AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions
Abstract
:1. Introduction
2. Basic Concepts of Artificial Intelligence and Machine Learning for Healthcare
2.1. Introduction to AI
- Narrow AI (Weak AI): Designed to perform a narrow task, such as facial recognition or internet searches. It operates under a limited set of constraints and cannot perform tasks outside its specific domain.
- General AI (Strong AI): Possesses the ability to understand, learn, and apply knowledge in a way that is indistinguishable from human intelligence. General AI remains largely theoretical and is not yet realized [10].
- Artificial Superintelligence (AS): An intelligence that surpasses human intelligence in all aspects, including creativity, general wisdom, and problem-solving. This type is also theoretical and a topic of debate and research in AI ethics and safety [11].
- In the medical field, narrow AI is primarily used to assist with tasks such as diagnostic imaging, patient data analysis, and personalized treatment recommendations due to its ability to perform specific, well-defined tasks with high accuracy and efficiency. General AI is still in the developmental stage and AS is a topic of ongoing debate and research in AI ethics and safety.
- Machine learning (ML): Involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed [12].
- Deep learning (DL): Uses neural networks (NN) with many layers (deep networks) to model complex patterns in data. DL has been particularly successful in image and speech recognition tasks [13].
- Natural language processing (NLP): Enables machines to understand and interact using human language. Applications include language translation, sentiment analysis, and chatbots [13].
- Robotics: Combines AI with mechanical engineering to create robots capable of performing tasks autonomously or semi-autonomously, often in complex and dynamic environments [12].
2.2. Introduction to Machine Learning
- Supervised learning: This approach uses labeled data to train the algorithm. Each input comes with a corresponding output label, which helps the algorithm learn the relationship between inputs and outputs [15]. It is commonly used in predictive analytics, such as forecasting heart disease from patient data.
- Unsupervised learning: In this method, the algorithm is given data without explicit instructions on what to do with them [8]. It must find patterns and relationships within the data on its own. This technique is often used for clustering patients based on symptoms or for identifying unknown correlations in medical data.
- Semi-supervised learning: Combines both labeled and unlabeled data during training. This method is useful when acquiring a fully labeled dataset is impractical or expensive [8]. It leverages the small amount of labeled data to better understand and label the large amount of unlabeled data.
- Reinforcement learning: This technique involves training algorithms to make a sequence of decisions by rewarding them for desirable behaviors and penalizing them for undesirable ones. It is particularly useful in optimizing treatment protocols where the algorithm learns the best strategies through trial and error [15].
Description of ML Algorithms Explored in This Article
3. AI and ML in Healthcare and Cardiology: AI-ECG for Heart Diseases
3.1. Risk Prediction in Heart Diseases
3.1.1. Risk Prediction of Arrhythmias
3.1.2. Heart Failure
3.1.3. Pulmonary Hypertension
3.1.4. Mortality Prediction
3.1.5. Cardiomyopathy
3.1.6. Chemotherapy Cardiotoxicity (Cardio-Oncology)
3.2. Diagnosis in Heart Diseases
3.2.1. Diagnosis of Arrhythmias
3.2.2. Mitral Regurgitation
3.2.3. Hypertension
3.2.4. Pulmonary Embolism
3.2.5. Myocardial Infarction
3.2.6. Cardiac Masses in Cardio-Oncology
3.3. Chapter Summary and Conclusions
4. AI Applications in COVID-19 and Cardiology
4.1. Impact of COVID-19 in Cardiovascular Health
- Lifestyle changes: Lockdowns and social distancing measures led to reduced physical activity, poor diet, and increased stress, all of which are detrimental to cardiovascular health.
- Mental health: The psychological impacts of the pandemic, including anxiety and depression, further contributed to cardiovascular risks.
- COVID-19 infection: The virus itself has been shown to have direct cardiovascular effects, including myocarditis, arrhythmias, and thromboembolic events. These complications posed significant risks, particularly for patients with pre-existing cardiovascular conditions.
4.2. AI in COVID-19: Role of Diagnosis, Monitoring and Risk Assessment
4.3. Risk Prediction in the COVID-19 Pandemic
4.3.1. Complication Prediction Associated with COVID-19
4.3.2. Mortality Risk Prediction
4.4. Diagnosis in the COVID-19 Pandemic
4.4.1. COVID-19 Diagnosis Using AI-ECG
4.4.2. Diagnosis of Complications Associated with COVID-19
4.5. Challenges, Ethical and Societal Implications
4.5.1. Challenges
4.5.2. Ethical Considerations
4.5.3. Societal Implications
5. Discussions on Future Directions and Challenges
5.1. Emerging Trends, Technical Challenges, and Research Needs
5.2. Healthcare System Readiness
5.3. Collaboration and Innovation
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
