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
|
Matusik PS, Alomar O, Hussain MR, Akrmah M, Matusik PT, Chen DM, Alomar M, Stein PK. Heart Rate Variability and Coronary Artery Bypass Grafting: A Systematic Review. Rev Cardiovasc Med 2024; 25:36. [PMID: 39077663 PMCID: PMC11262371 DOI: 10.31083/j.rcm2501036] [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: 09/01/2023] [Revised: 10/16/2023] [Accepted: 10/18/2023] [Indexed: 07/31/2024] Open
Abstract
Background Coronary artery bypass grafting (CABG) is a well-established surgical procedure used to treat significant coronary artery disease. Nevertheless, unfavorable cardiovascular events and complications, including cardiac arrhythmias may be observed in patients after CABG. Previous studies have revealed a relationship between risk of cardiac arrhythmias and abnormal heart rate variability (HRV), which reflects adverse alterations in cardiac autonomic functioning, that may occur in patients after a CABG procedure. The aim of this article was to provide a systematic review of the major research findings in this area. Methods A literature search was carried out using PubMed, Cochrane, and Embase databases and relevant articles, published in English, were analyzed in detail. Results Studies performed so far have shown time depending changes in HRV after CABG. Time and frequency domain HRV decrease acutely after CABG but recover almost completely to pre-operative values by 6 months after surgery. Some preoperative clinical states such as: heart failure, type 2 diabetes mellitus and depression adversely affect post-CABG HRV. Finally, post-CABG cardiac rehabilitation appears to improve exercise capacity and speed up recovery of HRV. Conclusions Generally, traditional time and frequency domain HRV parameters fail to predict complications post-CABG. Altered non-linear measures of HRV may identify subgroups of subjects at increased risk of potential complications, including atrial fibrillation post-CABG. However, data available currently does not appear to unequivocally support the hypothesis that early HRV assessment in post-CABG patients predicts long-term mortality.
Collapse
Affiliation(s)
- Patrycja S. Matusik
- Chair of Radiology, Jagiellonian University Medical College and University Hospital, 30-688 Kraków, Poland
| | - Omar Alomar
- Heart Rate Variability Laboratory, Cardiovascular Division, Department of Medicine, Washington University School of Medicine in St. Louis, Saint Louis, MO 63130, USA
| | | | - Muhammad Akrmah
- Department of Pathology, Brigham and Women's Hospital, Boston, MA 02215, USA
| | - Paweł T. Matusik
- Department of Electrocardiology, Institute of Cardiology, Faculty of Medicine, Jagiellonian University Medical College, 31-202 Kraków, Poland
- Department of Electrocardiology, The John Paul II Hospital, 31-202 Kraków, Poland
| | - Daniel M. Chen
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Muhammed Alomar
- Heart Rate Variability Laboratory, Cardiovascular Division, Department of Medicine, Washington University School of Medicine in St. Louis, Saint Louis, MO 63130, USA
| | - Phyllis K. Stein
- Heart Rate Variability Laboratory, Cardiovascular Division, Department of Medicine, Washington University School of Medicine in St. Louis, Saint Louis, MO 63130, USA
| |
Collapse
|
3
|
Chen C, da Silva B, Yang C, Ma C, Li J, Liu C. AutoMLP: A Framework for the Acceleration of Multi-Layer Perceptron Models on FPGAs for Real-Time Atrial Fibrillation Disease Detection. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:1371-1386. [PMID: 37494158 DOI: 10.1109/tbcas.2023.3299084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Cardiovascular diseases are a leading cause of death globally, and atrial fibrillation (AF) is a common arrhythmia that affects many people. Detecting AF in real-time using hardware acceleration can prompt timely medical intervention. Multi-layer perceptron (MLP) has demonstrated the ability to detect AF accurately. However, implementing MLP on Field-Programmable Gate Array (FPGA) for real-time detection poses challenges due to the complex hardware design requirements. This study presents a novel framework for generating hardware accelerators to detect AF in real-time using MLP on FPGA. The framework automates evaluating MLP model topology, data type, and bit-widths to generate parallel acceleration. The generated solutions are evaluated using two AF datasets, PhysioNet MIT-BIH atrial fibrillation (AFDB) and China Physiological Signal Challenge 2018 (CPSC2018), regarding execution time, resource utilization, and accuracy. The evaluation results demonstrate that the hardware MLP can achieve a speedup higher than 1500× and around 25000× lower energy consumption than an embedded CPU. These satisfactory results prove the framework's suitability and convenience for the online detection of AF in an accelerated and automatic way through FPGA hardware implementation.
