Keywords
Cardiovascular disease
ECG
Convolutional neural networks
Deep learning
Submitted : 2024-09-30
Accepted : 2024-10-15
Published : 2024-10-30
Abstract
In recent years, artificial intelligence (AI) has demonstrated remarkable advancements in the field of cardiovascular disease (CVD), particularly in the analysis of electrocardiograms (ECGs). Due to its widespread use, low cost, and high efficiency, the ECG has long been regarded as a cornerstone of cardiological examinations and remains the most widely utilized diagnostic tool in cardiology. The integration of AI, especially deep learning (DL) technologies based on convolutional neural networks (CNNs), into ECG analysis, has shown immense potential across several cardiological subfields. Deep learning methods have provided robust support for the rapid interpretation of ECGs, enabling the fine-grained analysis of ECG waveform changes with diagnostic accuracy comparable to that of expert cardiologists. Additionally, CNN-based models have proven capable of capturing subtle ECG changes that are often undetectable by traditional methods, accurately predicting complex conditions such as atrial fibrillation, left and right ventricular dysfunction, hypertrophic cardiomyopathy, acute coronary syndrome, and aortic stenosis. This highlights the broad application potential of AI in the diagnosis of cardiovascular diseases. However, despite their extensive applications, CNN models also face significant limitations, primarily related to the reliability of the acquired data, the opacity of the “black box” processes, and the associated medical, legal, and ethical challenges. Addressing these limitations and seeking viable solutions remain critical challenges in modern medicine.
References
Vaduganathan M, Mensah GA, Turco JV, et al., 2022, The Global Burden of Cardiovascular Diseases and Risk: A Compass for Future Health. Journal of the American College of Cardiology, 80(25): 2361–2371.
Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, 2023, Summary of China Cardiovascular Health and Disease Report 2022, 23(7): 1–19 + 24.
Krittanawong C, Zhang H, Wang Z, et al., 2017, Artificial Intelligence in Precision Cardiovascular Medicine. Journal of the American College of Cardiology, 69(21): 2657–2664.
Litjens G, Kooi T, Bejnordi BE, et al., 2017, A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42: 60–88.
Hannun AY, Rajpurkar P, Haghpanahi M, et al., 2019, Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network. Nature Medicine, 25(1): 65–69.
Che C, Zhang P, Zhu M, et al., 2021, Constrained Transformer Network for ECG Signal Processing and Arrhythmia Classification. BMC Medical Informatics and Decision Making, 21(1): 184.
Li Y, Qian R, Li K, 2022, Inter-Patient Arrhythmia Classification with Improved Deep Residual Convolutional Neural Network. Computer Methods and Programs in Biomedicine, 214: 106582.
Kumar A, Kumar M, Mahapatra RP, et al., 2023, Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification. Sensors (Basel), 23(9): 4353.
Li P, Wang Y, He J, et al., 2017, High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal. IEEE Transactions on Biomedical Engineering, 64(1): 78–86.
Attia ZI, Noseworthy PA, Lopez-Jimenez F, et al., 2019, An Artificial Intelligence-Enabled ECG Algorithm for the Identification of Patients with Atrial Fibrillation During Sinus Rhythm: A Retrospective Analysis of Outcome Prediction. Lancet, 394(10201): 861–867.
Christopoulos G, Graff-Radford J, Lopez CL, et al., 2020, Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study. Circulation: Arrhythmia and Electrophysiology, 13(12): e009355.
Hill NR, Ayoubkhani D, McEwan P, et al., 2019, Predicting Atrial Fibrillation in Primary Care Using Machine Learning. PLoS One, 14(11): e0224582.
Sekelj S, Sandler B, Johnston E, et al., 2021, Detecting Undiagnosed Atrial Fibrillation in UK Primary Care: Validation of a Machine Learning Prediction Algorithm in a Retrospective Cohort Study. European Journal of Preventive Cardiology, 28(6): 598–605.
Raghunath S, Pfeifer JM, Ulloa-Cerna AE, et al., 2021, 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, 143(13): 1287–1298.
Khurshid S, Friedman S, Reeder C, et al., 2022, ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation, 145(2): 122–133.
Attia ZI, Kapa S, Lopez-Jimenez F, et al., 2019, Screening for Cardiac Contractile Dysfunction Using an Artificial Intelligence-Enabled Electrocardiogram. National Medicine, 25(1): 70–74.
Adedinsewo D, Carter RE, Attia Z, et al., 2020, Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department With Dyspnea. Circulation: Arrhythmia and Electrophysiology, 13(8): e008437.
Vaid A, Johnson KW, Badgeley MA, et al., 2022, Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram. JACC: Cardiovascular Imaging, 15(3): 395–410.
Yao X, McCoy RG, Friedman PA, et al., 2020, ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and Design of a Pragmatic Cluster Randomized Trial. American Heart Journal, 219: 31–36.
Yagi R, Goto S, Katsumata Y, et al., 2022, Importance of External Validation and Subgroup Analysis of Artificial Intelligence in the Detection of Low Ejection Fraction From Electrocardiograms. European Heart Journal: Digital Health, 3(4): 654–657.
Kwon JM, Lee SY, Jeon KH, et al., 2020, Deep Learning-Based Algorithm for Detecting Aortic Stenosis Using Electrocardiography. Journal of the American Heart Association, 9(7): e014717.
Cohen-Shelly M, Attia ZI, Friedman PA, et al., 2021, Electrocardiogram Screening for Aortic Valve Stenosis Using Artificial Intelligence. European Heart Journal, 42(30): 2885–2896.
Harmon DM, Malik A, Nishimura R, 2022, Progression of Calcific Aortic Stenosis Detected by Artificial Intelligence Electrocardiogram. Mayo Clinic Proceedings, 97(6): 1211–1212.
Ko WY, Siontis KC, Attia ZI, et al., 2020, Detection of Hypertrophic Cardiomyopathy Using a Convolutional Neural Network-Enabled Electrocardiogram. Journal of the American College of Cardiology, 75(7): 722–733.
Tison GH, Zhang J, Delling FN, et al., 2019, Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery. Circulation: Cardiovascular Quality and Outcomes, 12(9): e005289.
Chen KW, Wang YC, Liu MH, et al., 2022, Artificial Intelligence-Assisted Remote Detection of ST-Elevation Myocardial Infarction Using a Mini-12-Lead Electrocardiogram Device in Prehospital Ambulance Care. Frontiers in Cardiovascular Medicine, 9: 1001982.
Chen X, Guo W, Zhao L, et al., 2021, Acute Myocardial Infarction Detection Using Deep Learning-Enabled Electrocardiograms. Frontiers in Cardiovascular Medicine, 8: 654515.
Tadesse GA, Javed H, Weldemariam K, et al., 2021, DeepMI: Deep Multi-Lead ECG Fusion for Identifying Myocardial Infarction and Its Occurrence-Time. Artificial Intelligence in Medicine, 121: 102192.
Gumpfer N, Grün D, Hannig J, et al., 2020, Detecting Myocardial Scar Using Electrocardiogram Data and Deep Neural Networks. Journal of Biological Chemistry, 402(8): 911–923.
Galloway CD, Valys AV, Shreibati JB, et al., 2019, Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiology, 4(5): 428–436.
Lin CS, Lin C, Fang WH, et al., 2020, A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development. JMIR Medical Informatics, 8(3): e15931.
Attia ZI, DeSimone CV, Dillon JJ, et al., 2016, Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG. Journal of the American Heart Association, 5(1): e002746.