AdvRS&LP | advanced repeated structuring and learning procedures |
AF | atrial fibrillation |
AI | artificial intelligence |
AI-ECG | AI-enhanced electrocardiograms |
AMI | acute myocardial infarction |
AUC | area under the curve |
CNN | convolutional neural networks |
COVID-19 | coronavirus disease 2019 |
DNN | dense neural networks |
DL | deep learning |
ECG | electrocardiography |
EfficientNet | a family of convolutional neural networks optimized for accuracy and efficiency |
EF | ejection fraction |
FDA | U.S. Food and Drug Administration |
HBP | high blood pressure |
HCWs | healthcare workers |
HCM | hypertrophic cardiomyopathy |
HRV | heart rate variability |
KNN | K-Nearest Neighbors |
LSTM | long short-term memory |
LV | left ventricle |
MHPT | masked hypertension |
MI | myocardial infarction |
ML | machine learning |
NLP | natural language processing |
NN | neural networks |
PE | pulmonary embolism |
PH | pulmonary hypertension |
RNN | recurrent neural networks |
SCD | sudden cardiac death |
STEMI | ST-segment elevation acute myocardial infarction |
VCG | vectorcardiography |
References
- Martínez-Sellés, M.; Marina-Breysse, M. Current and Future Use of Artificial Intelligence in Electrocardiography. J. Cardiovasc. Dev. Dis. 2023, 10, 175. [Google Scholar] [CrossRef] [PubMed]
- Siontis, K.C.; Noseworthy, P.A.; Attia, Z.I.; Friedman, P.A. Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Disease Management. Nat. Rev. Cardiol. 2021, 18, 465–478. [Google Scholar] [CrossRef]
- Muzammil, M.A.; Javid, S.; Afridi, A.K.; Siddineni, R.; Shahabi, M.; Haseeb, M.; Fariha, F.N.U.; Kumar, S.; Zaveri, S.; Nashwan, A.J. Artificial Intelligence-Enhanced Electrocardiography for Accurate Diagnosis and Management of Cardiovascular Diseases. J. Electrocardiol. 2024, 83, 30–40. [Google Scholar] [CrossRef] [PubMed]
- Baek, Y.-S. The Emergence and Clinical Significance of Artificial Intelligence–Enhanced Electrocardiography. Cardiovasc. Prev. Pharmacother. 2024, 6, 41–47. [Google Scholar] [CrossRef]
- Hannun, A.Y.; Rajpurkar, P.; Haghpanahi, M.; Tison, G.H.; Bourn, C.; Turakhia, M.P.; Ng, A.Y. Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network. Nat. Med. 2019, 25, 65–69. [Google Scholar] [CrossRef]
- Baek, Y.-S.; Jo, Y.; Lee, S.-C.; Choi, W.; Kim, D.-H. Artificial Intelligence-Enhanced Electrocardiography for Early Assessment of Coronavirus Disease 2019 Severity. Sci. Rep. 2023, 13, 15187. [Google Scholar] [CrossRef]
- Sridhar, A.R.; Chen (Amber), Z.-H.; Mayfield, J.J.; Fohner, A.E.; Arvanitis, P.; Atkinson, S.; Braunschweig, F.; Chatterjee, N.A.; Zamponi, A.F.; Johnson, G.; et al. Identifying Risk of Adverse Outcomes in COVID-19 Patients via Artificial Intelligence–Powered Analysis of 12-Lead Intake Electrocardiogram. Cardiovasc. Digit. Health J. 2022, 3, 62–74. [Google Scholar] [CrossRef]
- Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput. Sci. 2021, 2, 160. [Google Scholar] [CrossRef]
- Rimmer, L.; Howard, C.; Picca, L.; Bashir, M. The Automaton as a Surgeon: The Future of Artificial Intelligence in Emergency and General Surgery. Eur. J. Trauma Emerg. Surg. 2021, 47, 757–762. [Google Scholar] [CrossRef]
- Baum, S.D. A Survey of Artificial General Intelligence Projects for Ethics, Risk, and Policy. SSRN J. 2017, 17-1, 57. [Google Scholar] [CrossRef]
- Narain, K.; Swami, A.; Srivastava, A.; Swami, S. Evolution and Control of Artificial Superintelligence (ASI): A Management Perspective. J. Adv. Manag. Res. 2019, 16, 698–714. [Google Scholar] [CrossRef]
- Tufail, S.; Riggs, H.; Tariq, M.; Sarwat, A.I. Advancements and Challenges in Machine Learning: A Comprehensive Review of Models, Libraries, Applications, and Algorithms. Electronics 2023, 12, 1789. [Google Scholar] [CrossRef]
- Janiesch, C.; Zschech, P.; Heinrich, K. Machine Learning and Deep Learning. Electronics 2021, 31, 685–695. [Google Scholar] [CrossRef]
- Schmidgall, S.; Ziaei, R.; Achterberg, J.; Kirsch, L.; Hajiseyedrazi, S.P.; Eshraghian, J. Brain-Inspired Learning in Artificial Neural Networks: A Review. APL Mach. Learn. 2024, 2, 021501. [Google Scholar] [CrossRef]
- Tangirala, S. Predictive Modeling in Health Care Data Analytics: A Sustainable Supervised Learning Technique. In Big Data Analytics and Intelligence: A Perspective for Health Care; Tanwar, P., Jain, V., Liu, C.-M., Goyal, V., Eds.; Emerald Publishing Limited: Leeds, UK, 2020; pp. 263–280. [Google Scholar] [CrossRef]
- Bonaccorso, G. Machine Learning Algorithms: A Reference Guide to Popular Algorithms for Data Science and Machine Learning; Packt Publishing: Birmingham, UK; Mumbai, India, 2017; ISBN 9781785889622. [Google Scholar]
- Baqal, O.; Habib, E.A.; Hasabo, E.A.; Galasso, F.; Barry, T.; Arsanjani, R.; Sweeney, J.P.; Noseworthy, P.; David Fortuin, F. Artificial Intelligence-Enabled Electrocardiogram (AI-ECG) Does Not Predict Atrial Fibrillation Following Patent Foramen Ovale Closure. IJC Heart Vasc. 2024, 51, 101361. [Google Scholar] [CrossRef] [PubMed]
- Choi, J.; Kim, J.Y.; Cho, M.S.; Kim, M.; Kim, J.; Oh, I.-Y.; Cho, Y.; Lee, J.H. Artificial Intelligence Predicts Undiagnosed Atrial Fibrillation in Patients with Embolic Stroke of Undetermined Source Using Sinus Rhythm Electrocardiograms. Heart Rhythm 2024, S1547527124002741. [Google Scholar] [CrossRef] [PubMed]
