Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals
Abstract
Background and objective Automatic screening tools can be applied to detect cardiovascular diseases (CVDs), which are the leading cause of death worldwide. As an effective and non-invasive method, electrocardiogram (ECG) based approaches are widely used to identify CVDs. Hence, this paper proposes a deep convolutional neural network (CNN) to classify five CVDs using standard 12-lead ECG signals. Methods The Physiobank (PTB) ECG database is used in this study. Firstly, ECG signals are segmented into different intervals (one-second, two-seconds and three-seconds), without any wave detection, and three datasets are obtained. Secondly, as an alternative to any complex preprocessing, durations of raw ECG signals have been considered as input with simple min-max normalization. Lastly, a ten-fold cross-validation method is employed for one-second ECG signals and also tested on other two datasets (two-seconds and three-seconds). Results Comparing to the competing approaches, the proposed CNN acquires the highest performance, having an accuracy, sensitivity, and specificity of 99.59%, 99.04%, and 99.87%, respectively, with one-second ECG signals. The overall accuracy, sensitivity, and specificity obtained are 99.80%, 99.48%, and 99.93%, respectively, using two-seconds of signals with pre-trained proposed models. The accuracy, sensitivity, and specificity of segmented ECG tested by three-seconds signals are 99.84%, 99.52%, and 99.95%, respectively. Conclusion The results of this study indicate that the proposed system accomplishes high performance and keeps the characterizations in brief with flexibility at the same time, which means that it has the potential for implementation in a practical, real-time medical environment.
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Dai et al. [151] achieved an accuracy of 99.84%, sensitivity of 99.52%, and specificity of 99.95% for three-second ECG signals.
“For three-second signal detection, the accuracies were 99.84%, sensitivity 99.52%, and specificity 99.95%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [151]
Dai et al. [151] achieved an accuracy of 99.59%, sensitivity of 99.04%, and specificity of 99.87% for one-second ECG signals.
“The proposed CNN model achieved an accuracy, sensitivity, and specificity of 99.59%, 99.04%, and 99.87%, respectively, for one-second signals, demonstrating superior performance.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [151]
Dai et al. [151] achieved an overall accuracy of 99.80%, sensitivity of 99.48%, and specificity of 99.93% for two-second ECG signals.
“For two-second signals using pre-trained models, the system achieved an overall accuracy, sensitivity, and specificity of 99.80%, 99.48%, and 99.93%.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [151]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809 · see citing works
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1993.341010 · Cited by 14,474 · see citing works
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909 · see citing works
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2858826 · Cited by 9,556 · see citing works
Third universal definition of myocardial infarction
the Writing Group on behalf of the Joint ESC/ACCF/AHA/WHF Task Force for the Universal Definition of Myocardial Infarction, Thygesen, K., Alpert, J. S., Jaffe, A. S., Simoons, M. L., CHAITMAN, B. R., & White, H. D. (2012) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2012.122 · Cited by 6,583 · see citing works
Global atlas on cardiovascular disease prevention and control.
