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Mohtasham Kia Y, Cannavo A, Bahiraie P, Alilou S, Saeedian B, Babajani N, Ghondaghsaz E, Khalaji A, Behnoush AH. Insights into the Role of Galectin-3 as a Diagnostic and Prognostic Biomarker of Atrial Fibrillation. DISEASE MARKERS 2023; 2023:2097012. [PMID: 37849915 PMCID: PMC10578984 DOI: 10.1155/2023/2097012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 09/02/2023] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
Atrial fibrillation (AF) is an irregular atrial activity and the most prevalent type of arrhythmia. Although AF is easily diagnosed with an electrocardiogram, there is a keen interest in identifying an easy-to-dose biomarker that can predict the prognosis of AF and its recurrence. Galectin-3 (Gal-3) is a beta-galactoside binding protein from the lectin family with pro-fibrotic and -inflammatory effects and a pivotal role in a variety of biological processes, cell proliferation, and differentiation; therefore, it is implicated in the pathogenesis of many cardiovascular (e.g., heart failure (HF)) and noncardiovascular diseases. However, its specificity and sensitivity as a potential marker in AF patients remain debated and controversial. This article comprehensively reviewed the evidence regarding the interplay between Gal-3 and patients with AF. Clinical implications of measuring Gal-3 in AF patients for diagnosis and prognosis are mentioned. Moreover, the role of Gal-3 as a potential biomarker for the management of AF recurrence is investigated. The association of Gal-3 and AF in special populations (coronary artery disease, HF, metabolic syndrome, chronic kidney disease, and diabetes mellitus) has been explored in this review. Overall, although further studies are needed to enlighten the role of Gal-3 in the diagnosis and treatment of AF, our study demonstrated the high potential of this molecule to be used and focused on by researchers and clinicians.
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Affiliation(s)
| | - Alessandro Cannavo
- Department of Translational Medical Sciences, Federico II University of Naples, Naples, Italy
| | - Pegah Bahiraie
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sanam Alilou
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Behrad Saeedian
- School of Medicine, Tehran University of Medical Sciences, Poursina St., Keshavarz Blvd., Tehran 1417613151, Iran
| | - Nastaran Babajani
- School of Medicine, Tehran University of Medical Sciences, Poursina St., Keshavarz Blvd., Tehran 1417613151, Iran
| | - Elina Ghondaghsaz
- Undergraduate Program in Neuroscience, University of British Columbia, Vancouver, BC, Canada
| | - Amirmohammad Khalaji
- School of Medicine, Tehran University of Medical Sciences, Poursina St., Keshavarz Blvd., Tehran 1417613151, Iran
| | - Amir Hossein Behnoush
- School of Medicine, Tehran University of Medical Sciences, Poursina St., Keshavarz Blvd., Tehran 1417613151, Iran
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2
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Kent M, Vasconcelos L, Ansari S, Ghanbari H, Nenadic I. Fourier space approach for convolutional neural network (CNN) electrocardiogram (ECG) classification: A proof-of-concept study. J Electrocardiol 2023; 80:24-33. [PMID: 37141727 DOI: 10.1016/j.jelectrocard.2023.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 02/15/2023] [Accepted: 04/04/2023] [Indexed: 05/06/2023]
Abstract
There has been a proliferation of machine learning (ML) electrocardiogram (ECG) classification algorithms reaching > 85% accuracy for various cardiac pathologies. Although the accuracy within institutions might be high, models trained at one institution might not be generalizable enough for accurate detection when deployed in other institutions due to differences in type of signal acquisition, sampling frequency, time of acquisition, device noise characteristics and number of leads. In this proof-of-concept study, we leverage the publicly available PTB-XL dataset to investigate the use of time-domain (TD) and frequency-domain (FD) convolutional neural networks (CNN) to detect myocardial infarction (MI), ST/T-wave changes (STTC), atrial fibrillation (AFIB) and sinus arrhythmia (SARRH). To simulate interinstitutional deployment, the TD and FD implementations were also compared on adapted test sets using different sampling frequencies 50 Hz, 100 Hz and 250 Hz, and acquisition times of 5 s and 10s at 100 Hz sampling frequency from the training dataset. When tested on the original sampling frequency and duration, the FD approach showed comparable results to TD for MI (0.92 FD - 0.93 TD AUROC) and STTC (0.94 FD - 0.95 TD AUROC), and better performance for AFIB (0.99 FD - 0.86 TD AUROC) and SARRH (0.91 FD - 0.65 TD AUROC). Although both methods were robust to changes in sampling frequency, changes in acquisition time were detrimental to the TD MI and STTC AUROCs, at 0.72 and 0.58 respectively. Alternatively, the FD approach was able to maintain the same level of performance, and, therefore, showed better potential for interinstitutional deployment.
