In this work we apply variations of ResNet architecture to the task of atrial fibrillation classification. Variations differ in number of filter after first convolution, ResNet block layout, number of filters in block convolutions and number of ResNet blocks between downsampling operations. We have found a range of model size in which models with quite different configurations show similar performance. It is likely that overall number of parameters plays dominant role in model performance. However, configuration parameters like layout have values that constantly lead to better results, which allows to suggest that these parameters should be defined and fixed in the first place, while others may be varied in a reasonable range to satisfy any existing constraints.
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Pennsylvania State University（宾夕法尼亚州立大学）；United International University（联合国际大学）；University of Rochester（罗切斯特大学）；United Arab Emirates University（阿联酋大学）
Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF) detection in real-time leveraging photoplethysmography (PPG) data, an inexpensive sensor widely available in almost all smartwatches. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a…
A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients
Department of Electrical and Computer Engineering（电气与计算机工程系）；Queen’s University（女王大学）；School of Computing（计算机学院）；Smith School of Business（商学院）；Departments of Emergency Medicine and Critical Care Medicine（急诊医学与重症医学系）；School of Computing and the Department of Electrical and Computer Engineering（计算机学院和电气与计算机工程系）；Department of Surgery（外科系）；Departments of Medicine and Critical Care Medicine（医学与重症医学系）；University of British Columbia（不列颠哥伦比亚大学）
Scientific Computing and Imaging Institute（科学计算与成像研究所）；Comprehensive Arrhythmia Research and Management Center（综合性心律失常研究与管理中心）
Left atrium shape has been shown to be an independent predictor of recurrence after atrial fibrillation (AF) ablation. Shape-based representation is imperative to such an estimation process, where correspondence-based representation offers the most flexibility and ease-of-computation for population-level shape statistics. Nonetheless, population-level shape representations in the form of image segmentation and correspondence models derived from cardiac MRI require significant human resources with sufficient anatomy-specific expertise. In this paper, we propose a machine learning approach that …
The understanding of complex physical or biological systems nearly always requires a characterisation of the variability that underpins these processes. In addition, the data used to calibrate such models may also often exhibit considerable variability. A recent approach to deal with these issues has been to calibrate populations of models (POMs), that is multiple copies of a single mathematical model but with different parameter values. To date this calibration has been limited to selecting models that produce outputs that fall within the ranges of the dataset, ignoring any trends that might …
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；King’s College London（伦敦国王学院）；Technical University Munich（慕尼黑工业大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）
Left atrial (LA) and atrial scar segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is an important task in clinical practice. %, to guide ablation therapy and predict treatment results for atrial fibrillation (AF) patients. The automatic segmentation is however still challenging, due to the poor image quality, the various LA shapes, the thin wall, and the surrounding enhanced regions. Previous methods normally solved the two tasks independently and ignored the intrinsic spatial relationship between LA and scars. In this work, we develop a new framework, namely Atr…
Integrating Deep Learning in Cardiology: A Comprehensive Review of Atrial Fibrillation, Left Atrial Scar Segmentation, and the Frontiers of State-of-the-Art Techniques
University of Auckland（奥克兰大学）；Auckland Bioengineering Institute（奥克兰生物工程研究所）
Atrial Fibrillation (AF) is the most common type of arrhythmia (Greek a-, loss + rhythmos, rhythm = loss of rhythm) leading to hospitalization in the United States. Though sometimes AF is asymptomatic, it increases the risk of stroke and heart failure in patients, in addition to lowering the health-related quality of life (HRQOL). AF-related care costs the healthcare system between $6.0 to $26 billion each year. Early detection of AF and clinical attention can help improve symptoms and HRQOL of the patient, as well as bring down the cost of care. However, the prevalent paradigm of AF detection…
Royal Brompton Hospital（皇家布朗普顿医院）；National Heart and Lung Institute, Imperial College London（帝国理工学院国家心肺研究所）；Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；University of Lincoln（林肯大学）；City University London（伦敦城市大学）
Purpose: Atrial fibrillation (AF) is the most common cardiac arrhythmia and is correlated with increased morbidity and mortality. It is associated with atrial fibrosis, which may be assessed non-invasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualised as a region of signal enhancement. In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients. Methods: Our fully automatic pipeline uniquely combined: (1) a multi-atlas bas…
Philips Research North America（飞利浦北美研究院）；Advanced Algorithm Research Center, Philips Healthcare（飞利浦医疗先进算法研究中心）
The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 seconds). For this purpose, signal quality index (SQI) along with dense convolutional neural networks was used. Two convolutional neural network (CNN) models (main model that acce…
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Technical University of Munich（慕尼黑工业大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and…
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）
University of Oulu（奥卢大学）；Medical Research Center Oulu（奥卢医学研究中心）
Objective: We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. Methods: Face videos, electrocardiography (ECG), and contact photoplethysmography (PPG) from 100 healthy subjects and 100 AF patients are recorded. Data recordings from healthy subjects are all labeled as healthy. Two cardiologists evaluated ECG recordings of patients and labeled each recording as AF, sinus rhythm (SR), or atrial flutter (AFL). We use the 3D convolutional neural network for remote PPG monitoring and propose a novel loss function (Wasserstein distance) to use the timing of syst…
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability of being undetected. In this work, we present an attention based deep learning framework for detection of paroxysmal AF episodes from a sequence of windows. Time-frequency representation of 30 seconds recording windows, over a 10 minute data segment, are fed sequentially into a deep convolutional neural network for image-based feature extraction, …
Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS), University of Santiago de Compostela（圣地亚哥-德孔波斯特拉大学信息技术研究中心）
Objective: This work aims at providing a new method for the automatic detection of atrial fibrillation, other arrhythmia and noise on short single lead ECG signals, emphasizing the importance of the interpretability of the classification results. Approach: A morphological and rhythm description of the cardiac behavior is obtained by a knowledge-based interpretation of the signal using the \textit{Construe} abductive framework. Then, a set of meaningful features are extracted for each individual heartbeat and as a summary of the full record. The feature distributions were used to elucidate the …
Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes
The Hong Kong University of Science and Technology（香港科技大学）；Sun Yat-Sen University（中山大学）；Sun Yat-Sen Memorial Hospital（孙逸仙纪念医院）；Chinese Academy of Sciences（中国科学院）
University of Warwick（华威大学）；King’s College London（伦敦国王学院）
Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder that causes changes in the anatomy of the atria. A better characterization of these changes is desirable for the definition of clinical biomarkers, furthermore, thus there is a need for its fully automatic segmentation from clinical images. In this work, we present an architecture based on 3D-convolution kernels, a Volumetric Fully Convolution Neural Network (V-FCNN), able to segment the entire volume in a one-shot, and consequently integrate the implicit spatial redundancy present in high-resolution images. A loss fun…
Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. However, it is difficult to incorporate local and global information without using contracting (pooli…