Mendis, S., Puska, P., & Norrving, B. G. (2011) · Virtual Defense Library (Ministerio de Defensa) · Cited by 2,691 · see citing works
Third Universal Definition of Myocardial Infarction
Thygesen, K. A., Alpert, J. S., Jaffe, A. S., Simoons, M. L., CHAITMAN, B. R., White, H. D., Thygesen, K. A., Alpert, J. S., White, H. D., Jaffe, A. S., et al. (2012) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.08.001 · Cited by 2,619 · see citing works
Time series classification from scratch with deep neural networks: A strong baseline
Wang, Z., Yan, W., & Oates, T. (2017) · 2017 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2017.7966039 · Cited by 2,299 · see citing works
Samaniego, E. P., Anitescu, C., Goswami, S., Nguyen‐Thanh, V. M., Guo, H., Hamdia, K. M., Zhuang, X., & Rabczuk, T. (2020) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2019.112790 · Cited by 2,139 · see citing works
Normalization: A Preprocessing Stage
Patro, S. G. K., & Sahu, K. K. (2015) · IARJSET · doi:10.17148/iarjset.2015.2305 · Cited by 1,594 · see citing works
Acharya, U. R., Fujita, H., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adam, M. (2017) · Information Sciences · doi:10.1016/j.ins.2017.06.027 · Cited by 936 · see citing works
Yıldırım, Ö. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.03.016 · Cited by 854 · see citing works
Recommendations for interpretation of 12-lead electrocardiogram in the athlete
Corrado, D., Pelliccia, A., Heidbüchel, H., Sharma, S., Link, M. S., Basso, C., Biffi, A., Buja, G., Delise, P., Gussac, I., et al. (2009) · European Heart Journal · doi:10.1093/eurheartj/ehp473 · Cited by 843 · see citing works
2018 International Joint Conference on Neural Networks (IJCNN)
(2018) · doi:10.1109/ijcnn40505.2018 · Cited by 770 · see citing works
Acharya, U. R., Fujita, H., Lih, O. S., Hagiwara, Y., Tan, J. H., & Adam, M. (2017) · Information Sciences · doi:10.1016/j.ins.2017.04.012 · Cited by 755 · see citing works
Recent Trends in the Incidence, Treatment, and Outcomes of Patients with STEMI and NSTEMI
McManus, D. D., Gore, J. M., Yarzebski, J., Spencer, F. A., Lessard, D., & Goldberg, R. J. (2010) · The American Journal of Medicine · doi:10.1016/j.amjmed.2010.07.023 · Cited by 671 · see citing works
Classification of myocardial infarction with multi-lead ECG signals and deep CNN
Baloğlu, U. B., Talo, M., Yıldırım, Ö., Tan, R., & Acharya, U. R. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.02.016 · Cited by 477 · see citing works
Global Atlas of Cardiovascular Disease 2000-2016: The Path to Prevention and Control
Thomas, H., Diamond, J. E., Vieco, A., Chaudhuri, S., Shinnar, E., CROMER, S. J., Perel, P., Mensah, G. A., Narula, J., Johnson, C. O., et al. (2018) · Global Heart · doi:10.1016/j.gheart.2018.09.511 · Cited by 440 · see citing works
Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis
Chen, P., Lin, M.-C., Lai, M.-J., Lin, J.-C., Lu, H. H., & Tseng, V. S. M. (2017) · Gastroenterology · doi:10.1053/j.gastro.2017.10.010 · Cited by 396 · see citing works
Self-citationAcharya, U. R., Fujita, H., Sudarshan, V. K., Oh, S. L., Adam, M., Koh, J. E. W., Tan, J. H., Ghista, D. N., Martis, R. J., Chua, C. K., et al. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.01.040 · Cited by 241 · see citing works
Acharya, U. R., Fujita, H., Adam, M., Lih, O. S., Sudarshan, V. K., Hong, T. J., Koh, J. E., Hagiwara, Y., Chua, C. K., Chua, K. P., et al. (2016) · Information Sciences · doi:10.1016/j.ins.2016.10.013 · Cited by 233 · see citing works
Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review
Hagiwara, Y., Fujita, H., Oh, S. L., Tan, J. H., Tan, R., Ciaccio, E. J., & Acharya, U. R. (2018) · Information Sciences · doi:10.1016/j.ins.2018.07.063 · Cited by 193 · see citing works
Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection
Liu, W., Zhang, M., Zhang, Y., Liao, Y., Huang, Q., Chang, S., Wang, H., & He, J. L. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2771768 · Cited by 183 · see citing works
