1
|
Liang H, Zhang H, Wang J, Shao X, Wu S, Lyu S, Xu W, Wang L, Tan J, Wang J, Yang Y. The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment. Rev Cardiovasc Med 2024; 25:257. [PMID: 39139434 PMCID: PMC11317345 DOI: 10.31083/j.rcm2507257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/16/2024] [Accepted: 01/26/2024] [Indexed: 08/15/2024] Open
Abstract
Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for AF have been updated in recent years, its gradual onset and associated risk of stroke pose challenges for both patients and cardiologists in real-world practice. Artificial intelligence (AI) is a powerful tool in image analysis, data processing, and for establishing models. It has been widely applied in various medical fields, including AF. In this review, we focus on the progress and knowledge gap regarding the use of AI in AF patients and highlight its potential throughout the entire cycle of AF management, from detection to drug treatment. More evidence is needed to demonstrate its ability to improve prognosis through high-quality randomized controlled trials.
Collapse
Affiliation(s)
- Hanyang Liang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Han Zhang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Juan Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Xinghui Shao
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Shuang Wu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Siqi Lyu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Wei Xu
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Lulu Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Jiangshan Tan
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Jingyang Wang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| | - Yanmin Yang
- Emergency Center, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease of China, National Center for Cardiovascular Diseases, National Clinical Research Center of Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 100037 Beijing, China
| |
Collapse
|
2
|
Geurts S, Lu Z, Kavousi M. Perspectives on Sex- and Gender-Specific Prediction of New-Onset Atrial Fibrillation by Leveraging Big Data. Front Cardiovasc Med 2022; 9:886469. [PMID: 35898269 PMCID: PMC9309362 DOI: 10.3389/fcvm.2022.886469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 06/17/2022] [Indexed: 02/01/2023] Open
Abstract
Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, has a large impact on quality of life and is associated with increased risk of hospitalization, morbidity, and mortality. Over the past two decades advances regarding the clinical epidemiology and management of AF have been established. Moreover, sex differences in the prevalence, incidence, prediction, pathophysiology, and prognosis of AF have been identified. Nevertheless, AF remains to be a complex and heterogeneous disorder and a comprehensive sex- and gender-specific approach to predict new-onset AF is lacking. The exponential growth in various sources of big data such as electrocardiograms, electronic health records, and wearable devices, carries the potential to improve AF risk prediction. Leveraging these big data sources by artificial intelligence (AI)-enabled approaches, in particular in a sex- and gender-specific manner, could lead to substantial advancements in AF prediction and ultimately prevention. We highlight the current status, premise, and potential of big data to improve sex- and gender-specific prediction of new-onset AF.
Collapse
|
3
|
Huang YH, Lyle JV, Razak ASA, Nandi M, Marr CM, Huang CLH, Aston PJ, Jeevaratnam K. Detecting Paroxysmal Atrial Fibrillation from Normal Sinus Rhythm in Equine Athletes using Symmetric Projection Attractor Reconstruction and Machine Learning. CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:96-106. [PMID: 35493267 PMCID: PMC9043370 DOI: 10.1016/j.cvdhj.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
|
4
|
Radhakrishnan T, Karhade J, Ghosh SK, Muduli PR, Tripathy RK, Acharya UR. AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals. Comput Biol Med 2021; 137:104783. [PMID: 34481184 DOI: 10.1016/j.compbiomed.2021.104783] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/02/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022]
Abstract
Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is characterized by the heart's beating in an uncoordinated manner. In clinical studies, patients often do not have visible symptoms during AF, and hence it is harder to detect this cardiac ailment. Therefore, automated detection of AF using the electrocardiogram (ECG) signals can reduce the risk of stroke, coronary artery disease, and other cardiovascular complications. In this paper, a novel time-frequency domain deep learning-based approach is proposed to detect AF and classify terminating and non-terminating AF episodes using ECG signals. This approach involves evaluating the time-frequency representation (TFR) of ECG signals using the chirplet transform. The two-dimensional (2D) deep convolutional bidirectional long short-term memory (BLSTM) neural network model is used to detect and classify AF episodes using the time-frequency images of ECG signals. The proposed TFR based 2D deep learning approach is evaluated using the ECG signals from three public databases. Our developed approach has obtained an accuracy, sensitivity, and specificity of 99.18% (Confidence interval (CI) as [98.86, 99.49]), 99.17% (CI as [98.85 99.49]), and 99.18% (CI as [98.86 99.49]), respectively, with 10-fold cross-validation (CV) technique to detect AF automatically. The proposed approach also classified terminating and non-terminating AF episodes with an average accuracy of 75.86%. The average accuracy value obtained using the proposed approach is higher than the short-time Fourier transform (STFT), discrete-time continuous wavelet transform (DT-CWT), and Stockwell transform (ST) based time-frequency analysis methods with deep convolutional BLSTM models to detect AF. The proposed approach has better AF detection performance than the existing deep learning-based techniques using ECG signals from the MIT-BIH database.
Collapse
Affiliation(s)
- Tejas Radhakrishnan
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India
| | - Jay Karhade
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India
| | - S K Ghosh
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India
| | - P R Muduli
- Department of Electronics Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, 221005, India
| | - R K Tripathy
- Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore
| |
Collapse
|
5
|
A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia. SENSORS 2021; 21:s21155222. [PMID: 34372459 PMCID: PMC8348396 DOI: 10.3390/s21155222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 02/01/2023]
Abstract
Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.
