A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings
Zhejiang University（浙江大学）；The First Affiliated Hospital of University of Science and Technology of China（中国科学技术大学附属第一医院）；HeartVoice Medical Technology（心声医疗科技）；University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）
With the development of deep learning-based methods, automated classification of electrocardiograms (ECGs) has recently gained much attention. Although the effectiveness of deep neural networks has been encouraging, the lack of information given by the outputs restricts clinicians' reexamination. If the uncertainty estimation comes along with the classification results, cardiologists can pay more attention to "uncertain" cases. Our study aims to classify ECGs with rejection based on data uncertainty and model uncertainty. We perform experiments on a real-world 12-lead ECG dataset. First, we estimate uncertainties using the Monte Carlo dropout for each classification prediction, based on our Bayesian neural network. Then, we accept predictions with uncertainty under a given threshold and provide "uncertain" cases for clinicians. Furthermore, we perform a simulation experiment using varying thresholds. Finally, with the help of a clinician, we conduct case studies to explain the results of large uncertainties and incorrect predictions with small uncertainties. The results show that correct predictions are more likely to have smaller uncertainties, and the performance on accepted predictions improves as the accepting ratio decreases (i.e. more rejections). Case studies also help explain why rejection can improve the performance. Our study helps neural networks produce more accurate results and provide information on uncertainties to better assist clinicians in the diagnosis process. It can also enable deep-learning-based ECG interpretation in clinical implementation.
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…
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）
Chonnam National University（全南国立大学）；University of Arkansas（阿肯色大学）；West Virginia University（西弗吉尼亚大学）
Electrocardiogram (ECG) signal is one of the most effective sources of information mainly employed for the diagnosis and prediction of cardiovascular diseases (CVDs) connected with the abnormalities in heart rhythm. Clearly, single modality ECG (i.e. time series) cannot convey its complete characteristics, thus, exploiting both time and time-frequency modalities in the form of time-series data and spectrogram is needed. Leveraging the cutting-edge self-supervised learning (SSL) technique on unlabeled data, we propose SSL-based multimodality ECG classification. Our proposed network follows SSL …
National University of Singapore（新加坡国立大学）；HeartVoice Medical Technology（心声医疗科技）；University of Science and Technology of China（中国科学技术大学）；Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院附属邵逸夫医院）；Peking University（北京大学）；Institute of Medical Technology, Health Science Center of Peking University（北京大学医学部医学技术研究院）
Ventricular arrhythmias (VA) are the main causes of sudden cardiac death. Developing machine learning methods for detecting VA based on electrocardiograms (ECGs) can help save people's lives. However, developing such machine learning models for ECGs is challenging because of the following: 1) group-level diversity from different subjects and 2) individual-level diversity from different moments of a single subject. In this study, we aim to solve these problems in the pre-training and fine-tuning stages. For the pre-training stage, we propose a novel model agnostic meta-learning (MAML) with curr…
National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；Peking University（北京大学）
Learning information-rich and generalizable representations effectively from unlabeled multivariate cardiac signals to identify abnormal heart rhythms (cardiac arrhythmias) is valuable in real-world clinical settings but often challenging due to its complex temporal dynamics. Cardiac arrhythmias can vary significantly in temporal patterns even for the same patient ($i.e.$, intra subject difference). Meanwhile, the same type of cardiac arrhythmia can show different temporal patterns among different patients due to different cardiac structures ($i.e.$, inter subject difference). In this paper, w…
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）
Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital（人工智能在医疗与医学中的中心，慕尼黑技术大学（TUM）和慕尼黑大学医院）；Department of Cardiology, TUM University Hospital（心血管科，慕尼黑大学医院）；Department of Computing, Imperial College London（计算系，伦敦帝国理工学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））
Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging
School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；University of Zurich（苏黎世大学）；School of Medicine, Klinikum Rechts der Isar, Technical University of Munich（慕尼黑工业大学雷赫特-伊萨尔医院医学院）；Imperial College London（伦敦帝国学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
TRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care
Emory University（埃默里大学）；Massachusetts Institute of Technology（麻省理工学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Rochester（罗切斯特大学）
Combining ECG Foundation Model and XGBoost to Predict In-Hospital Malignant Ventricular Arrhythmias in AMI Patients
National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Fuwai Central China Cardiovascular Hospital（阜外华中心血管病医院）；HeartVoice Medical Technology（心音医疗科技）；Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究院）
Global and Local Contrastive Learning for Joint Representations from Cardiac MRI and ECG
Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany（人工智能在医疗与医学中的研究中心，技术大学慕尼黑（TUM）及慕尼黑技术大学医院）；School of Medicine, Klinikum rechts der Isar, TUM, Germany（医学院，右岸克里克医院，TUM，德国）；Department of Computing, Imperial College London, UK（计算学院，伦敦帝国学院，英国）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），慕尼黑，德国）