Collapse
|
4
|
Buś S, Jędrzejewski K, Guzik P. A New Approach to Detecting Atrial Fibrillation Using Count Statistics of Relative Changes between Consecutive RR Intervals. J Clin Med 2023; 12:jcm12020687. [PMID: 36675616 PMCID: PMC9865604 DOI: 10.3390/jcm12020687] [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: 12/20/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The ratio of the difference between neighboring RR intervals to the length of the preceding RR interval (x%) represents the relative change in the duration between two cardiac cycles. We investigated the diagnostic properties of the percentage of relative RR interval differences equal to or greater than x% (pRRx%) with x% in a range between 0.25% and 25% for the distinction of atrial fibrillation (AF) from sinus rhythm (SR). METHODS We used 1-min ECG segments with RR intervals with either AF (32,141 segments) or SR (32,769 segments) from the publicly available Physionet Long-Term Atrial Fibrillation Database (LTAFDB). The properties of pRRx% for different x% were analyzed using the statistical procedures and metrics commonly used to characterize diagnostic methods. RESULTS The distributions of pRRx% for AF and SR differ significantly over the whole studied range of x% from 0.25% to 25%, with particularly outstanding diagnostic properties for the x% range of 1.5% to 6%. However, pRR3.25% outperformed other pRRx%. Firstly, it had one of the highest and closest to perfect areas under the curve (0.971). For pRR3.25%, the optimal threshold for distinction AF from SR was set at 75.32%. Then, the accuracy was 95.44%, sensitivity was 97.16%, specificity was 93.76%, the positive predictive value was 93.85%, the negative predictive value was 97.11%, and the diagnostic odds ratio was 514. The excellent diagnostic properties of pRR3.25% were confirmed in the publicly available MIT-BIH Atrial Fibrillation Database. In a direct comparison, pRR3.25% outperformed the diagnostic properties of pRR31 (the percentage of successive RR intervals differing by at least 31 ms), i.e., so far, the best single parameter differentiating AF from SR. CONCLUSIONS A family of pRRx% parameters has excellent diagnostic properties for AF detection in a range of x% between 1.5% and 6%. However, pRR3.25% outperforms other pRRx% parameters and pRR31 (until now, probably the most robust single heart rate variability parameter for AF diagnosis). The exquisite pRRx% diagnostic properties for AF and its simple computation make it well-suited for AF detection in modern ECG technologies (mobile/wearable devices, biopatches) in long-term monitoring. The diagnostic properties of pRRx% deserve further exploration in other databases with AF.