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Affiliation(s)
- Madeline Kent
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | | | - Sardar Ansari
- Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Hamid Ghanbari
- Cardiovascular Disease, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Ivan Nenadic
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Radiology, Mayo Clinic, Rochester, MN, USA
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Duangburong S, Phruksaphanrat B, Muengtaweepongsa S. Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities. APPLIED SCIENCES 2023; 13:1712. [DOI: 10.3390/app13031712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
Classification of normal sinus rhythm (NSR), paroxysmal atrial fibrillation (PAF), and persistent atrial fibrillation (AF) is crucial in order to diagnose and effectively plan treatment for patients. Current classification models were primarily developed by electrocardiogram (ECG) signal databases, which may be unsuitable for local patients. Therefore, this research collected ECG signals from 60 local Thai patients (age 52.53 ± 23.92) to create a classification model. The coefficient of variance (CV), the median absolute deviation (MAD), and the root mean square of the successive differences (RMSSD) are ordinary feature variables of RR irregularities used by existing models. The square of average variation (SAV) is a newly proposed feature that extracts from the irregularity of RR intervals. All variables were found to be statistically different using ANOVA tests and Tukey’s method with a p-value less than 0.05. The methods of artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were also tested and compared to find the best classification model. Finally, SAV showed the best performance using the ANFIS model with trapezoidal membership function, having the highest system accuracy (ACC) at 89.33%, sensitivity (SE), specificity (SP), and positive predictivity (PPR) for NSR at 100.00%, 94.00%, and 89.29%, PAF at 88.00%, 90.57%, and 81.48%, and AF at 80.00%, 96.00%, and 90.91%, respectively.
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Affiliation(s)
- Suttirak Duangburong
- Research Unit in Industrial Statistics and Operational Research, Industrial Engineering Department, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani 12121, Thailand
| | - Busaba Phruksaphanrat
- Research Unit in Industrial Statistics and Operational Research, Industrial Engineering Department, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani 12121, Thailand
| | - Sombat Muengtaweepongsa
- Center of Excellence in Stroke, Faculty of Medicine, Thammasat University, Pathum Thani 10121, Thailand
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Mäkynen M, Ng GA, Li X, Schlindwein FS. Wearable Devices Combined with Artificial Intelligence-A Future Technology for Atrial Fibrillation Detection? SENSORS (BASEL, SWITZERLAND) 2022; 22:8588. [PMID: 36433186 PMCID: PMC9697321 DOI: 10.3390/s22228588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 11/02/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world. The arrhythmia and methods developed to cure it have been studied for several decades. However, professionals worldwide are still working to improve treatment quality. One novel technology that can be useful is a wearable device. The two most used recordings from these devices are photoplethysmogram (PPG) and electrocardiogram (ECG) signals. As the price lowers, these devices will become significant technology to increase sensitivity, for monitoring and for treatment quality support. This is important as AF can be challenging to detect in advance, especially during home monitoring. Modern artificial intelligence (AI) has the potential to respond to this challenge. AI has already achieved state of the art results in many applications, including bioengineering. In this perspective, we discuss wearable devices combined with AI for AF detection, an approach that enables a new era of possibilities for the future.
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Affiliation(s)
- Marko Mäkynen
- School of Engineering, University of Leicester, Leicester LE1 7RH, UK; (M.M.); (X.L.)
| | - G. Andre Ng
- School of Engineering, University of Leicester, Leicester LE1 7RH, UK; (M.M.); (X.L.)
- National Institute for Health Research Leicester Cardiovascular Biomedical Research Centre, Glenfield Hospital, Leicester LE5 4PW, UK;
| | - Xin Li
- School of Engineering, University of Leicester, Leicester LE1 7RH, UK; (M.M.); (X.L.)