- Pipilas, D.; Friedman, S.F.; Khurshid, S. The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation. Curr. Cardiol. Rep. 2023, 25, 381–389. [Google Scholar] [CrossRef]
- Raghunath, S.; Pfeifer, J.M.; Kelsey, C.R.; Nemani, A.; Ruhl, J.A.; Hartzel, D.N.; Ulloa Cerna, A.E.; Jing, L.; van Maanen, D.P.; Leader, J.B.; et al. An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk. J. Electrocardiol. 2023, 76, 61–65. [Google Scholar] [CrossRef] [PubMed]
- Raghunath, A.; Nguyen, D.D.; Schram, M.; Albert, D.; Gollakota, S.; Shapiro, L.; Sridhar, A.R. Artificial intelligence-enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation. Cardiovasc. Digit. Health J. 2023, 4, 21–28. [Google Scholar] [CrossRef]
- Baj, G.; Gandin, I.; Scagnetto, A.; Bortolussi, L.; Cappelletto, C.; Di Lenarda, A.; Barbati, G. Comparison of discrimination and calibration performance of ECG-based machine learning models for prediction of new-onset atrial fibrillation. BMC Med. Res. Methodol. 2023, 23, 169. [Google Scholar] [CrossRef]
- Jiang, J.; Deng, H.; Liao, H.; Fang, X.; Zhan, X.; Wei, W.; Wu, S.; Xue, Y. An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation. J. Clin. Med. 2023, 12, 1933. [Google Scholar] [CrossRef]
- Bai, Y.; Wang, Z.Z.; Zhang, G.G.; Guo, S.D.; Rivera-Caravaca, J.M.; Wang, Y.L.; Jin, Y.Y.; Liu, Y. Validating scores predicting atrial fibrillation recurrence post catheter ablation in patients with concurrent atrial fibrillation and pulmonary diseases. Ann. Palliat. Med. 2021, 10, 4299–4307. [Google Scholar] [CrossRef] [PubMed]
- Raghunath, S.; Pfeifer, J.M.; Ulloa-Cerna, A.E.; Nemani, A.; Carbonati, T.; Jing, L.; vanMaanen, D.P.; Hartzel, D.N.; Ruhl, J.A.; Lagerman, B.F.; et al. Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke. Circulation 2021, 143, 1287–1298. [Google Scholar] [CrossRef]
- Holmstrom, L.; Chugh, H.; Nakamura, K.; Bhanji, Z.; Seifer, M.; Uy-Evanado, A.; Reinier, K.; Ouyang, D.; Chugh, S.S. An ECG-based artificial intelligence model for assessment of sudden cardiac death risk. Commun. Med. 2024, 4, 17. [Google Scholar] [CrossRef]
- Grün, D.; Rudolph, F.; Gumpfer, N.; Hannig, J.; Elsner, L.K.; von Jeinsen, B.; Hamm, C.W.; Rieth, A.; Guckert, M.; Keller, T. Identifying Heart Failure in ECG Data With Artificial Intelligence-A Meta-Analysis. Front. Digit. Health 2021, 2, 584555. [Google Scholar] [CrossRef]
- Akbilgic, O.; Butler, L.; Karabayir, I.; Chang, P.P.; Kitzman, D.W.; Alonso, A.; Chen, L.Y.; Soliman, E.Z. ECG-AI: Electrocardiographic Artificial Intelligence Model for Prediction of Heart Failure. Eur. Heart J. Digit. Health 2021, 2, 626–634. [Google Scholar] [CrossRef] [PubMed]
- Kwon, J.M.; Kim, K.H.; Medina-Inojosa, J.; Jeon, K.H.; Park, J.; Oh, B.H. Artificial Intelligence for Early Prediction of Pulmonary Hypertension Using Electrocardiography. J. Heart Lung Transplant. 2020, 39, 805–814. [Google Scholar] [CrossRef] [PubMed]
- Tsai, D.J.; Lou, Y.S.; Lin, C.S.; Fang, W.H.; Lee, C.C.; Ho, C.L.; Wang, C.H.; Lin, C. Mortality Risk Prediction of the Electrocardiogram as an Informative Indicator of Cardiovascular Diseases. Digit. Health 2023, 9, 20552076231187247. [Google Scholar] [CrossRef] [PubMed]
- Baek, Y.S.; Lee, D.H.; Jo, Y.; Lee, S.C.; Choi, W.; Kim, D.H. Artificial Intelligence-Estimated Biological Heart Age Using a 12-Lead Electrocardiogram Predicts Mortality and Cardiovascular Outcomes. Front. Cardiovasc. Med. 2023, 10, 1137892. [Google Scholar] [CrossRef]
- Shiraishi, Y.; Goto, S.; Niimi, N.; Katsumata, Y.; Goda, A.; Takei, M.; Saji, M.; Sano, M.; Fukuda, K.; Kohno, T.; et al. Improved Prediction of Sudden Cardiac Death in Patients with Heart Failure through Digital Processing of Electrocardiography. Europace 2023, 25, 922–930. [Google Scholar] [CrossRef]
- Liu, C.M.; Shih, E.S.C.; Chen, J.Y.; Huang, C.H.; Wu, I.C.; Chen, P.F.; Higa, S.; Yagi, N.; Hu, Y.F.; Hwang, M.J.; et al. Artificial Intelligence-Enabled Electrocardiogram Improves the Diagnosis and Prediction of Mortality in Patients with Pulmonary Hypertension. JACC Asia 2022, 2, 258–270. [Google Scholar] [CrossRef] [PubMed]
- Güntürkün, F.; Akbilgic, O.; Davis, R.L.; Armstrong, G.T.; Howell, R.M.; Jefferies, J.L.; Ness, K.K.; Karabayir, I.; Lucas, J.T., Jr.; Srivastava, D.K.; et al. Artificial Intelligence-Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors. JCO Clin. Cancer Inform. 2021, 5, 459–468. [Google Scholar] [CrossRef]
- Ko, W.Y.; Siontis, K.C.; Attia, Z.I.; Carter, R.E.; Kapa, S.; Ommen, S.R.; Demuth, S.J.; Ackerman, M.J.; Gersh, B.J.; Arruda-Olson, A.M.; et al. Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram. J. Am. Coll. Cardiol. 2020, 75, 722–733. [Google Scholar] [CrossRef]
- Jacobs, J.E.J.; Greason, G.; Mangold, K.E.; Wildiers, H.; Willems, R.; Janssens, S.; Noseworthy, P.; Lopez-Jimenez, F.; Voigt, J.-U.; Friedman, P.; et al. Artificial Intelligence Electrocardiogram as a Novel Screening Tool to Detect a Newly Abnormal Left Ventricular Ejection Fraction after Anthracycline-Based Cancer Therapy. Eur. J. Prev. Cardiol. 2024, 31, 560–566. [Google Scholar] [CrossRef] [PubMed]
