AFibNet: an implementation of atrial fibrillation detection with convolutional neural network

Published on Jul 14, 2021
· DOI :10.1186/S12911-021-01571-1
Bambang Tutuko7
Estimated H-index: 7
Siti Nurmaini12
Estimated H-index: 12
+ 5 AuthorsAde Iriani Sapitri2
Estimated H-index: 2
Abstract The most prevalent arrhythmia observed in clinical practice is atrial fibrillation (AF). AF is associated with an irregular heartbeat pattern and a lack of a distinct P-waves signal. A low-cost method for identifying this condition is the use of a single-lead electrocardiogram (ECG) as the gold standard for AF diagnosis, after annotation by experts. However, manual interpretation of these signals may be subjective and susceptible to inter-observer variabilities because many non-AF rhyth...
#1Abdulhamit SubasiH-Index: 41
#2Saeed Mian QaisarH-Index: 13
#1Sidrah Liaqat (University of the West of Scotland)H-Index: 6
#2Kia Dashtipour (Glas.: University of Glasgow)H-Index: 16
Last. Naeem RamzanH-Index: 19
view all 6 authors...
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF...
#1Solveig K. Sieberts (Sage Bionetworks)H-Index: 23
#2Thanneer M. Perumal (Sage Bionetworks)H-Index: 15
Last. Chris GaiteriH-Index: 23
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The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer's Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples...
#1Jessica Torres-Soto (Stanford University)H-Index: 1
#2Euan A. Ashley (Stanford University)H-Index: 80
Wearable devices enable theoretically continuous, longitudinal monitoring of physiological measurements such as step count, energy expenditure, and heart rate. Although the classification of abnormal cardiac rhythms such as atrial fibrillation from wearable devices has great potential, commercial algorithms remain proprietary and tend to focus on heart rate variability derived from green spectrum LED sensors placed on the wrist, where noise remains an unsolved problem. Here we develop DeepBeat, ...
#1Zahra Ebrahimi (University of Shahrood)H-Index: 1
#2Mohammad Loni (MDH: Mälardalen University College)H-Index: 6
Last. Arash Gharehbaghi (MDH: Mälardalen University College)H-Index: 12
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Abstract Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Ne...
#1Gloria M. Sheynkman (Harvard University)H-Index: 17
#2Katharine S. Tuttle (ISMMS: Icahn School of Medicine at Mount Sinai)H-Index: 4
Last. Marc Vidal (Harvard University)H-Index: 117
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Most human protein-coding genes are expressed as multiple isoforms, which greatly expands the functional repertoire of the encoded proteome. While at least one reliable open reading frame (ORF) model has been assigned for every coding gene, the majority of alternative isoforms remains uncharacterized due to (i) vast differences of overall levels between different isoforms expressed from common genes, and (ii) the difficulty of obtaining full-length transcript sequences. Here, we present ORF Capt...
#1Antônio H. Ribeiro (UFMG: Universidade Federal de Minas Gerais)H-Index: 7
#2Manoel Horta Ribeiro (UFMG: Universidade Federal de Minas Gerais)H-Index: 10
Last. Antonio Luiz Pinho Ribeiro (UFMG: Universidade Federal de Minas Gerais)H-Index: 69
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#1Oliver Faust (SHU: Sheffield Hallam University)H-Index: 30
#2Edward J. Ciaccio (Columbia University)H-Index: 32
Last. U. Rajendra Acharya (Ngee Ann Polytechnic)H-Index: 101
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Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service...
Cited By2
#1Ho Es (Lafayette College)
#2Ding Z (Johns Hopkins University)
Background and purposes: Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propose the use of a one-dimensional convolutional network (1D-CNN) architecture ...
#1Nikoletta Katsaouni (Goethe University Frankfurt)H-Index: 1
#2Florian Aul (Goethe University Frankfurt)
Last. Marcel H. Schulz (Goethe University Frankfurt)H-Index: 21
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Abstract null Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of sma...
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