Lancellotti, P., Rosenhek, R., Pîbarot, P., Iung, B., Otto, C. M., Tornos, P., Donal, E., Prendergast, B., Magné, J., Canna, G. L., et al. (2013) · European Heart Journal · doi:10.1093/eurheartj/ehs443 · Cited by 179 · see citing works
Detection of myocardial infarction in 12 lead ECG using support vector machine
Dohare, A. K., Kumar, V., & Kumar, R. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.12.001 · Cited by 169 · see citing works
Liu, W., Huang, Q., Chang, S., Wang, H., & He, J. L. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.05.013 · Cited by 168 · see citing works
MFB-CBRNN: A Hybrid Network for MI Detection Using 12-Lead ECGs
Liu, W., Wang, F., Huang, Q., Chang, S., Wang, H., & He, J. L. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2910082 · Cited by 151 · see citing works
Computer Aided detection for fibrillations and flutters using deep convolutional neural network
Fujita, H., & Cimr, D. (2019) · Information Sciences · doi:10.1016/j.ins.2019.02.065 · Cited by 145 · see citing works
A Novel Approach for Detection of Myocardial Infarction From ECG Signals of Multiple Electrodes
Tripathy, R. K., Bhattacharyya, A., & Pachori, R. B. (2019) · IEEE Sensors Journal · doi:10.1109/jsen.2019.2896308 · Cited by 129 · see citing works
Zhang, Z.-M., Rautaharju, P. M., Soliman, E. Z., Manson, J. E., CAIN, M. E., Martin, L. W., Bavry, A. A., Mehta, L. S., Vitolins, M. Z., & Prineas, R. J. (2012) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2012.06.060 · Cited by 117 · see citing works
The Electrocardiogram in the Diagnosis and Management of Patients with Dilated Cardiomyopathy
Finocchiaro, G., Merlo, M., Sheikh, N., Angelis, G. D., Papadakis, M., Olivotto, I., Rapezzi, C., Carr‐White, G., Sharma, S., Mestroni, L., et al. (2020) · European Journal of Heart Failure · doi:10.1002/ejhf.1815 · Cited by 111 · see citing works
Study of heart rate variability signals at sitting and lying postures
Acharya, U. R., Kannathal, N., Hua, L. M., & Yi, L. M. (2004) · Journal of Bodywork and Movement Therapies · doi:10.1016/j.jbmt.2004.04.001 · Cited by 107 · see citing works
Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification
Rahman, Q. A., Tereshchenko, L. G., Kongkatong, M., Abraham, T. P., Abraham, M. R., & Shatkay, H. (2015) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2015.2426213 · Cited by 100 · see citing works
Acharya, U. R., Fujita, H., Sudarshan, V. K., Oh, S. L., Adam, M., Tan, J. H., Koo, J. H., Jain, A., Lim, C. M., & Chua, K. C. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.06.026 · Cited by 99 · see citing works
Sopic, D., Aminifar, A., Aminifar, A., & Atienza, D. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2848477 · Cited by 97 · see citing works
Han, C., & Li, S. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.03.012 · Cited by 93 · see citing works
Leveraging knowledge engineering and machine learning for microbial bio-manufacturing
Oyetunde, T., Bao, F. S., Chen, J.-W., Martín, H. G., & Tang, Y. (2018) · Biotechnology Advances · doi:10.1016/j.biotechadv.2018.04.008 · Cited by 91 · see citing works
Formal Design Methods for Reliable Computer-Aided Diagnosis: A Review
Faust, O., Acharya, U. R., & TAMURA, T. (2012) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2012.2184750 · Cited by 73 · see citing works
Hybrid Bacterial Foraging and Particle Swarm Optimization for detecting Bundle Branch Block
Kora, P., & Kalva, S. R. (2015) · SpringerPlus · doi:10.1186/s40064-015-1240-z · Cited by 64 · see citing works
Adam, M., Oh, S. L., Sudarshan, V. K., Koh, J. E., Hagiwara, Y., Tan, J. H., Tan, R., & Acharya, U. R. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.018 · Cited by 63 · see citing works
Zhang, J., Lin, F., Xiong, P., Du, H., Zhang, H., Liu, M., Hou, Z., & Liu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2919068 · Cited by 59 · see citing works
Posterior myocardial infarction: the dark side of the moon.