- Yagi, R.; Goto, S.; Himeno, Y.; Katsumata, Y.; Hashimoto, M.; MacRae, C.A.; Deo, R.C. Artificial Intelligence Prediction of Chemotherapy-Induced Cardiotoxicity from Baseline Electrocardiograms. Nat. Commun. 2024, 15, 2536. [Google Scholar] [CrossRef] [PubMed]
- Halasz, G.; Mazzacuva, P.; Mistrulli, R. Artificial Intelligence-Assisted Electrocardiography: A New and Easily Accessible Approach for Diagnosing Cancer Therapy-Related Cardiac Dysfunction. Eur. J. Prev. Cardiol. 2024, 31, 557–559. [Google Scholar] [CrossRef]
- Baek, Y.S.; Kwon, S.; You, S.C.; Lee, K.N.; Yu, H.T.; Lee, S.R.; Roh, S.Y.; Kim, D.H.; Shin, S.Y.; Lee, D.I.; et al. Artificial Intelligence-Enhanced 12-Lead Electrocardiography for Identifying Atrial Fibrillation During Sinus Rhythm (AIAFib) Trial: Protocol for a Multicenter Retrospective Study. Front. Cardiovasc. Med. 2023, 10, 1258167. [Google Scholar] [CrossRef]
- Baek, Y.S.; Lee, S.C.; Choi, W.; Kim, D.H. A New Deep Learning Algorithm of 12-Lead Electrocardiogram for Identifying Atrial Fibrillation During Sinus Rhythm. Sci. Rep. 2021, 11, 12818. [Google Scholar] [CrossRef]
- Sabut, S.; Pandey, O.; Mishra, B.S.P.; Mohanty, M. Detection of Ventricular Arrhythmia Using Hybrid Time-Frequency-Based Features and Deep Neural Network. Phys. Eng. Sci. Med. 2021, 44, 135–145. [Google Scholar] [CrossRef]
- Bos, J.M.; Attia, Z.I.; Albert, D.E.; Noseworthy, P.A.; Friedman, P.A.; Ackerman, M.J. Use of Artificial Intelligence and Deep Neural Networks in Evaluation of Patients With Electrocardiographically Concealed Long QT Syndrome From the Surface 12-Lead Electrocardiogram. JAMA Cardiol. 2021, 6, 532–538. [Google Scholar] [CrossRef]
- Kwon, J.M.; Kim, K.H.; Akkus, Z.; Jeon, K.H.; Park, J.; Oh, B.H. Artificial Intelligence for Detecting Mitral Regurgitation Using Electrocardiography. J. Electrocardiol. 2020, 59, 151–157. [Google Scholar] [CrossRef]
- Soh, D.C.K.; Ng, E.Y.K.; Jahmunah, V.; Oh, S.L.; San, T.R.; Acharya, U.R. A computational intelligence tool for the detection of hypertension using empirical mode decomposition. Comput. Biol. Med. 2020, 118, 103630. [Google Scholar] [CrossRef]
- Valente Silva, B.; Marques, J.; Nobre Menezes, M.; Oliveira, A.L.; Pinto, F.J. Artificial Intelligence-Based Diagnosis of Acute Pulmonary Embolism: Development of a Machine Learning Model Using 12-Lead Electrocardiogram. Rev. Port. Cardiol. 2023, 42, 643–651. [Google Scholar] [CrossRef]
- Liu, W.C.; Lin, C.S.; Tsai, C.S.; Tsao, T.P.; Cheng, C.C.; Liou, J.T.; Lin, W.S.; Cheng, S.M.; Lou, Y.S.; Lee, C.C.; et al. A Deep Learning Algorithm for Detecting Acute Myocardial Infarction. EuroIntervention 2021, 17, 765–773. [Google Scholar] [CrossRef] [PubMed]
- Liu, W.C.; Lin, C.; Lin, C.S.; Tsai, M.C.; Chen, S.J.; Tsai, S.H.; Lin, W.S.; Lee, C.C.; Tsao, T.P.; Cheng, C.C. An Artificial Intelligence-Based Alarm Strategy Facilitates Management of Acute Myocardial Infarction. J. Pers. Med. 2021, 11, 1149. [Google Scholar] [CrossRef] [PubMed]
- Cho, Y.; Kwon, J.M.; Kim, K.H.; Medina-Inojosa, J.R.; Jeon, K.H.; Cho, S.; Lee, S.Y.; Park, J.; Oh, B.H. Artificial Intelligence Algorithm for Detecting Myocardial Infarction Using Six-Lead Electrocardiography. Sci. Rep. 2020, 10, 20495. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Y.; Xiong, J.; Hou, Y.; Zhu, M.; Lu, Y.; Xu, Y.; Teliewubai, J.; Liu, W.; Xu, X.; Li, X.; et al. Early Detection of ST-Segment Elevated Myocardial Infarction by Artificial Intelligence with 12-Lead Electrocardiogram. Int. J. Cardiol. 2020, 317, 223–230. [Google Scholar] [CrossRef]
- Oikonomou, E.K.; Sangha, V.; Dhingra, L.S.; Aminorroaya, A.; Coppi, A.; Krumholz, H.M.; Baldassarre, L.A.; Khera, R. Artificial Intelligence-Enhanced Risk Stratification of Cancer Therapeutics-Related Cardiac Dysfunction Using Electrocardiographic Images. medRxiv 2024. [Google Scholar] [CrossRef]
- Martinez, D.S.; Noseworthy, P.A.; Akbilgic, O.; Herrmann, J.; Ruddy, K.J.; Hamid, A.; Maddula, R.; Singh, A.; Davis, R.; Gunturkun, F.; et al. Artificial Intelligence Opportunities in Cardio-Oncology: Overview with Spotlight on Electrocardiography. Am. Heart J. Plus 2022, 15, 100129. [Google Scholar] [CrossRef]
- Nawaz, M.; Nazir, T.; Javed, A.; Malik, K.M.; Saudagar, A.K.J.; Khan, M.B.; Abul Hasanat, M.H.; AlTameem, A.; AlKhathami, M. Efficient-ECGNet Framework for COVID-19 Classification and Correlation Prediction with Cardio Disease Through Electrocardiogram Medical Imaging. Front. Med. 2022, 9, 1005920. [Google Scholar] [CrossRef]
- Gupta, S.; Rathee, A.; Agarwal, M.; Gupta, N. Social Distance Monitoring and Infection Risk Assessment in COVID-19 Pandemic. In Communication, Networks and Computing; Tomar, R.S., Verma, S., Chaurasia, B.K., Singh, V., Abawajy, J.H., Akashe, S., Hsiung, P.-A., Prasad, R., Eds.; Communications in Computer and Information Science; Springer Nature: Cham, Switzerland, 2023; Volume 1894, pp. 159–170. [Google Scholar] [CrossRef]
- Baek, Y.; Jo, Y.; Lee, D.H.; Lee, S.C.; Choi, W.I.; Kim, D.H. Artificial intelligence-enhanced electrocardiography for predicting severity prognosis in patients with COVID-19. Europace 2023, 25 (Suppl. S1), euad122.539. [Google Scholar] [CrossRef]