Collapse
Affiliation(s)
- Szymon Buś
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
- Correspondence: ; Tel.: +48-22-2345883
| | - Konrad Jędrzejewski
- Institute of Electronic Systems, Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland
| | - Przemysław Guzik
- Department of Cardiology-Intensive Therapy and Internal Disease, Poznan University of Medical Sciences, 60-355 Poznan, Poland
| |
Collapse
|
5
|
Matusik PS, Matusik PT, Stein PK. Heart rate variability and heart rate patterns measured from wearable and implanted devices in screening for atrial fibrillation: potential clinical and population-wide applications. Eur Heart J 2022; 44:1105-1107. [PMID: 36527270 DOI: 10.1093/eurheartj/ehac546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Patrycja S Matusik
- University Hospital , Kraków , Poland
- Jagiellonian University Medical College , Kraków , Poland
| | - Paweł T Matusik
- Institute of Cardiology, Faculty of Medicine, Jagiellonian University Medical College , Kraków , Poland
- Department of Electrocardiology, The John Paul II Hospital , Kraków , Poland
| | - Phyllis K Stein
- Cardiovascular Division, Department of Medicine, Washington University School of Medicine , Saint Louis, MO 63110 , USA
| |
Collapse
|
6
|
Identifying Complex Emotions in Alexithymia Affected Adolescents Using Machine Learning Techniques. Diagnostics (Basel) 2022; 12:diagnostics12123188. [PMID: 36553197 PMCID: PMC9777297 DOI: 10.3390/diagnostics12123188] [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: 10/03/2022] [Revised: 10/30/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022] Open
Abstract
Many scientific researchers' study focuses on enhancing automated systems to identify emotions and thus relies on brain signals. This study focuses on how brain wave signals can be used to classify many emotional states of humans. Electroencephalography (EEG)-based affective computing predominantly focuses on emotion classification based on facial expression, speech recognition, and text-based recognition through multimodality stimuli. The proposed work aims to implement a methodology to identify and codify discrete complex emotions such as pleasure and grief in a rare psychological disorder known as alexithymia. This type of disorder is highly elicited in unstable, fragile countries such as South Sudan, Lebanon, and Mauritius. These countries are continuously affected by civil wars and disaster and politically unstable, leading to a very poor economy and education system. This study focuses on an adolescent age group dataset by recording physiological data when emotion is exhibited in a multimodal virtual environment. We decocted time frequency analysis and amplitude time series correlates including frontal alpha symmetry using a complex Morlet wavelet. For data visualization, we used the UMAP technique to obtain a clear district view of emotions. We performed 5-fold cross validation along with 1 s window subjective classification on the dataset. We opted for traditional machine learning techniques to identify complex emotion labeling.
Collapse
|
7
|
Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection. J Clin Med 2022; 11:jcm11195702. [PMID: 36233572 PMCID: PMC9572524 DOI: 10.3390/jcm11195702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/22/2022] [Accepted: 09/23/2022] [Indexed: 11/30/2022] Open
Abstract
Background: We studied the diagnostic properties of the percentage of successive RR intervals differing by at least x ms (pRRx) as functions of the threshold value x in a range of 7 to 195 ms for the differentiation of atrial fibrillation (AF) from sinus rhythm (SR). Methods: RR intervals were measured in 60-s electrocardiogram (ECG) segments with either AF (32,141 segments) or SR (32,769 segments) from the publicly available Physionet Long-Term Atrial Fibrillation Database (LTAFDB). For validation, we have used ECGs from the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH) Atrial Fibrillation Database. The pRRx distributions in AF and SR in relation to x were studied by histograms, along with the mutual association by the nonparametric Spearman correlations for all pairs of pRRx, and separately for AF or SR. The optimal cutoff values for all pRRx were determined using the receiver operator curve characteristic. A nonparametric bootstrap with 5000 samples was used to calculate a 95% confidence interval for several classification metrics. Results: The distributions of pRRx for x in the 7–195 ms range are significantly different in AF than in SR. The sensitivity, specificity, accuracy, and diagnostic odds ratios differ for pRRx, with the highest values for x = 31 ms (pRR31) rather than x = 50 (pRR50), which is most commonly applied in studies on heart rate variability. For the optimal cutoff of pRR31 (68.79%), the sensitivity is 90.42%, specificity 95.37%, and the diagnostic odds ratio is 194.11. Validation with the ECGs from the MIT–BIH Atrial Fibrillation Database confirmed our findings. Conclusions: We demonstrate that the diagnostic properties of pRRx depend on x, and pRR31 outperforms pRR50, at least for ECGs of 60-s duration.
Collapse
|