Deep Learning systems have achieved great success in the past few years, even surpassing human intelligence in several cases. As of late, they have also established themselves in the biomedical and healthcare domains, where they have shown a lot of promise, but have not yet achieved widespread adoption. This is in part due to the fact that most methods fail to maintain their performance when they are called to make decisions on data that originate from a different distribution than the one they were trained on, namely Out-Of-Distribution (OOD) data. For example, in the case of biosignal classification, models often fail to generalize well on datasets from different hospitals, due to the distribution discrepancy amongst different sources of data. Our goal is to demonstrate the Domain Generalization problem present between distinct hospital databases and propose a method that classifies abnormalities on 12-lead Electrocardiograms (ECGs), by leveraging information extracted across the architecture of a Deep Neural Network, and capturing the underlying structure of the signal. To this end, we adopt a ResNet-18 as the backbone model and extract features from several intermediate convolutional layers of the network. To evaluate our method, we adopt publicly available ECG datasets from four sources and handle them as separate domains. To simulate the distributional shift present in real-world settings, we train our model on a subset of the domains and leave-out the remaining ones. We then evaluate our model both on the data present at training time (intra-distribution) and the held-out data (out-of-distribution), achieving promising results and surpassing the baseline of a vanilla Residual Network in most of the cases.
In this paper, a novel ECG monitoring approach based on IoT technology is suggested. This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality (REL). In addition, the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolution Neural Networks (CNNs)-based approaches for ECG signal categorization were tested in this study. Deep-ECG will employ a deep CNN to extract important characteristics, which will then be compared using simple and fast distance functions in order to classify cardiac prob…
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…
LVCG: Learning ECG Representations in the Latent Vectorcardiogram Space
Griffith University, Australia（格里菲斯大学）；SingHealth Duke-NUS AI in Medicine Institute, Singapore（新加坡SingHealth Duke-NUS医学人工智能研究所）；Emory University, USA（埃默里大学）
Virgen Del Rocío University Hospital（罗西奥圣母大学医院）；Universidad San Jorge（圣豪尔赫大学）；Pompeu Fabra University（庞培法布拉大学）
Obtaining per-beat information is a key task in the analysis of cardiac electrocardiograms (ECG), as many downstream diagnosis tasks are dependent on ECG-based measurements. Those measurements, however, are costly to produce, especially in recordings that change throughout long periods of time. However, existing annotated databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically compos…
We present an electrocardiogram (ECG) -based emotion recognition system using self-supervised learning. Our proposed architecture consists of two main networks, a signal transformation recognition network and an emotion recognition network. First, unlabelled data are used to successfully train the former network to detect specific pre-determined signal transformations in the self-supervised learning step. Next, the weights of the convolutional layers of this network are transferred to the emotion recognition network, and two dense layers are trained in order to classify arousal and valence sco…
The analysis of electrocardiogram (ECG) signals can be time consuming as it is performed manually by cardiologists. Therefore, automation through machine learning (ML) classification is being increasingly proposed which would allow ML models to learn the features of a heartbeat and detect abnormalities. The lack of interpretability hinders the application of Deep Learning in healthcare. Through interpretability of these models, we would understand how a machine learning algorithm makes its decisions and what patterns are being followed for classification. This thesis builds Convolutional Neura…
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…
From Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student
São Paulo State University (Unesp)（圣保罗州立大学）；Hospital Israelita Albert Einstein（以色列阿尔伯特·爱因斯坦医院）；University of São Paulo Faculty of Medicine Clinics Hospital（圣保罗大学医学院临床医院）
Pathology-Aware Multi-View Contrastive Learning for Patient-Independent ECG Reconstruction
Department of Computer Science and Engineering, Indian Institute of Technology Roorkee（计算机科学与工程系，印度理工学院Roorkee）；Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee（生物科学与生物工程系，印度理工学院Roorkee）
ImputeECG: Deep Learning Reconstruction of Complete 12-Lead Electrocardiograms from Incomplete Recordings for Cardiac Assessment
National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）；School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）；University of the Chinese Academy of Sciences（中国科学院大学）；Department of Cardiology, Peking University People’s Hospital（北京大学人民医院心内科）；Department of Computer Science, Tianjin University of Technology（天津理工大学计算机科学与工程学院）
ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification
Dipartimento di Informatica, Università di Verona, Verona, Italy（信息学院，威尼斯大学，威尼斯，意大利）；Department of Computer Science, School of Mathematics and Computer Science, Institute of Business Administration, Karachi, Pakistan（计算机科学系，数学与计算机科学学院，商学院，卡拉奇，巴基斯坦）；Faculty of Information Technology, Salim Habib University, Karachi, Pakistan（信息技术学院，萨利姆·哈比大学，卡拉奇，巴基斯坦）
uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm
Faculty of Biomedical Engineering, Technion-IIT（生物医学工程学院，技术ion-IIT）；Ruth and Bruce Rappaport Faculty of Medicine, Technion-IIT（鲁思和布鲁斯·拉帕波特医学学院，技术ion-IIT）
Self-supervised inter-intra period-aware ECG representation learning for detecting atrial fibrillation
Shenzhen International Graduate School（深圳国际研究生院）；Tsinghua University（清华大学）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Beijing Tsinghua Changgung Hospital（北京清华长庚医院）；School of Medicine（医学院）
针对 ECG 标注昂贵及房颤特征捕获不足的问题，提出融合 RR 间期与 P 波知识的周期间-周期内自监督表示学习方法，并在多个数据集取得优异房颤检测性能。
Tsinghua University（清华大学）；HeartVoice Medical Technology（心之声医疗科技）；University of Science and Technology of China（中国科学技术大学）；HeartRhythm Medical（心律医疗）；Tianjin Institute of Cardiology（天津心脏病学研究所）；Second Hospital of Tianjin Medical University（天津医科大学第二医院）；Peking University（北京大学）
In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by using medical and commercial devices. Deep neural networks (DNNs) can be used to analyze these signals because of their high accuracy rate. However, researchers have found that adversarial attacks can significantly reduce the accuracy of DNNs. Studies have been conducted to defend ECG-based DNNs against traditional adversarial attacks, such as projected gradient descent (PGD), and smooth adve…
ETH Zürich（苏黎世联邦理工学院）；University of Bologna（博洛尼亚大学）；Huawei Technologies（华为技术有限公司）
Personalized ubiquitous healthcare solutions require energy-efficient wearable platforms that provide an accurate classification of bio-signals while consuming low average power for long-term battery-operated use. Single lead electrocardiogram (ECG) signals provide the ability to detect, classify, and even predict cardiac arrhythmia. In this paper, we propose a novel temporal convolutional network (TCN) that achieves high accuracy while still being feasible for wearable platform use. Experimental results on the ECG5000 dataset show that the TCN has a similar accuracy (94.2%) score as the state…
National Physical Laboratory（英国国家物理实验室）；University of Surrey（萨里大学）
The electrocardiogram (ECG) is one of the most widespread diagnostic tools in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study we generate clean and noisy versions of an ECG dataset before applying Symmetric Projection Attractor Reconstruction (SPAR) and scalogram image transformations. A pretrained convolutional neural ne…
AICRN: Attention-Integrated Convolutional Residual Network for Interpretable Electrocardiogram Analysis
Department of Computer Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka（珀达尼亚大学计算机工程系）；Simula Research Laboratory, Kristian Augusts Gate 23, 0164 Oslo（Simula研究实验室）
Swiss Center for Electronics and Microtechnology (CSEM)（瑞士电子与微技术中心）
We developed a convolutional recurrent neural network to classify 12-lead ECG signals for the challenge of PhysioNet/ Computing in Cardiology 2020 as team Pink Irish Hat. The model combines convolutional and recurrent layers, takes sliding windows of ECG signals as input and yields the probability of each class as output. The convolutional part extracts features from each sliding window. The bi-directional gated recurrent unit (GRU) layer and an attention layer aggregate these features from all windows into a single feature vector. Finally, a dense layer outputs class probabilities. The final …
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…
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…
In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep convolutional networks. In the first step we use a Bayesian spectro-temporal representation based on the estimation of time-varying coefficients of Fourier series using Kalman filter and smoother. Next, we derive an alternative model based on a stochastic oscillator differential equation to accelerate the estimation of the spectro-temporal representation in lengthy signals. Finally, after comparative evaluations of …
Automated QRS detection methods depend on the ECG data which is sampled at a certain frequency, irrespective of filter-based traditional methods or convolutional network (CNN) based deep learning methods. These methods require a selection of the sampling frequency at which they operate in the very first place. While working with data from two different datasets, which are sampled at different frequencies, often, data from both the datasets may need to resample at a common target frequency, which may be the frequency of either of the datasets or could be a different one. However, choosing data …
National Taiwan University（国立台湾大学）；Academia Sinica（中央研究院）；National Taiwan University of Science and Technology（国立台湾科技大学）
Electrocardiogram (ECG) artifact contamination often occurs in surface electromyography (sEMG) applications when the measured muscles are in proximity to the heart. Previous studies have developed and proposed various methods, such as high-pass filtering, template subtraction and so forth. However, these methods remain limited by the requirement of reference signals and distortion of original sEMG. This study proposed a novel denoising method to eliminate ECG artifacts from the single-channel sEMG signals using fully convolutional networks (FCN). The proposed method adopts a denoise autoencode…