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Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8002. [PMID: 36298352 PMCID: PMC9610988 DOI: 10.3390/s22208002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/06/2023]
Abstract
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
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Affiliation(s)
- Jian-Dong Huang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Jinling Wang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Gerard Leavey
- School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK
| | - Timothy J. A. Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
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Duan J, Wang Q, Zhang B, Liu C, Li C, Wang L. Accurate detection of atrial fibrillation events with R-R intervals from ECG signals. PLoS One 2022; 17:e0271596. [PMID: 35925979 PMCID: PMC9352004 DOI: 10.1371/journal.pone.0271596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 07/03/2022] [Indexed: 11/18/2022] Open
Abstract
Atrial fibrillation (AF) is a typical category of arrhythmia. Clinical diagnosis of AF is based on the detection of abnormal R-R intervals (RRIs) with an electrocardiogram (ECG). Previous studies considered this detection problem as a classification problem and focused on extracting a number of features. In this study we demonstrate that instead of using any specific numerical characteristic as the input feature, the probability density of RRIs from ECG conserves comprehensive statistical information; hence, is a natural and efficient input feature for AF detection. Incorporated with a support vector machine as the classifier, results on the MIT-BIH database indicates that the proposed method is a simple and accurate approach for AF detection in terms of accuracy, sensitivity, and specificity.
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Affiliation(s)
- Junbo Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
- * E-mail:
| | - Qing Wang
- School of Electronic Engineering, Xidian University, Xi’an, China
| | - Bo Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Chen Liu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Chenrui Li
- Key Laboratory of Biomedical Information Engineering of Ministry of Education and Department of Biomedical Engineering, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, China
| | - Lei Wang
- Cardiovascular Medicine, Weinan Central Hospital, Weinan, China
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Circulating Concentrations of Nutrition-Related Factors Are Not Causally Associated With Atrial Fibrillation: A Mendelian Randomization Study. J Cardiovasc Pharmacol 2022; 80:210-215. [PMID: 35353069 DOI: 10.1097/fjc.0000000000001263] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 03/11/2022] [Indexed: 11/25/2022]
Abstract
ABSTRACT Observational studies reported conflicting results regarding the association between circulating concentrations of nutrition-related factors and atrial fibrillation (AF). The aim of this study was to evaluate the potential causal effect of 8 circulating nutrition-related factors (vitamin B12, vitamin E, folate, retinol, β-carotene, iron, zinc, and copper) on AF risk using mendelian randomization (MR). Summary-level data for the nutrition-related factors and AF were obtained from genome-wide association studies conducted among individuals of European ancestry. The genome-wide association study on AF included 60,620 cases and 970,216 controls. A 2-sample MR design was applied for evaluating the causal association. In the primary MR analyses, the inverse variance-weighted method did not identify any causal effect of circulating concentrations of vitamin B12 [β = 0.000, standard error (SE) = 0.021, P = 0.994], vitamin E (β = 0.080, SE = 0.152, P = 0.600), retinol (β = 0.098, SE = 0.397, P = 0.806), folate (β = -0.006, SE = 0.052, P = 0.901), β-carotene (β = 0.014, SE = 0.025, P = 0.560), iron (β = -0.009, SE = 0.072, P = 0.905), zinc (β = 0.038, SE = 0.032, P = 0.239), and copper (β = -0.012, SE = 0.023, P = 0.589) on AF. The MR-Egger and MR pleiotropy residual sum and outlier (MR-PRESSO) analyses did not suggest the presence of pleiotropy. In addition, the lack of association remained in the leave-one-out analysis. This MR study indicates no causal association of circulating concentrations of vitamin B12, vitamin E, folate, retinol, β-carotene, iron, zinc, and copper with AF.
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8
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Kumar D, Puthusserypady S, Dominguez H, Sharma K, Bardram JE. CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset. Front Cardiovasc Med 2022; 9:893090. [PMID: 35845039 PMCID: PMC9283915 DOI: 10.3389/fcvm.2022.893090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022] Open
Abstract
ECG is a non-invasive tool for arrhythmia detection. In recent years, wearable ECG-based ambulatory arrhythmia monitoring has gained increasing attention. However, arrhythmia detection algorithms trained on existing public arrhythmia databases show higher FPR when applied to such ambulatory ECG recordings. It is primarily because the existing public databases are relatively clean as they are recorded using clinical-grade ECG devices in controlled clinical environments. They may not represent the signal quality and artifacts present in ambulatory patient-operated ECG. To help build and evaluate arrhythmia detection algorithms that can work on wearable ECG from free-living conditions, we present the design and development of the CACHET-CADB, a multi-site contextualized ECG database from free-living conditions. The CACHET-CADB is subpart of the REAFEL study, which aims at reaching the frail elderly patient to optimize the diagnosis of atrial fibrillation. In contrast to the existing databases, along with the ECG, CACHET-CADB also provides the continuous recording of patients' contextual data such as activities, body positions, movement accelerations, symptoms, stress level, and sleep quality. These contextual data can aid in improving the machine/deep learning-based automated arrhythmia detection algorithms on patient-operated wearable ECG. Currently, CACHET-CADB has 259 days of contextualized ECG recordings from 24 patients and 1,602 manually annotated 10 s heart-rhythm samples. The length of the ECG records in the CACHET-CADB varies from 24 h to 3 weeks. The patient's ambulatory context information (activities, movement acceleration, body position, etc.) is extracted for every 10 s interval cumulatively. From the analysis, nearly 11% of the ECG data in the database is found to be noisy. A software toolkit for the use of the CACHET-CADB is also provided.