- Gupta, M.D.; Jha, M.K.; Bansal, A.; Yadav, R.; Ramakrishanan, S.; Girish, M.P.; Sarkar, P.G.; Qamar, A.; Kumar, S.; Kumar, S.; et al. COVID 19-related burnout among healthcare workers in India and ECG based predictive machine learning model: Insights from the BRUCEE- Li study. Indian Heart J. 2021, 73, 674–681. [Google Scholar] [CrossRef]
- Sbrollini, A.; Leoni, C.; Morettini, M.; Rivolta, M.W.; Swenne, C.A.; Mainardi, L.; Burattini, L.; Sassi, R. Identification of Electrocardiographic Patterns Related to Mortality with COVID-19. Appl. Sci. 2024, 14, 817. [Google Scholar] [CrossRef]
- van de Leur, R.R.; Bleijendaal, H.; Taha, K.; Mast, T.; Gho, J.M.I.H.; Linschoten, M.; van Rees, B.; Henkens, M.T.H.M.; Heymans, S.; Sturkenboom, N.; et al. Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning. Neth. Heart J. 2022, 30, 312–318. [Google Scholar] [CrossRef]
- Sakr, A.S.; Pławiak, P.; Tadeusiewicz, R.; Pławiak, J.; Sakr, M.; Hammad, M. ECG-COVID: An end-to-end deep model based on electrocardiogram for COVID-19 detection. Inf. Sci. 2023, 619, 324–339. [Google Scholar] [CrossRef] [PubMed]
- Agrawal, A.; Chauhan, A.; Shetty, M.K.; Girish, M.P.; Gupta, M.D.; Gupta, A. ECG-iCOVIDNet: Interpretable AI model to identify changes in the ECG signals of post-COVID subjects. Comput. Biol. Med. 2022, 146, 105540. [Google Scholar] [CrossRef]
- Irmak, E. COVID-19 disease diagnosis from paper-based ECG trace image data using a novel convolutional neural network model. Phys. Eng. Sci. Med. 2022, 45, 167–179. [Google Scholar] [CrossRef]
- Nguyen, T.; Pham, H.H.; Le, K.H.; Nguyen, A.T.; Thanh, T.; Do, C. Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks. PLoS ONE 2022, 17, e0277081. [Google Scholar] [CrossRef]
- Rahman, T.; Akinbi, A.; Chowdhury, M.E.H.; Rashid, T.A.; Şengür, A.; Khandakar, A.; Islam, K.R.; Ismael, A.M. COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network. Health Inf. Sci. Syst. 2022, 10, 1. [Google Scholar] [CrossRef]
- Sobahi, N.; Sengur, A.; Tan, R.S.; Acharya, U.R. Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection. Comput. Biol. Med. 2022, 143, 105335. [Google Scholar] [CrossRef]
- Attia, Z.I.; Kapa, S.; Dugan, J.; Pereira, N.; Noseworthy, P.A.; Jimenez, F.L.; Cruz, J.; Carter, R.E.; DeSimone, D.C.; Signorino, J.; et al. Rapid Exclusion of COVID Infection with the Artificial Intelligence Electrocardiogram. Mayo Clin. Proc. 2021, 96, 2081–2094. [Google Scholar] [CrossRef]
- Ozdemir, M.A.; Ozdemir, G.D.; Guren, O. Classification of COVID-19 electrocardiograms by using hexaxial feature mapping and deep learning. BMC Med. Inform. Decis. Mak. 2021, 21, 170. [Google Scholar] [CrossRef] [PubMed]
- Gomes, J.C.; de Santana, M.A.; Masood, A.I.; de Lima, C.L.; Dos Santos, W.P. COVID-19’s influence on cardiac function: A machine learning perspective on ECG analysis. Med. Biol. Eng. Comput. 2023, 61, 1057–1081. [Google Scholar] [CrossRef] [PubMed]
- Bassiouni, M.M.; Hegazy, I.; Rizk, N.; El-Dahshan, E.A.; Salem, A.M. Automated Detection of COVID-19 Using Deep Learning Approaches with Paper-Based ECG Reports. Circuits Syst. Signal Process. 2022, 41, 5535–5577. [Google Scholar] [CrossRef] [PubMed]
- Prashant, K.; Choudhary, P.; Agrawal, T.; Kaushik, E. OWAE-Net: COVID-19 detection from ECG images using deep learning and optimized weighted average ensemble technique. Intell. Syst. Appl. 2022, 16, 200154. [Google Scholar] [CrossRef]
- Attallah, O. An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques. Biosensors 2022, 12, 299. [Google Scholar] [CrossRef]
- Gupta, M.D.; Bansal, A.; Sarkar, P.G.; Girish, M.P.; Jha, M.; Yusuf, J.; Kumar, S.; Kumar, S.; Jain, A.; Kathuria, S.; et al. Design and rationale of an intelligent algorithm to detect BuRnoUt in HeaLthcare workers in COVID era using ECG and artificiaL intelligence: The BRUCEE-LI study. Indian Heart J. 2021, 73, 109–113. [Google Scholar] [CrossRef]
- Attia, Z.I.; Kapa, S.; Noseworthy, P.A.; Lopez-Jimenez, F.; Friedman, P.A. Artificial Intelligence ECG to Detect Left Ventricular Dysfunction in COVID-19: A Case Series. Mayo Clin. Proc. 2020, 95, 2464–2466. [Google Scholar] [CrossRef]
Algorithm | Key Characteristics | Use Cases | Advantages | Disadvantages |
---|---|---|---|---|
Convolutional Neural Networks (CNNs) | Specialized for processing grid-like data such as images, extensively used in medical image analysis | Predicting and diagnosing arrhythmias, heart failure, myocardial infarction, and COVID-19 complications | High accuracy in image and pattern recognition, effective for complex data | Require large datasets, computationally intensive |
Recurrent Neural Networks (RNNs) | Effective for sequential data like time series or heart rate data, identifies patterns over time | Heart failure risk assessment, time series analysis | Capture temporal patterns, suitable for sequential data | Computationally intensive, require large datasets. |
Autoencoders | Used for unsupervised learning tasks, anomaly detection in ECG data | Anomaly detection in ECG data | Effective for unsupervised learning, detect anomalies | Limited to unsupervised tasks, may not generalize well |
Dense Neural Networks (DNNs) | Traditional neural networks with full connectivity, versatile for various predictive tasks | Various predictive tasks in medical data analysis | Versatile, used for a wide range of predictive tasks | Computationally intensive, require large datasets |