Stanford Online High School（斯坦福在线高中）；AliveCor, Inc.（AliveCor公司）
Mobile electrocardiogram (ECG) recording technologies represent a promising tool to fight the ongoing epidemic of cardiovascular diseases, which are responsible for more deaths globally than any other cause. While the ability to monitor one's heart activity at any time in any place is a crucial advantage of such technologies, it is also the cause of a drawback: signal noise due to environmental factors can render the ECGs illegible. In this work, we develop convolutional neural networks (CNNs) to automatically label ECGs for noise, training them on a novel noise-annotated dataset. By reducing …
OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records
The First Affiliated Hospital, Jiangxi Medical College, Nanchang University（南昌大学江西医学院第一附属医院）；Nanchang University（南昌大学）；Georgia Institution of Technology（佐治亚理工学院）
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…
The study in this paper presents a one-dimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from single-channel electrocardiogram (ECG) signals. The system provides mechanisms in clinical practice that help diagnose patients suffering with OSA. Using the state-of-the-art in 1DCNNs, a model is constructed using convolutional, max pooling layers and a fully connected Multilayer Perceptron (MLP) consisting of a hidden layer and SoftMax output for classification. The 1DCNN extracts prominent features, which are used…
Rice University（莱斯大学）；Prudence Medical Technologies Ltd（审慎医疗技术有限公司）
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 promis…
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…
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…
Electrocardiogram (ECG) has been widely used for emotion recognition. This paper presents a deep neural network based on convolutional layers and a transformer mechanism to detect stress using ECG signals. We perform leave-one-subject-out experiments on two publicly available datasets, WESAD and SWELL-KW, to evaluate our method. Our experiments show that the proposed model achieves strong results, comparable or better than the state-of-the-art models for ECG-based stress detection on these two datasets. Moreover, our method is end-to-end, does not require handcrafted features, and can learn ro…
École de technologie supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）
Idiopathic ventricular arrhythmia (IVAs) is extra abnormal heartbeats disturbing the regular heart rhythm that can become fatal if left untreated. Cardiac catheter ablation is the standard approach to treat IVAs, however, a crucial prerequisite for the ablation is the localization of IVAs' origin. The current IVA localization techniques are invasive, rely on expert interpretation, or are inaccurate. In this study, we developed a new deep-learning algorithm that can automatically identify the origin of IVAs from ECG signals without the need for expert manual analysis. Our developed deep learnin…
Qatar University（卡塔尔大学）；Liverpool John Moores University（利物浦约翰摩尔斯大学）；University of Kurdistan Hewler（库尔德斯坦休勒尔大学）；Firat University（菲拉特大学）；Sulaimani Polytechnic University（苏莱曼尼亚理工大学）
The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-lear…
Qatar University（卡塔尔大学）；Izmir University of Economics（伊兹密尔经济大学）；Tampere University（坦佩雷大学）
Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Therefore, in this study, a novel implementation of the 1D Convolutional Neural Network (CNN) is used integrated with a verification model to reduce the number of…
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…
University of Sfax（斯法克斯大学）；National Engineering School of Sfax (ENIS)（斯法克斯国家工程学院）；Faculty of Science of Sfax (FSS)（斯法克斯科学学院）
The interpretation of the electrocardiogram (ECG) gives clinical information and helps in assessing heart function. There are distinct ECG patterns associated with a specific class of arrythmia. The convolutional neural network is currently one of the most commonly employed deep learning algorithms for ECG processing. However, deep learning models require many hyperparameters to tune. Selecting an optimal or best hyperparameter for the convolutional neural network algorithm is a highly challenging task. Often, we end up tuning the model manually with different possible ranges of values until a…
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences（波兰科学院理论与应用信息学研究所）；Warsaw University of Technology（华沙理工大学）；Cracow University of Technology（克拉科夫理工大学）；Firat University（菲拉特大学）；Singapore University of Social Sciences（新加坡社会科学大学）；University of Southern Queensland（南昆士兰大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）；Kumamoto University（熊本大学）；National Heart Centre Singapore（新加坡国家心脏中心）
Interpretation of electrocardiography (ECG) signals is required for diagnosing cardiac arrhythmia. Recently, machine learning techniques have been applied for automated computer-aided diagnosis. Machine learning tasks can be divided into regression and classification. Regression can be used for noise and artifacts removal as well as resolve issues of missing data from low sampling frequency. Classification task concerns the prediction of output diagnostic classes according to expert-labeled input classes. In this work, we propose a deep neural network model capable of solving regression and cl…
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…