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Affiliation(s)
- Devender Kumar
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Helena Dominguez
- Department of Cardiology, Bispebjerg-Frederiksberg Hospital, Copenhagen, Denmark
| | - Kamal Sharma
- U. N. Mehta Institute of Cardiology and Research Centre, Civil Hospital Campus, and SAL Hospital, Ahmedabad, India
| | - Jakob E. Bardram
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
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Dilaveris PE, Antoniou CK, Caiani EG, Casado-Arroyo R, Climent AΜ, Cluitmans M, Cowie MR, Doehner W, Guerra F, Jensen MT, Kalarus Z, Locati ET, Platonov P, Simova I, Schnabel RB, Schuuring M, Tsivgoulis G, Lumens J. ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients : In collaboration with the Council on Stroke, the European Heart Rhythm Association, and the Digital Health Committee. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2022; 3:341-358. [PMID: 36712155 PMCID: PMC9707962 DOI: 10.1093/ehjdh/ztac026] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The role of subclinical atrial fibrillation as a cause of cryptogenic stroke is unambiguously established. Long-term electrocardiogram (ECG) monitoring remains the sole method for determining its presence following a negative initial workup. This position paper of the European Society of Cardiology Working Group on e-Cardiology first presents the definition, epidemiology, and clinical impact of cryptogenic ischaemic stroke, as well as its aetiopathogenic association with occult atrial fibrillation. Then, classification methods for ischaemic stroke will be discussed, along with their value in providing meaningful guidance for further diagnostic efforts, given disappointing findings of studies based on the embolic stroke of unknown significance construct. Patient selection criteria for long-term ECG monitoring, crucial for determining pre-test probability of subclinical atrial fibrillation, will also be discussed. Subsequently, the two major classes of long-term ECG monitoring tools (non-invasive and invasive) will be presented, with a discussion of each method's pitfalls and related algorithms to improve diagnostic yield and accuracy. Although novel mobile health (mHealth) devices, including smartphones and smartwatches, have dramatically increased atrial fibrillation detection post ischaemic stroke, the latest evidence appears to favour implantable cardiac monitors as the modality of choice; however, the answer to whether they should constitute the initial diagnostic choice for all cryptogenic stroke patients remains elusive. Finally, institutional and organizational issues, such as reimbursement, responsibility for patient management, data ownership, and handling will be briefly touched upon, despite the fact that guidance remains scarce and widespread clinical application and experience are the most likely sources for definite answers.