van Gorselen, E. O. F., Verheugt, F. W. A., Meursing, B. T. J., & Ophuis, A. J. M. O. (2007) · PubMed Central · Cited by 38 · see citing works
Detection and localization of myocardial infarction based on a convolutional autoencoder
Sugimoto, K., Kon, Y., Lee, S., & Okada, Y. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.04.023 · Cited by 35 · see citing works
Detection of Bundle Branch Block using Adaptive Bacterial Foraging Optimization and Neural Network
Kora, P., & Kalva, S. R. K. (2016) · Egyptian Informatics Journal · doi:10.1016/j.eij.2016.04.004 · Cited by 30 · see citing works
Zhang, Y., & Li, J. (2019) · Applied Sciences · doi:10.3390/app9163328 · Cited by 27 · see citing works
Liu, N., Wang, L., Chang, Q., Xing, Y., & Zhou, X. (2018) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2018.2463 · Cited by 23 · see citing works
MOHSIN, A., & Faust, O. (2019) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519419400098 · Cited by 18 · see citing works
Einthoven's Triangle: Lead Errors and an Algorithm for Solution
Bennett, F. T., Bennett, K. R., & Markov, A. K. (2005) · The American Journal of the Medical Sciences · doi:10.1097/00000441-200502000-00004 · Cited by 16 · see citing works
ECG-based heartbeat classification for disease diagnosis
Sabeeha, S., & Shiny, C. (2017) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2017.8282646 · Cited by 8 · see citing works
Detection of Bundle Branch Block Using Bat Algorithm and Levenberg Marquardt Neural Network
Kora, P., & Krishna, K. S. R. (2016) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-30933-0_55 · Cited by 5 · see citing works
Robust Sensor-based Human Activity Recognition with Snippet Consensus Neural Networks
Huang, Y., Lee, M.-C., Tseng, V. S. M., Hsiao, C.-J., & Huang, C.-C. (2019) · Wearable and Implantable Body Sensor Networks · doi:10.1109/bsn.2019.8771073 · Cited by 5 · see citing works
Self-citation
Flagged first, then by citations
2 of the 56 references couldn’t be matched to a known paper.
Cited by
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Application of artificial intelligence to the electrocardiogram
Attia, Z. I., Harmon, D., Behr, E. R., & Friedman, P. A. (2021) · European Heart Journal · doi:10.1093/eurheartj/ehab649 · Cited by 313 · see citing works
A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis
Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works
State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review
Πετμεζάς, Γ., Stefanopoulos, L., Kilintzis, V., Tzavelis, A., Rogers, J. A., Katsaggelos, A. K., & Maglaveras, N. (2022) · JMIR Medical Informatics · doi:10.2196/38454 · Cited by 120 · see citing works
Xiong, P., Lee, S. M., & Chan, G. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.860032 · Cited by 104 · see citing works
Moreno-Sánchez, P. A., Isla, G. G., Corino, V., Vehkaoja, A. T., Brukamp, K., van Gils, M., & Mainardi, L. T. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108235 · Cited by 92 · see citing works
Hybrid Convolutional Neural Network-Multilayer Perceptron Model for Solar Radiation Prediction
Ghimire, S., Nguyen‐Huy, T., Prasad, R., Deo, R. C., Casillas-Pérez, D., Salcedo‐Sanz, S., & Bhandari, B. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10070-y · Cited by 85 · see citing works
Tiwari, S., Jain, A., Sapra, V., Koundal, D., Alenezi, F. S., Polat, K., Alhudhaif, A., & Nour, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118933 · Cited by 58 · see citing works
Cuevas-Chávez, A., Hernández, Y., Ortiz-Hernández, J., Sánchez-Jiménez, E., Ochoa‐Ruiz, G., Pérez-Ortega, J., & González, G. (2023) · Healthcare · doi:10.3390/healthcare11162240 · Cited by 66 · see citing works