K-Nearest Neighbors (KNN) | Simple, non-parametric, classifies based on the majority class among nearest neighbors | Hypertension detection, hospital diagnoses, continuous monitoring | Simple, easy to implement, effective for small datasets | Less effective for large datasets, may not handle complex relationships well |
Extra Tree Classifier | Robust feature extraction and prediction, handles complex interactions | Predicting burnout among healthcare workers | Handles complex interactions, robust prediction | Computationally intensive, requires large datasets |
Support Vector Machines (SVMs) | Used for classification and regression, combines multiple predictors for enhanced accuracy | Enhancing diagnostic accuracy in combination with CNNs | High accuracy, interpretable, robust to overfitting | Require careful tuning of parameters, may not handle temporal data effectively |
Ensemble Learning Random Forest, XGBoost | Combines strengths of different algorithms, robust to overfitting, handles missing data well | Robust prediction models for cardiovascular diseases | Handles missing data well, robust predictions | Computationally intensive, requires large datasets |
Hybrid Models | Combine multiple algorithms, enhance robustness and interpretability | Combining CNNs with SVMs or ensemble methods for improved diagnostics | Combine strengths of various models, highly robust and interpretable | Complex implementation, require large datasets |
Affections | Method | First Author | Brief Description of the Paper Topic |
---|---|---|---|
Arrythmias | ML | Baqal O. (2024) [17] | Identifying AF in normal sinus rhythm: AI-ECG can detect high-risk ECG features invisible to the human eye. |
Choi J. (2024) [18] | Effectiveness of AI in predicting AF: AI improves patient prognosis through prompt interventions. | ||
Pipilas D. (2023) [19] | Prioritization of preventive interventions: AI prioritizes individuals at high risk of AF for preventive measures. | ||
Raghunath S. (2023) [20] | Predicting new-onset atrial AF: AI helps in predicting AF from sinus rhythm ECGs. | ||
CNN | Raghunath A. (2023) [21] | Diagnosis of paroxysmal AF: AI predicts AF from sinus rhythm ECGs, often escaping early diagnosis. | |
Baj G. (2023) [22] | Machine learning methods for AF prediction: The development of ECG-based prediction models is booming. | ||
Jiang, J. (2023) [23] | Catheter ablation and AF recurrence: AI-ECG predicts the risk of recurrence in paroxysmal AF post-ablation. | ||
Bai Y. (2021) [24] | Predicting AF recurrence post catheter ablation: AI-ECG models for predicting AF recurrence post-ablation. | ||
DL | Raghunath S. (2021) [25] | Prediction of incipient AF with deep learning: DL predicts AF using 12-lead ECG in patients with no known history of AF. | |
Holmstrom L. (2024) [26] | Assessment of sudden cardiac death risk: DL model identifies cases at risk of sudden cardiac death (SCD). | ||
Heart failure | CNN | Grun D. (2021) [27] | Predicting heart failure using ECG signals: AI demonstrates high accuracy in predicting HF with ECG. |
Akbilgic O. (2021) [28] | Early intervention in reducing morbidity and mortality: AI aids in HF risk prediction through ECG analysis. | ||
Pulmonary hypertension | DL | Kwon J.M. (2020) [29] | Early diagnosis of PH: AI algorithm demonstrates high accuracy in predicting PH using both 12-lead and single-lead ECGs. |
Mortality prediction | DL | Tsai D.J. (2023) [30] | DL-based survival model: Establishes critical value of ECG for CVD management and predicts SCD. |
Baek Y.S. (2023) [31] | Prediction of mortality: DL model estimates cardiac age and predicts mortality using 12-lead ECG. | ||
CNN | Shiraishi Y. (2023) [32] | Evaluating predictive models for SCD: ECG-based AI enhances prediction of sudden cardiac death (SCD). | |
Liu C.M. (2022) [33] | Risk of cardiovascular mortality: AI model identifies pulmonary hypertension and predicts cardiovascular mortality. | ||
Cardiomyopathy | CNN | Güntürkün F. (2021) [34] | Predicting late-onset cardiomyopathy: AI identifies childhood cancer survivors at risk of cardiomyopathy. |
Siontis K.C (2021) [2] | Detecting hypertrophic cardiomyopathy (HCM): DL model accurately diagnoses HCM using 12-lead ECG. | ||
Ko W.Y. (2020) [35] | High performance in HCM detection: AI complements traditional diagnostic methods, enhancing speed and accuracy. | ||
Chemotherapy-induced cardiotoxicity | CNN | Jacobs JEJ (2024) [36] | AI-ECG detects newly abnormal left ventricular ejection fraction after anthracycline therapy. |
DL | Yagi R. (2024) [37] | AI predicts cardiotoxicity from baseline ECGs in chemotherapy patients. | |
Halasz G. (2024) [38] | AI-assisted ECG provides accessible diagnosis of cancer therapy-related cardiac dysfunction. | ||
ML | Güntürkün F. (2021) [34] | AI predicts late-onset cardiomyopathy among childhood cancer survivors. |
Affections | Method | First Author | Brief Description of the Paper Topic |
---|---|---|---|
Arrythmias | DL | Baek Y.S. (2023) [39] | Identifying AF: DL-based algorithm shows high accuracy in detecting AF from 12-lead ECG during normal sinus rhythm. |