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Affiliation(s)
| | - Christos Konstantinos Antoniou
- First Department of Cardiology, Hippokration Hospital, National and Kapodistrian University of Athens, 114 Vas. Sofias Avenue, 11527 Athens, Greece,Electrophysiology and Pacing Laboratory, Athens Heart Centre, Athens Medical Center, Marousi, Attica, Greece
| | - Enrico G Caiani
- Politecnico di Milano, Department of Electronics, Information and Biomedical Engineering, Milan, Italy,National Council of Research, Institute of Electronics, Information and Telecommunication Engineering, Milan, Italy
| | - Ruben Casado-Arroyo
- Department of Cardiology, Erasme Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Andreu Μ Climent
- ITACA Institute, Universitat Politècnica de València, Camino de Vera s/n, Valencia, Spain
| | - Matthijs Cluitmans
- CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Martin R Cowie
- Department of Cardiology, Royal Brompton Hospital, London, United Kingdom
| | - Wolfram Doehner
- Berlin Institute of Health at Charité—Universitätsmedizin Berlin, BIH Center for Regenerative Therapies (BCRT), Charitéplatz 1, 10117 Berlin, Germany,Department of Cardiology (Virchow Klinikum), and Center for Stroke Research Berlin, Charité Universitätsmedizin Berlin, and German Centre for Cardiovascular Research (DZHK), partner site Berlin, Germany
| | - Federico Guerra
- Cardiology and Arrhythmology Clinic, Marche Polytechnic University, University Hospital ‘Ospedali Riuniti Umberto I—Lancisi—Salesi’, Ancona, Italy
| | - Magnus T Jensen
- Department of Cardiology, Copenhagen University Hospital Amager & Hvidovre, Denmark
| | - Zbigniew Kalarus
- DMS in Zabrze, Department of Cardiology, Medical University of Silesia, Katowice, Poland
| | - Emanuela Teresa Locati
- Arrhythmology & Electrophysiology Department, IRCCS Policlinico San Donato, Milan, Italy
| | - Pyotr Platonov
- Department of Cardiology, Clinical Sciences, Lund University Hospital, Lund, Sweden
| | - Iana Simova
- Cardiology Clinic, Heart and Brain Centre of Excellence—University Hospital, Medical University Pleven, Pleven, Bulgaria
| | - Renate B Schnabel
- Department of Cardiology, University Heart and Vascular Centre Hamburg-Eppendorf, Hamburg, Germany,German Center for Cardiovascular Research (DZHK) partner site, Hamburg/Kiel/Lübeck, Germany
| | - Mark Schuuring
- Department of Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Georgios Tsivgoulis
- Second Department of Neurology, ‘Attikon’ University Hospital, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece,Department of Neurology, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Joost Lumens
- CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, The Netherlands
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Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
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Murat F, Sadak F, Yildirim O, Talo M, Murat E, Karabatak M, Demir Y, Tan RS, Acharya UR. Review of Deep Learning-Based Atrial Fibrillation Detection Studies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:11302. [PMID: 34769819 PMCID: PMC8583162 DOI: 10.3390/ijerph182111302] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 10/22/2021] [Accepted: 10/24/2021] [Indexed: 02/01/2023]
Abstract
Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.
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Affiliation(s)
- Fatma Murat
- Department of Electrical and Electronics Engineering, Firat University, Elazig 23000, Turkey;
| | - Ferhat Sadak
- Department of Mechanical Engineering, Bartin University, Bartin 74100, Turkey;
| | - Ozal Yildirim
- Department of Software Engineering, Firat University, Elazig 23000, Turkey; (O.Y.); (M.T.); (M.K.)
| | - Muhammed Talo
- Department of Software Engineering, Firat University, Elazig 23000, Turkey; (O.Y.); (M.T.); (M.K.)
| | - Ender Murat
- Department of Cardiology, Gülhane Training and Research Hospital, Ankara 06000, Turkey;
| | - Murat Karabatak
- Department of Software Engineering, Firat University, Elazig 23000, Turkey; (O.Y.); (M.T.); (M.K.)
| | - Yakup Demir
- Department of Electrical and Electronics Engineering, Firat University, Elazig 23000, Turkey;
| | - Ru-San Tan
- Department of Cardiology, National Heart Centre Singapore, Singapore 169609, Singapore;
- Department of Cardiology, Duke-NUS Graduate Medical School, Singapore 169857, Singapore
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 138607, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
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12
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Kareem M, Lei N, Ali A, Ciaccio EJ, Acharya UR, Faust O. A review of patient-led data acquisition for atrial fibrillation detection to prevent stroke. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102818] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Tutuko B, Nurmaini S, Tondas AE, Rachmatullah MN, Darmawahyuni A, Esafri R, Firdaus F, Sapitri AI. AFibNet: an implementation of atrial fibrillation detection with convolutional neural network. BMC Med Inform Decis Mak 2021; 21:216. [PMID: 34261486 PMCID: PMC8281594 DOI: 10.1186/s12911-021-01571-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 06/29/2021] [Indexed: 11/27/2022] Open
Abstract
Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment
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Affiliation(s)
- Bambang Tutuko
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia.