Denysyuk, H. V., Pinto, R. J., Silva, P. M., Duarte, R. P., Marinho, F. A., Pimenta, L., Gouveia, A. J., Gonçalves, N. J., Coelho, P. J., Zdravevski, E., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13601 · Cited by 55 · see citing works
An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques
Attallah, O. (2022) · Biosensors · doi:10.3390/bios12050299 · Cited by 53 · see citing works
Cardiovascular disease detection based on deep learning and multi-modal data fusion
Zhu, J.-Y., Liu, H., Liu, X.-W., Chen, C., & Shu, M.-L. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106882 · Cited by 47 · see citing works
Costanzo, A. D., Spaccarotella, C., Esposito, G., & Indolfi, C. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13041033 · Cited by 45 · see citing works
Capsule network assisted electrocardiogram classification model for smart healthcare
Jiao, Y., Qi, H., & Wu, J. (2022) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2022.03.006 · Cited by 33 · see citing works
Pasero, E. G. A., Gaïta, F., Randazzo, V., Meynet, P., Cannata, S., Maury, P., & Giustetto, C. (2023) · Sensors · doi:10.3390/s23218900 · Cited by 29 · see citing works
Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis
Zhang, J., Zhang, J., Jin, J., Jiang, X., Yang, L., Fan, S., Zhang, Q., & Chi, M. (2024) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2024.1323918 · Cited by 24 · see citing works
Prasad, S. S., Deo, R. C., Salcedo‐Sanz, S., Downs, N. J., Casillas-Pérez, D., & Parisi, A. V. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107737 · Cited by 24 · see citing works
Dami, S. (2022) · World Journal of Clinical Cases · doi:10.12998/wjcc.v10.i26.9207 · Cited by 22 · see citing works
An efficient honey badger based Faster region CNN for chronc heart Failure prediction
Sherly, S. I., & Mathivanan, G. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104165 · Cited by 22 · see citing works
Boujnouni, I. E., Harouchi, B., Tali, A., Rachafi, S., & Laaziz, Y. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104497 · Cited by 18 · see citing works
Tseng, L.-M., Chuang, C.-Y., Chua, S., & Tseng, V. S. M. (2022) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2022.3227204 · Cited by 19 · see citing works
Self-citationElshafey, R., Hamila, R., & Bensaali, F. (2024) · Medicine in Novel Technology and Devices · doi:10.1016/j.medntd.2024.100322 · Cited by 17 · see citing works
A semi-supervised autoencoder framework for joint generation and classification of breathing
Pastor‐Serrano, O., Lathouwers, D., & Perkó, Z. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106312 · Cited by 16 · see citing works
Randazzo, V., Caligari, S., Pasero, E. G. A., Giustetto, C., Saglietto, A., Bertarello, W., Averbuch, A., Marcus-Kalish, M., Zheludev, V. A., & Gaïta, F. (2025) · Sensors · doi:10.3390/s25030824 · Cited by 16 · see citing works
A Feature Selection-Based K-NN Model for Fast Software Defect Prediction
Awotunde, J. B., Misra, S., Adeniyi, A. E., Abiodun, M. K., Kaushik, M., & Lawrence, M. O. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10542-5_4 · Cited by 15 · see citing works
Rao, C. L. N., & Kakollu, V. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106388 · Cited by 14 · see citing works
Jiménez-Serrano, S., Rodrigo, M., Calvo, C. J., Millet, J., & Castells, F. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac72f5 · Cited by 13 · see citing works
Enhancing Arrhythmia Diagnosis with Data-Driven Methods: A 12-Lead ECG-Based Explainable AI Model
Chukwu, E. C., & Moreno-Sánchez, P. A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-59091-7_16 · Cited by 12 · see citing works