Collapse
|
6
|
Olier I, Ortega-Martorell S, Pieroni M, Lip GYH. How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management. Cardiovasc Res 2021; 117:1700-1717. [PMID: 33982064 PMCID: PMC8477792 DOI: 10.1093/cvr/cvab169] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/11/2021] [Indexed: 02/01/2023] Open
Abstract
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.
Collapse
Affiliation(s)
- Ivan Olier
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Mark Pieroni
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK.,Liverpool Heart and Chest Hospital, Liverpool, UK
| |
Collapse
|
7
|
Alexeenko V, Howlett PJ, Fraser JA, Abasolo D, Han TS, Fluck DS, Fry CH, Jabr RI. Prediction of Paroxysmal Atrial Fibrillation From Complexity Analysis of the Sinus Rhythm ECG: A Retrospective Case/Control Pilot Study. Front Physiol 2021; 12:570705. [PMID: 33679427 PMCID: PMC7933455 DOI: 10.3389/fphys.2021.570705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 01/26/2021] [Indexed: 01/15/2023] Open
Abstract
Paroxysmal atrial fibrillation (PAF) is the most common cardiac arrhythmia, conveying a stroke risk comparable to persistent AF. It poses a significant diagnostic challenge given its intermittency and potential brevity, and absence of symptoms in most patients. This pilot study introduces a novel biomarker for early PAF detection, based upon analysis of sinus rhythm ECG waveform complexity. Sinus rhythm ECG recordings were made from 52 patients with (n = 28) or without (n = 24) a subsequent diagnosis of PAF. Subjects used a handheld ECG monitor to record 28-second periods, twice-daily for at least 3 weeks. Two independent ECG complexity indices were calculated using a Lempel-Ziv algorithm: R-wave interval variability (beat detection, BD) and complexity of the entire ECG waveform (threshold crossing, TC). TC, but not BD, complexity scores were significantly greater in PAF patients, but TC complexity alone did not identify satisfactorily individual PAF cases. However, a composite complexity score (h-score) based on within-patient BD and TC variability scores was devised. The h-score allowed correct identification of PAF patients with 85% sensitivity and 83% specificity. This powerful but simple approach to identify PAF sufferers from analysis of brief periods of sinus-rhythm ECGs using hand-held monitors should enable easy and low-cost screening for PAF with the potential to reduce stroke occurrence.
Collapse
Affiliation(s)
- Vadim Alexeenko
- Department of Biochemical Sciences, Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Surrey, United Kingdom
| | - Philippa J Howlett
- Department of Biochemical Sciences, Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Surrey, United Kingdom
| | - James A Fraser
- Department of Physiology, Faculty of Biology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom
| | - Daniel Abasolo
- Centre for Biomedical Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey, United Kingdom
| | - Thang S Han
- Department of Diabetes and Endocrinology, Ashford and St Peter's Hospitals NHS Foundation Trust, Ashford, United Kingdom
| | - David S Fluck
- Department of Cardiology, Ashford and St Peter's Hospitals NHS Foundation Trust, Ashford, United Kingdom
| | - Christopher H Fry
- School of Physiology, Pharmacology and Neuroscience, Faculty of Biomedical Sciences, University of Bristol, Bristol, United Kingdom
| | - Rita I Jabr
- Department of Biochemical Sciences, Faculty of Health and Medical Sciences, School of Biosciences and Medicine, University of Surrey, Surrey, United Kingdom
| |
Collapse
|
8
|
Wu X, Zheng Y, Chu CH, He Z. Extracting deep features from short ECG signals for early atrial fibrillation detection. Artif Intell Med 2020; 109:101896. [DOI: 10.1016/j.artmed.2020.101896] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 05/18/2020] [Accepted: 05/29/2020] [Indexed: 02/02/2023]
|
9
|
Zhang K, Aleexenko V, Jeevaratnam K. Computational approaches for detection of cardiac rhythm abnormalities: Are we there yet? J Electrocardiol 2020; 59:28-34. [PMID: 31954954 DOI: 10.1016/j.jelectrocard.2019.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 12/10/2019] [Accepted: 12/16/2019] [Indexed: 12/16/2022]
Abstract
The analysis of an electrocardiogram (ECG) is able to provide vital information on the electrical activity of the heart and is crucial for the accurate diagnosis of cardiac arrhythmias. Due to the nature of some arrhythmias, this might be a time-consuming and difficult to accomplish process. The advent of novel machine learning technologies in this field has a potential to revolutionise the use of the ECG. In this review, we outline key advances in ECG analysis for atrial, ventricular and complex multiform arrhythmias, as well as discuss the current limitations of the technology and the barriers that must be overcome before clinical integration is feasible.
Collapse
Affiliation(s)
- Kevin Zhang
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7AL, United Kingdom; School of Medicine, Imperial College London, United Kingdom
| | - Vadim Aleexenko
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7AL, United Kingdom
| | - Kamalan Jeevaratnam
- Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7AL, United Kingdom.
| |
Collapse
|