| | - Alexander Edo Tondas
- Department of Cardiology and Vascular Medicine, Dr. Mohammad Hoesin Hospital, Palembang, Indonesia
| | - Muhammad Naufal Rachmatullah
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Annisa Darmawahyuni
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Ria Esafri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Firdaus Firdaus
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Ade Iriani Sapitri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
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Lei N, Kareem M, Moon SK, Ciaccio EJ, Acharya UR, Faust O. Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:813. [PMID: 33477887 PMCID: PMC7833442 DOI: 10.3390/ijerph18020813] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 11/16/2022]
Abstract
In this paper, we discuss hybrid decision support to monitor atrial fibrillation for stroke prevention. Hybrid decision support takes the form of human experts and machine algorithms working cooperatively on a diagnosis. The link to stroke prevention comes from the fact that patients with Atrial Fibrillation (AF) have a fivefold increased stroke risk. Early diagnosis, which leads to adequate AF treatment, can decrease the stroke risk by 66% and thereby prevent stroke. The monitoring service is based on Heart Rate (HR) measurements. The resulting signals are communicated and stored with Internet of Things (IoT) technology. A Deep Learning (DL) algorithm automatically estimates the AF probability. Based on this technology, we can offer four distinct services to healthcare providers: (1) universal access to patient data; (2) automated AF detection and alarm; (3) physician support; and (4) feedback channels. These four services create an environment where physicians can work symbiotically with machine algorithms to establish and communicate a high quality AF diagnosis.
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Affiliation(s)
- Ningrong Lei
- College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Murtadha Kareem
- Materials & Engineering Research Institute, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Seung Ki Moon
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore;
| | - Edward J. Ciaccio
- Department of Medicine-Cardiology, Columbia University, New York, NY 10027, USA;
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Singapore 598269, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- School of Management and Enterprise, University of Southern Queensland, Toowoomba 4350, Australia
| | - Oliver Faust
- College of Business, Technology and Engineering, Sheffield Hallam University, Sheffield S1 1WB, UK;
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15
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Li Z, Derksen H, Gryak J, Jiang C, Gao Z, Zhang W, Ghanbari H, Gunaratne P, Najarian K. Prediction of cardiac arrhythmia using deterministic probabilistic finite-state automata. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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16
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Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings. BIOMED RESEARCH INTERNATIONAL 2020; 2020:8843963. [PMID: 33415163 PMCID: PMC7769642 DOI: 10.1155/2020/8843963] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 11/22/2020] [Accepted: 12/08/2020] [Indexed: 12/21/2022]
Abstract
The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.
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Faust O, Lei N, Chew E, Ciaccio EJ, Acharya UR. A Smart Service Platform for Cost Efficient Cardiac Health Monitoring. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E6313. [PMID: 32872667 PMCID: PMC7504315 DOI: 10.3390/ijerph17176313] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 08/20/2020] [Accepted: 08/24/2020] [Indexed: 11/22/2022]
Abstract
AIM In this study we have investigated the problem of cost effective wireless heart health monitoring from a service design perspective. SUBJECT AND METHODS There is a great medical and economic need to support the diagnosis of a wide range of debilitating and indeed fatal non-communicable diseases, like Cardiovascular Disease (CVD), Atrial Fibrillation (AF), diabetes, and sleep disorders. To address this need, we put forward the idea that the combination of Heart Rate (HR) measurements, Internet of Things (IoT), and advanced Artificial Intelligence (AI), forms a Heart Health Monitoring Service Platform (HHMSP). This service platform can be used for multi-disease monitoring, where a distinct service meets the needs of patients having a specific disease. The service functionality is realized by combining common and distinct modules. This forms the technological basis which facilitates a hybrid diagnosis process where machines and practitioners work cooperatively to improve outcomes for patients. RESULTS Human checks and balances on independent machine decisions maintain safety and reliability of the diagnosis. Cost efficiency comes from efficient signal processing and replacing manual analysis with AI based machine classification. To show the practicality of the proposed service platform, we have implemented an AF monitoring service. CONCLUSION Having common modules allows us to harvest the economies of scale. That is an advantage, because the fixed cost for the infrastructure is shared among a large group of customers. Distinct modules define which AI models are used and how the communication with practitioners, caregivers and patients is handled. That makes the proposed HHMSP agile enough to address safety, reliability and functionality needs from healthcare providers.
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Affiliation(s)
- Oliver Faust
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Ningrong Lei
- Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK;
| | - Eng Chew
- Faculty of Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia;
| | - Edward J. Ciaccio
- Department of Medicine—Cardiology, Columbia University, New York, NY 10027, USA;
| | - U Rajendra Acharya
- Biomedical Engineering Department, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- School of Management and Enterprise, University of Southern Queensland, Springfield, QLD 4350, Australia
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