Goud, P. S., Sastry, P. N., & Sekhar, P. C. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16850-4 · Cited by 12 · see citing works
PoEMS: Policy Network-Based Early Warning Monitoring System for Sepsis in Intensive Care Units
Dai, H., Hwang, H., & Tseng, V. S. M. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3272486 · Cited by 11 · see citing works
Self-citationXie, Y.-L., & Lin, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125691 · Cited by 10 · see citing works
Gunasekaran, K., Kumar, V. D. A., & Jayashree, K. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106222 · Cited by 8 · see citing works
Enhanced DWT for Denoising Heartbeat Signal in Non-Invasive Detection
Zhu, P., Feng, L., Yu, K., Zhang, Y., Chen, W., & Hao, J. (2025) · Sensors · doi:10.3390/s25061743 · Cited by 8 · see citing works
Kim, Y., Jang, T. G., Park, S. Y., Park, H., Lee, J. A., Oyun-Erdene, T., Kim, S.-H., Park, Y., Cho, S. P., Park, J., et al. (2025) · npj Cardiovascular Health · doi:10.1038/s44325-025-00051-z · Cited by 8 · see citing works
The Role of Artificial Intelligence in Diagnosing Heart Disease in Humans: A Review
Yousiaf, T. H., & Al-Tamimi, M. S. H. (2023) · Journal of Applied Engineering and Technological Science (JAETS) · doi:10.37385/jaets.v5i1.3413 · Cited by 6 · see citing works
Hybrid Learning Approach for Automated Identification and Categorization of Cardiovascular Disorders
Padmavathi, C., & Veenadevi, S. V. (2024) · International Journal of Electrical and Electronics Research · doi:10.37391/ijeer.120423 · Cited by 5 · see citing works
Gunjal, A. S., & Judgi, T. (2025) · MethodsX · doi:10.1016/j.mex.2025.103678 · Cited by 5 · see citing works
Hussain, S., Ahmad, S., & Wasid, M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109342 · Cited by 5 · see citing works
Jiménez-Serrano, S., Sanz‐Sánchez, J., Vives‐Gilabert, Y., Millet, J., Zorio, E., & Castells, F. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107224 · Cited by 5 · see citing works
Expert consensus document on artificial intelligence of the Italian Society of Cardiology
Indolfi, C., Agostoni, P., Barillà, F., Barison, A., Benenati, S., Bilo, G., Boriani, G., Brunetti, N. D., Calabrò, P., Carugo, S., et al. (2025) · Journal of Cardiovascular Medicine · doi:10.2459/jcm.0000000000001716 · Cited by 5 · see citing works
Diaz, E. O., Mauricio, D., Maculan, N., & Godoy, G. V. U. (2026) · Diagnostics · doi:10.3390/diagnostics16010161 · Cited by 36 · see citing works
Łajczak, P. M., Righetto, B. B., Obi, O. S., Wijaya, P., Sahin, O. K., Eltawansy, S., Salha, I., Łajczak, A., & Schincariol, M. (2025) · Heart & Lung · doi:10.1016/j.hrtlng.2025.09.012 · Cited by 5 · see citing works
Deep Learning-Based Myoelectric Potential Estimation Method for Wheelchair Operation
Aihara, S., Shibata, R., Mizukami, R., Sakai, T., & Shionoya, A. (2022) · Sensors · doi:10.3390/s22041615 · Cited by 4 · see citing works
Pacing Electrocardiogram Detection With Memory-Based Autoencoder and Metric Learning
Ge, Z., Cheng, H., Tong, Z., Yang, L., Zhou, B., & Wang, Z. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.727210 · Cited by 4 · see citing works
Islam, R., Aurangzaib, Abid, M. K., Aziz, Y., Naeem, A., & Aslam, N. (2024) · VAWKUM Transactions on Computer Sciences · doi:10.21015/vtcs.v12i1.1781 · Cited by 4 · see citing works
A Review on Heart Diseases Using Machine Learning and Deep Learning Techniques
Mallikarjunamallu, K., & Syed, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-3292-0_45 · Cited by 4 · see citing works