Learning Cardiac Features: ECG Biometrics Across Time and~Exercise
Aix-Marseille Univ（艾克斯-马赛大学）；CNRS（法国国家科学研究中心）；LIS（计算机与系统实验室）；Inserm-INRAE（法国国家健康与医学研究院-法国国家农业、食品与环境研究院）；C2VN（神经科学与心血管营养中心）；University Hospitals of Marseille（马赛大学医院）
Versatile and Risk-Sensitive Cardiac Diagnosis via Graph-Based ECG Signal Representation
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院第二附属医院国家心血管介入装置重点实验室）；Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence（浙江省医学影像人工智能重点实验室）；Alibaba Group（阿里巴巴集团）；School of Computer and Computational Science, Hangzhou City University（杭州城市学院计算机与计算科学学院）；Information Hub, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心）；Department of Computer Science and Engineering, University of Notre Dame（诺特大学计算机科学与工程系）；State Key Laboratory of Transvascular Implantation Devices of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine（浙江大学医学院第二附属医院和公共卫生学院国家心血管介入装置重点实验室）
University of Nevada, Reno（内华达大学里诺分校）；Fudan University（复旦大学）
Automating arrhythmia detection from ECG requires a robust and trusted system that retains high accuracy under electrical disturbances. Many machine learning approaches have reached human-level performance in classifying arrhythmia from ECGs. However, these architectures are vulnerable to adversarial attacks, which can misclassify ECG signals by decreasing the model's accuracy. Adversarial attacks are small crafted perturbations injected in the original data which manifest the out-of-distribution shifts in signal to misclassify the correct class. Thus, security concerns arise for false hospita…
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）
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…
Many types of ventricular and atrial cardiac arrhythmias have been discovered in clinical practice in the past 100 years, and these arrhythmias are a major contributor to sudden cardiac death. Ventricular tachycardia, ventricular fibrillation, and paroxysmal atrial fibrillation are the most commonly-occurring and dangerous arrhythmias, therefore early detection is crucial to prevent any further complications and reduce fatalities. Implantable devices such as pacemakers are commonly used in patients at high risk of sudden cardiac death. While great advances have been made in medical technology,…
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…
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…
Healthcare Technology and Innovation Center (HTIC), Indian Institute of Technology (IIT-M)（印度理工学院马德拉斯分校医疗技术与创新中心）；Indian Institute of Technology, Madras (IITM)（印度理工学院马德拉斯分校）
Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is performed by examination of the Electrocardiogram (ECG) by a cardiologist. This method of diagnosis is hampered by the lack of accessibility to expert cardiologists. For quite some time, signal processing methods had been used to automate arrhythmia diagnosis. However, these traditional methods require expert knowledge and are unable to model a wide range of arr…
Electrocardiogram (ECG) is one of the non-invasive and low-risk methods to monitor the condition of the human heart. Any abnormal pattern(s) in the ECG signal is an indicative measure of malfunctioning of the heart, termed as arrhythmia. Due to the lack of human expertise and high probability to misdiagnose, computer-aided diagnosis and analysis are preferred. In this work, we perform arrhythmia detection with an optimized neural network having piecewise linear approximation based activation function to alleviate the complex computations in the traditional activation functions. Further, we pro…
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…
School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）；Department of Electrocardiogram, The First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital)（中国科学技术大学附属第一医院（安徽省立医院）心电图科）
Automatic arrhythmia detection using 12-lead electrocardiogram (ECG) signal plays a critical role in early prevention and diagnosis of cardiovascular diseases. In the previous studies on automatic arrhythmia detection, most methods concatenated 12 leads of ECG into a matrix, and then input the matrix to a variety of feature extractors or deep neural networks for extracting useful information. Under such frameworks, these methods had the ability to extract comprehensive features (known as integrity) of 12-lead ECG since the information of each lead interacts with each other during training. How…
University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；Clemson University（克莱姆森大学）
ECG databases are usually highly imbalanced due to the abundance of Normal ECG and scarcity of abnormal cases. As such, deep learning classifiers trained on imbalanced datasets usually perform poorly, especially on minor classes. One solution is to generate realistic synthetic ECG signals using Generative Adversarial Networks (GAN) to augment imbalanced datasets. In this study, we combined conditional GAN with WGAN-GP and developed AC-WGAN-GP in 1D form for the first time to be applied on MIT-BIH Arrhythmia dataset. We investigated the impact of data augmentation on arrhythmia classification. …
University of Engineering and Technology Taxila（塔克西拉工程技术大学）；University of Central Florida（中佛罗里达大学）；Hankuk University of Foreign Studies（韩国外国语大学）；Xiamen University Malaysia（厦门大学马来西亚分校）
The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch b…
University of Lille（里尔大学）；ENSAIT（国立高等艺术与纺织工业学院）；GEMTEX–Laboratoire de Génie et Matériaux Textiles（纺织工程与材料实验室）；University of Kent（肯特大学）
Deep learning play a vital role in classifying different arrhythmias using the electrocardiography (ECG) data. Nevertheless, training deep learning models normally requires a large amount of data and it can lead to privacy concerns. Unfortunately, a large amount of healthcare data cannot be easily collected from a single silo. Additionally, deep learning models are like black-box, with no explainability of the predicted results, which is often required in clinical healthcare. This limits the application of deep learning in real-world health systems. In this paper, we design a new explainable a…