Baek Y.S. (2021) [40] | High accuracy in AF detection: DL model effectively identifies AF in clinical settings. | ||
DNN | Sabut S. (2021) [41] | Detection of ventricular arrhythmia: AI accurately detects ventricular tachyarrhythmia conditions. | |
Bos J.M. (2021) [42] | Early detection of congenital long QT syndrome: AI differentiates patients with electrocardiographically occult long QT syndrome. | ||
Mitral regurgitation | DL | Kwon J.M. (2020) [43] | Detecting mitral regurgitation: AI algorithm shows promising results using 12-lead and single-lead ECGs. |
Hypertension | KNN | Soh D. C.K. (2020) [44] | Detection of hypertension: Computational intelligence tool uses ECG signals for HPT detection and identification of masked hypertension. |
Pulmonary embolism | DNN | Valente S.B. (2023) [45] | Diagnosis of acute pulmonary embolism: DL model shows high specificity in diagnosing PE using 12-lead ECG. |
Myocardial infarction | DL | Liu W.C. (2021) [46] | Detecting myocardial infarction: DL model aids in timely diagnostic decision-making using 12-lead ECG. |
Liu W.C. (2021) [47] | Diagnosis of acute myocardial infarction: AI algorithm successfully detects MI with both 12-lead and 6-lead ECG. | ||
CNN | Cho Y. (2020) [48] | Detecting myocardial infarction: AI model demonstrates exceptional diagnostic capability for STEMI. | |
Zhao Y. (2020) [49] | Early detection of STEMI: AI-S trial shows significant improvement in time to treatment in AMI cases. | ||
Cardiac Masses in Cardio- Oncology | CNN | Oikonomou EK. (2024) [50] | Stratification of cancer-related cardiac dysfunction using ECG imaging. |
DL | Martinez DS. (2022) [51] | AI in cardio-oncology: Enhancing ECG-based diagnosis of cardiac masses. |
Study | Algorithm | Strengths | Weaknesses | Accuracy | Clinical Relevance | Data Source | Key Findings |
---|---|---|---|---|---|---|---|
Baek Y. (2023) [54] | CNN | High accuracy in image recognition, capable of detecting subtle patterns in ECG | Requires large dataset, computationally intensive | High | High, as it directly processes ECG | Multi institutional dataset | CNN accurately predicts cardiovascular complications from ECGs, demonstrating its utility in clinical settings |
Nawaz M. (2022) [52] | Ensemble Learning (Random Forest, XGBoost) | Handles missing data well, interpretable, robust to overfitting | May not capture temporal data effectively | Moderate to High | Moderate, focuses on combining various predictors | Hospital records, ECGs, clinical data | Ensemble models performed well with integrated clinical and ECG data, highlighting their robustness |
Sridhar A.R. (2022) [7] | CNN-LSTM | Captures temporal patterns in ECG, good for sequential data | Computationally intensive, requires large dataset | Moderate | High, predicts mortality and MACE from ECGs | Multi institutional dataset | CNN-LSTM had limited predictive power alone, suggesting need for mixed-input models for improved accuracy |
Gupta M.D. (2021) [55] | Extra Tree Classifier | Good feature importance analysis, interpretable, robust | Lower sensitivity compared to DL methods | Moderate | High, predicts burnout and stress in HCWs using HRV | Multicenter dataset | Extra tree classifier effectively distinguished between burnout and non-burnout cases with HRV data, Demonstrating its utility in stress prediction |
Study | Algorithm | Strengths | Weaknesses | Accuracy | Clinical Relevance | Data Source | Key Findings |
---|---|---|---|---|---|---|---|
Sbrollini et al. (2024) [56] | AdvRS&LP with LIME | High interpretability; robust feature extraction; high AUC | Limited dataset size; Complexity of the model | AUC 1: 84.31% | High: Easily interpretable and clinically relevant | COVIDSQUARED project | Identified ECG and VCG patterns related to COVID-19 mortality with high accuracy and interpretability. |
Van de Leur et al. (2022) [57] | Logistic Regression, LASSO, DNN | Comparison of multiple models; external validation; automated approach | Decrease in performance during external validation; less interpretable | AUC: 77% (DNN) | Moderate: Automated but less interpretable | CAPACITY-COVID registry | Showed ECG-based models can predict COVID-19 mortality; DNN performed similarly to human annotation but with limited added value above age and sex. |
Study | Algorithm | Strengths | Weaknesses | Accuracy | Clinical Relevance | Data Source | Key Findings |
---|---|---|---|---|---|---|---|
Sakr A.S. (2023) [58] | CNN + RNN | Good balance of precision and sensitivity | Slightly lower accuracy compared to others | 96.0 (Binary) | Useful for real-time diagnosis | Public ECG dataset | Combines temporal and spatial features effectively, achieving robust diagnostic performance |
Agrawal A. (2022) [59] | CNN + SVM | High accuracy and specificity | Requires careful tuning of SVM parameters | 97.8 (Binary) 1 | Suitable for clinical implementation | Public ECG dataset | SVM enhances the classification capability of CNN, resulting in high diagnostic accuracy |
Irmak E. (2022) [60] | CNN + Data Augmentation | Reduces overfitting 2, enhances model robustness | Lower accuracy compared to other models | 81.8 (Multiclass) 3 | Useful for improving model generalization | Public ECG dataset | Data augmentation improves robustness but may not significantly enhance accuracy in complex multiclass classification tasks |