Caligari, S., Randazzo, V., Giustetto, C., Gaïta, F., & Pasero, E. G. A. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-96-0994-9_2 · Cited by 3 · see citing works
Xu, J., Chen, B., Liu, W., Dong, W., Zhuang, Y., Zhang, P., & He, K. (2025) · Bioengineering · doi:10.3390/bioengineering12030250 · Cited by 3 · see citing works
ECG diagnosis device based on machine learning
Wang, B., Chen, G., Rong, L., Yu, A., Wen, T., Zhang, Y., & Hu, B. (2021) · IEEE International Conference on Emergency Science and Information Technology (ICESIT) · doi:10.1109/icesit53460.2021.9697057 · Cited by 2 · see citing works
Predicting Cardiovascular Disease: A Scoping Survey on different Datasets and DL/ML Models using ECG
Akouz, N., Ghazi, A. E., Moutaouakil, W., Hamida, S., Cherradi, B., & Raihani, A. (2024) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/iscv60512.2024.10620160 · Cited by 2 · see citing works
Miyamoto, K., Tanaka, H., Shidara, K., & Nakamura, S. (2023) · International Conference on Speech Database and Assessments · doi:10.1109/o-cocosda60357.2023.10482975 · Cited by 2 · see citing works
Abnormal ECG Detection using Optimized Boosting Tree Classifier
Mohapatra, A., Dastidar, A., Mohapatra, S. K., & Mohanty, M. N. (2022) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit56763.2022.00012 · Cited by 1 · see citing works
Nuannimnoi, S., Azhar, M. W., Chang, K.-H., Baskoro, A., Pratiwi, J. A. D., & Huang, C. (2025) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e65071.2025.11075395 · Cited by 1 · see citing works
Alanís, A. Y., Mendez-Palos, E., Sánchez, O. D., & Magaña, R. D. S. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-332856-5.00014-9 · Cited by 1 · see citing works
Divya, N. J., Kumar, N. S., & Devi, R. K. (2025) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.70229 · Cited by 1 · see citing works
Reehana, S., & Ibrahim, S. P. S. (2025) · Frontiers in Physiology · doi:10.3389/fphys.2025.1650134 · Cited by 1 · see citing works
Dang, T. H., Lee, S., Jin, J., Hong, K., Kwon, H., Kim, H., & Bien, F. D. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01406-w · Cited by 1 · see citing works
Deep Belief Neural Network Based Automatic CVD Prediction Using Adaptive Sliding Window Technique
Patil, S. S., & Mohite-Patil Tanaji, B. (2023) · ICST Transactions on Scalable Information Systems · doi:10.4108/eetsis.v10i3.2891 · Cited by 1 · see citing works
Cardiovascular Diseases Divination using Artificial Neural Network with Ensemble Models
Pabitha, B., Sanshi, S., & Karthik, N. (2023) · International Conference on Advances in Computational Intelligence and Communication (ICACIC) · doi:10.1109/icacic59454.2023.10435207 · Cited by 1 · see citing works
Cardiac Arrhythmias Detection Using a Hybrid 2D CNN-SVM Model
Tihak, A., Hrustemovic, L., & Bošković, D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-71694-2_38 · Cited by 1 · see citing works
Dharani, L., George, G. V. S., & Geetha, S. (2025) · Australian Journal of Electrical & Electronics Engineering · doi:10.1080/1448837x.2025.2457802 · Cited by 1 · see citing works
Mande, S. S., & Wategaonkar, D. (2025) · Biomedical Materials & Devices · doi:10.1007/s44174-025-00455-6 · Cited by 1 · see citing works
State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)Preprint
Πετμεζάς, Γ., Stefanopoulos, L., Kilintzis, V., Tzavelis, A., Rogers, J. A., Katsaggelos, A. K., & Maglaveras, N. (2022) · doi:10.2196/preprints.38454 · Cited by 1 · see citing works
Kim, Y., Jang, T. G., Park, S. Y., Park, H. Y., Lee, J. A., Tumenbat, O.-E., Cho, S. P., Park, J. H., Kang, D., Kim, S., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4855246 · Cited by 1 · see citing works
