range of heart diseases, including Cardiac Arrhythmia, Acute Coronary Syndrome, et al. However, traditional ECG disease detection models show substantial rates of misdiagnosis due to the limitations of the abilities of extracted features. Recent deep learning methods have shown significant advantages, but they do not provide publicly available services for those who have no training data or computational resources. In this paper, we demonstra…
School of Computer Science and Technology, Harbin Institute of Technology (HIT)（哈尔滨工业大学计算机科学与技术学院）；School of Physics and Astronomy, The University of Manchester（曼彻斯特大学物理与天文学院）；SPACEnter Space Science and Technology Institute（SPACEnter空间科学与技术研究院）；Northeastern University（东北大学）；Beijing Electric Power Hospital（北京电力医院）
Heart disease is one of the most common diseases causing morbidity and mortality. Electrocardiogram (ECG) has been widely used for diagnosing heart diseases for its simplicity and non-invasive property. Automatic ECG analyzing technologies are expected to reduce human working load and increase diagnostic efficacy. However, there are still some challenges to be addressed for achieving this goal. In this study, we develop an algorithm to identify multiple abnormalities from 12-lead ECG recordings. In the algorithm pipeline, several preprocessing methods are firstly applied on the ECG data for de…
Electrocardiography plays an essential role in diagnosing and screening cardiovascular diseases in daily healthcare. Deep neural networks have shown the potentials to improve the accuracies of arrhythmia detection based on electrocardiograms (ECGs). However, more ECG records with ground truth are needed to promote the development and progression of deep learning techniques in automatic ECG analysis. Here we propose a web-based tool for ECG viewing and annotating, LabelECG. With the facilitation of unified data management, LabelECG is able to distribute large cohorts of ECGs to dozens of techni…
ECGtizer: a fully automated digitizing and signal recovery pipeline for electrocardiograms
IRD（法国发展研究院）；Sorbonne Université（索邦大学）；INSERM（法国国家健康与医学研究院）；Pitié-Salpêtrière University Hospital（皮蒂-萨尔皮特里耶大学医院）；Vanderbilt University Medical Center（范德堡大学医学中心）
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Uppsala University（乌普萨拉大学）；Telehealth Center from Hospital das Clínicas da Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学医院临床中心远程医疗中心）；Hospital das Clínicas da Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学医院临床中心（注：此处原文本中“Telehealth Center from Hospital das Clínicas da Universidade Federal de Minas Gerais”中的“Hospital das Clínicas da Universidade Federal de Minas Gerais”是更基础的机构，按要求提取到大学级别或包含的独立机构，此处直接提取该医院作为机构名））
We present a model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large telehealth network, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consist of 12-lead ECGs recorded during standard i…
Deep Neural Network Architectures for Electrocardiogram Classification: A Comprehensive Evaluation
Independent Researcher（独立研究者）；Department of Materials Engineering, Xi'an Polytechnic University（材料工程系，西安理工大学）；Tandon School of Engineering, New York University（工程学院，纽约大学）；College of Computing, Georgia Institute of Technology（计算学院，佐治亚理工学院）
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 …
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Uppsala University（乌普萨拉大学）；Hospital das Clínicas da Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学临床医院）；Glasgow University（格拉斯哥大学）
The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electroca…
Electrocardiogram (ECG) is the most widely used diagnostic tool to monitor the condition of the human heart. By using deep neural networks (DNNs), interpretation of ECG signals can be fully automated for the identification of potential abnormalities in a patient's heart in a fraction of a second. Studies have shown that given a sufficiently large amount of training data, DNN accuracy for ECG classification could reach human-expert cardiologist level. However, despite of the excellent performance in classification accuracy, DNNs are highly vulnerable to adversarial noises that are subtle change…
EcoScaleNet: A Lightweight Multi Kernel Network for Long Sequence 12 lead ECG Classification
Department of Electrical and Computer Engineering, Inha University, Republic of Korea（电气与计算机工程系，釜山大学，大韩民国）；DeepCardio, Republic of Korea（DeepCardio，大韩民国）
Currently, the telehealth monitoring field has gained huge attention due to its noteworthy use in day-to-day life. This advancement has led to an increase in the data collection of electrophysiological signals. Due to this advancement, electrocardiogram (ECG) signal monitoring has become a leading task in the medical field. ECG plays an important role in the medical field by analysing cardiac physiology and abnormalities. However, these signals are affected due to numerous varieties of noises, such as electrode motion, baseline wander and white noise etc., which affects the diagnosis accuracy.…
Institutes of Biomedical Engineering, Shanghai Jiaotong University（上海交通大学生物医学工程研究所）；Shukun (Beijing) Network Technology Co.,Ltd.（树坤（北京）网络科技有限公司）；Institutes of Medical Robotics, Shanghai Jiaotong University（上海交通大学医疗机器人研究所）
Objective: A novel structure based on channel-wise attention mechanism is presented in this paper. Embedding with the proposed structure, an efficient classification model that accepts multi-lead electrocardiogram (ECG) as input is constructed. Methods: One-dimensional convolutional neural networks (CNN) have proven to be effective in pervasive classification tasks, enabling the automatic extraction of features while classifying targets. We implement the Residual connection and design a structure which can learn the weights from the information contained in different channels in the input feat…
MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network
State Key Laboratory of Transvascular Implantation Devices and TIDRI, Zhejiang University（浙江大学血管介入关键实验室及TIDRI）；School of Public Health, Zhejiang University and Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江大学公共卫生学院及浙江省医学影像人工智能重点实验室）；State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital and Liangzhu Laboratory, Zhejiang University（浙江大学第二附属医院血管介入关键实验室及梁渚实验室）；Department of Gastroenterology, First Hospital of Quanzhou Affiliated to Fujian Medical University（福建医科大学泉州第一医院消化内科）；School of Medicine, Stanford University（斯坦福大学医学院）