Nguyen T. (2022) [61] | CNN + Transfer Learning | High specificity and precision | Moderate sensitivity | 97.62 (Binary) | Effective for COVID-19 diagnosis | Public ECG dataset | Demonstrates the effectiveness of transfer learning in improving ECG-based COVID-19 diagnosis accuracy |
Rahman T. (2022) [62] | Hexaxial 4 Feature Mapping + CNN | High sensitivity | Moderate specificity | 96.2 (Binary) | Effective for initial screening | Public ECG dataset | Hexaxial feature mapping improves sensitivity, making it effective for initial screening of COVID-19 |
Sobahi N. (2022) [63] | EfficientNet 5 | Efficient model, low computational cost | Lower accuracy compared to more complex models | 81.8 (Multiclass) | Suitable for resource-limited settings | Public ECG dataset | EfficientNet offers a balance between efficiency and accuracy, suitable for quick screening in resource-limited settings |
Attia Z.I. (2021) [64] | Hybrid CNN + Traditional ML | High accuracy, combines multiple models, feature selection | Complex, requires multiple models | 98.2 (Binary), 91.6 (Multiclass) | Potential alternative diagnostic tool | Public ECG dataset | High accuracy in diagnosing COVID-19 using ECG, demonstrates potential as an alternative diagnostic tool |
Ozdemir M.A. (2021) [65] | EfficientNet | Efficient model, low computational cost | Lower accuracy compared to more complex models | 81.8 (Multiclass) | Suitable for resource-limited settings | Public ECG dataset | EfficientNet offers a balance between efficiency and accuracy, suitable for quick screening in resource-limited settings |
Gomes J.C. (2023) [66] | Multi-Branch Fusion Network 6 | High accuracy, handles multiple features | Complex architecture | 98.0 (Binary) | Highly reliable for clinical diagnosis | Public ECG dataset | Multi-branch fusion network effectively combines multiple feature sets, resulting in high diagnostic accuracy |
Bassiouni M.M. (2022) [67] | CNN + LSTM 7 | Handles temporal dependencies well | Requires large amounts of data for training | 97.0 (Binary) | Effective for dynamic ECG signal analysis | Public ECG dataset | LSTM effectively captures temporal features, improving COVID-19 diagnosis from ECG data |
Prashant K. (2022) [68] | CNN + RF | High accuracy, combines ensemble learning | Computationally intensive | 98.0 (Binary) | Reliable for clinical use | Public ECG dataset | Random Forest improves robustness and accuracy of CNN in diagnosing COVID-19 from ECG |
Attallah O. (2022) [69] | Hybrid CNN + Traditional ML | High accuracy, combines multiple models, feature selection | Complex, requires multiple models | 98.2 (Binary), 91.6 (Multiclass) | Potential alternative diagnostic tool | Public ECG dataset | High accuracy in diagnosing COVID-19 using ECG, demonstrates potential as an alternative diagnostic tool |
Study | Algorithm | Strengths | Weaknesses | Accuracy | Clinical Relevance | Data Source | Key Findings |
---|---|---|---|---|---|---|---|
Gupta, M. D. (2021) [70] | Extra tree classifier | Large sample size (1615 participants), use of validated Mini-Z 1.0 Survey 1, comprehensive analysis of HRV features, multicentric study | Cross-sectional study, self-reported burnout measure, limited to Indian HCWs 2 | Sensitivity: 84%, AUC: 84%, Accuracy: 77% | Highlights the need for mental health support and better working conditions for HCWs, Potential for ML models to predict burnout and inform interventions | HCWs from four academic centers in India | Burnout rate: 16.2% overall, higher in second-line workers (20.5%), lower HRV in burned-out HCWs, significant factors: stress, job dissatisfaction, chaotic environment, mental impact of COVID-19 |
Attia Z. I. (2020) [71] | AI-enabled ECG analysis using a CNN | High accuracy (AUC 95%), rapid, noninvasive screening, FDA 3 emergency use authorization, real-world clinical data from Mayo Clinic | Small sample size (27 patients), retrospective study, lack of routine rhythm surveillance and other clinical markers | AUC: 95% | Provides a tool for early detection of cardiac dysfunction in COVID-19 patients, minimizes need for echocardiography, reducing healthcare staff exposure | COVID-19 patients at Mayo Clinic | AI ECG accurately detected EF 4 ≤40%, one patient with myocarditis showed rapid clinical decline detected by AI ECG, AI ECG effective in detecting pre-existing and new LV 5 dysfunction |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Nechita, L.C.; Nechita, A.; Voipan, A.E.; Voipan, D.; Debita, M.; Fulga, A.; Fulga, I.; Musat, C.L. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics 2024, 14, 1839. https://doi.org/10.3390/diagnostics14171839
Nechita LC, Nechita A, Voipan AE, Voipan D, Debita M, Fulga A, Fulga I, Musat CL. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics. 2024; 14(17):1839. https://doi.org/10.3390/diagnostics14171839
Chicago/Turabian StyleNechita, Luiza Camelia, Aurel Nechita, Andreea Elena Voipan, Daniel Voipan, Mihaela Debita, Ana Fulga, Iuliu Fulga, and Carmina Liana Musat. 2024. "AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions" Diagnostics 14, no. 17: 1839. https://doi.org/10.3390/diagnostics14171839