Whale optimization algorithm for Covid-19 detection based on ECG
Latreche, I., Khelili, M. A., Slatnia, S., Kazar, O., & Harous, S. (2024) · Elsevier eBooks · doi:10.1016/b978-0-32-395365-8.00046-4
Golande, A. L., & Pavankumar, T. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20113-1
Deep Learning Approach Heart Disease Prediction by Using Paper Based ECG Report
Mandava, M., & Vinta, S. R. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-4359-9_51
MECG-E: Mamba-based ECG Enhancer for Baseline Wander Removal
Hung, K.-H., Wang, K.-C., Liu, K.-C., Chen, W.-L., Lu, X., Tsao, Y., & Lin, C.-H. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825253
Boujnouni, I. E. (2025) · International Conference on Optimization and Applications (ICOA) · doi:10.1109/icoa66896.2025.11236831
A Quantum-Classical Approach Multimodal Cardiac Health Prediction
Hasan, M., Noman, M. A., Shetty, S., & Foytik, P. B. (2025) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc66965.2025.11250427
Transfer Learning Strategies for Cardiovascular Disease Detection in ECG Imagery
Soudagar, A., Shetty, S. K., Shivalingappa, S. H., Niranjanamurthy, M., Sinha, A., Khalid, S., & Ansarullah, S. I. (2025) · Biomedical Engineering and Computational Biology · doi:10.1177/11795972251397812 · Cited by 2 · see citing works
Data-Driven Predictive Systems for Improved Cardiovascular Disease Diagnosis
Rayate, H. B., Nikose, M., & Burade, P. G. (2025) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscn67106.2025.11308335
Brindha, M., Baranidharan, T., Malarvizhi, K., & Karthik, S. (2026) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-026-01021-1
AI-enabled clinically aligned fewer-lead ECG approaches for detecting cardiovascular diseases
Sikandar, T., Hasan, M. T., Azad, A. K. M., Khan, A., & Moni, M. A. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11577-8
Non-invasive Multimodal Cardiovascular Disease Detection Method Based on Comprehensive View Analysis
Xie, Y., Zhang, K., Long, J., Long, J., & Ming, H. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01985-1
Daphin Lilda, S., Jayaparvathy, R., Kumar, S. P., & Vedachalam, N. (2026) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-026-01760-0
Imbalance-Aware Benchmarking of Machine Learning Models for Multi-Class ECG Heartbeat Classification
Shukla, V., Gupta, V. K., Tiwari, A. K., & Mishra, R. (2026) · International Conference on NextGen Data Science and Analytics (ICNDSA) · doi:10.1109/icndsa68777.2026.11652173
Xu, T., Zhang, H., Xu, H., & Xu, S. (2026) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2026.109398
A dual-stream multi-scale cross-attention network for pericardial effusion detection.
Gao, J., Ju, P.-L., Chen, L.-X., Li, Y.-Z., Huang, J.-J., & Wu, J. (2026) · Computer Methods in Biomechanics and Biomedical Engineering · doi:10.1080/10255842.2026.2736736
- Authors
- Dai, H., Hwang, H.-G., & Tseng, V. S.
- Affiliations
- National Yang Ming Chiao Tung University (TW)
- Corresponding author
- Dai, Hao
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 203
- Pages
- 106035
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W3133554815
- Cited by
- 81
- References
- 56
- Citations by year
- 2026: 9 · 2025: 19 · 2024: 22 · 2023: 12 · 2022: 15 · 2021: 4
- FWCI
- 8.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- cardiovascular disease classification, electrocardiogram, convolutional neural networks, ECG segmentation, automated screening, pre-trained models
- MeSH terms
- Arrhythmias, Cardiac, Cardiovascular Diseases, Electrocardiography, Humans, Databases, Factual, Neural Networks, Computer
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