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…
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
Universiti Malaysia Pahang（马来西亚彭亨大学）；Queensland University of Technology（昆士兰科技大学）；Islamic University（伊斯兰大学）；The University of Sydney（悉尼大学）；University of Cambridge（剑桥大学）；The Garvan Institute of Medical Research（加文医学研究所）；The University of Queensland（昆士兰大学）
Due to the data shortage problem, which is one of the major problems in the field of machine learning, the accuracy level of many applications remains well below the expected. It prevents researchers from producing new artificial intelligence-based systems with the available data. This problem can be solved by generating new synthetic data with augmentation methods. In this study, new ECG signals are produced using MIT-BIH Arrhythmia Database by using Generative Adversarial Neural Networks (GAN), which is a modern data augmentation method. These generated data are used for training a machine l…
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…
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
The Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology, Peng Cheng Laboratory（南方科技大学鹏城实验室）；Huazhong University of Science and Technology（华中科技大学）
Indian Institute of Engineering Science and Technology, Shibpur（印度工程科学与技术学院，斯赫普尔）；IIT (ISM), Dhanbad（印度理工学院（印度矿业学院），丹巴德）
In primary diagnosis and analysis of heart defects, an ECG signal plays a significant role. This paper presents a model for the prediction of ventricular tachycardia arrhythmia using noise filtering, a unique set of ECG features, and a machine learning-based classifier model. Before signal feature extraction, we detrend and denoise the signal to eliminate the noise for detecting features properly. After that necessary features have been extracted and necessary parameters related to these features are measured. Using these parameters, we prepared one efficient multiclass classifier model using …
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…
Deep learning based ECG segmentation for delineation of diverse arrhythmias
Seoul National University（首尔大学）；Asan Medical Center, University of Ulsan College of Medicine（蔚山大学医学院附属峨山医院）；Pusan National University Hospital, Pusan National University School of Medicine（釜山大学医学院附属釜山国立大学医院）；AI Institute, Seoul National University（首尔大学人工智能研究院）；Seoul National University Hospital（首尔大学医院）；Medifarmsoft Co., Ltd.（Medifarmsoft有限公司）
本文提出一种类 U-Net 的心电图分割模型及后处理算法，用于在多种心律失常条件下准确 delineation P、QRS 和 T 波，并针对标准基准中代表性不足的心律失常提出改进策略。
The human heart is a complex system exhibiting stochastic nature, as reflected in electrocardiogram (ECG) signals. ECG signal is a weak, non-stationary, and nonlinear signal, which indicates the health of a heart in terms of temporal variations of electromagnetic pulses from the heart. Abnormal fluctuations in ECG signal invokes the possibility of various cardiovascular disorders, which is diagnosed through intuitive analysis of the ECG reports by the medical practitioners. This could be made fast, accurate, and simple by imposing advanced nonlinear tools on the recorded ECG signals. In this p…
Cross-Learning Between ECG and PCG: Exploring Common and Exclusive Characteristics of Bimodal Electromechanical Cardiac Waveforms
School of Medicine, Emory University（埃默里大学医学院）；Rollins School of Public Health, Department of Epidemiology, Emory University（埃默里大学罗林斯公共卫生学院流行病学系）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（佐治亚理工学院生物医学工程系）
Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main objective is developing the compact deep learning based detect system which minimally uses the dataset but delivers the confident accuracy rate of the Arrhythmia detectio…
Carnegie Mellon University（卡内基梅隆大学）；Allegheny Health Network（阿勒格尼健康网络）；University of Colorado（科罗拉多大学）
In this paper, we focus on a new method of data augmentation to solve the data imbalance problem within imbalanced ECG datasets to improve the robustness and accuracy of heart disease detection. By using Optimal Transport, we augment the ECG disease data from normal ECG beats to balance the data among different categories. We build a Multi-Feature Transformer (MF-Transformer) as our classification model, where different features are extracted from both time and frequency domains to diagnose various heart conditions. Learning from 12-lead ECG signals, our model is able to distinguish five categ…
Wearable electrocardiograph (ECG) recording and processing systems have been developed to detect cardiac arrhythmia to help prevent heart attacks. Conventional wearable systems, however, suffer from high energy consumption at both circuit and system levels. To overcome the design challenges, this paper proposes an event-driven compressive ECG recording and neuromorphic processing system for cardiac arrhythmia detection. The proposed system achieves low power consumption and high arrhythmia detection accuracy via system level co-design with spike-based information representation. Event-driven l…
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…
It is challenging to visually detect heart disease from the electrocardiographic (ECG) signals. Implementing an automated ECG signal detection system can help diagnosis arrhythmia in order to improve the accuracy of diagnosis. In this paper, we proposed, implemented, and compared an automated system using two different frameworks of the combination of convolutional neural network (CNN) and long-short term memory (LSTM) for classifying normal sinus signals, atrial fibrillation, and other noisy signals. The dataset we used is from the MIT-BIT Arrhythmia Physionet. Our approach demonstrated that …
