Lead Analysis for the Classification of Multi-Label Cardiovascular Diseases and Neural Network Architecture Design
Abstract
1. Introduction
- A series of thorough experiments was conducted to evaluate the fusion performance of 1-, 3-, 6-, 9- and 12-lead configurations with different fusion strategies. The diagnostic importance of each lead was evaluated, and an efficient lead selection approach was proposed on the basis of experimental results.
- Based on anatomical knowledge, we innovatively proposed a novel five-lead-grouping strategy and a neural network architecture named Lead-5-Group Net (L5G-Net) for multi-label ECG classification.
- No enhancement strategies, such as oversampling, model ensemble, and attention mechanisms, were used in this study. The approach demonstrates that a simple feed forward architecture can effectively learn ECG features and results close to SOTA methods can be obtained by promoting lead information fusion with appropriate methods.
2. Background and Related Works
3. Materials and Methods
3.1. Dataset and Preprocessing Method
3.2. Baseline Model
3.3. Single-Lead Experiment
3.4. Three-Lead to Twelve-Lead Experiment
3.5. Lead Grouping and Model Structure Optimization
3.6. Evaluation Metrics
4. Results
4.1. Experiment Setups
4.2. Result of the Single-Lead Experiment
4.3. Result of 3–12 Lead Experiment
4.4. Result of Lead Grouping and Structure Optimization
- Concat exhibits better performance than Add;
- Higher performance was obtained when fusing at the lower levels of the model.
- Fusing methods at different levels improves the classification performance compared with the 12-lead baseline model.
- Compared with the results of the three-branch experiment (Table 6), the L5G-Net has strong consistency, and the effect of fusion at different levels makes little difference, and the difference in average AUC is only 0.0019.
5. Discussion
5.1. Comparison of Results
5.2. Limitations and Future Directions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Roopa, C.K.; Harish, B.S. A Survey on various Machine Learning Approaches for ECG Analysis. Int. J. Comput. Appl. 2017, 163, 25–33. [Google Scholar] [CrossRef]
- Ayano, Y.M.; Schwenker, F.; Dufera, B.D.; Debelee, T.G. Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review. Diagnostics 2023, 13, 111. [Google Scholar] [CrossRef] [PubMed]
- Krishnan, R.; Rajpurkar, P.; Topol, E.J. Self-supervised learning in medicine and healthcare. Nat. Biomed. Eng. 2022, 6, 1346–1352. [Google Scholar] [CrossRef] [PubMed]
- Thygesen, K.; Alpert, J.S.; Jaffe, A.S.; Chaitman, B.R.; Bax, J.J.; Morrow, D.A.; White, H.D.; Executive Group on behalf of the Joint European Society of Cardiology (ESC)/American College of Cardiology (ACC)/American Heart Association (AHA)/World Heart Federation (WHF). Fourth universal definition of myocardial infarction (2018). Circulation 2018, 138, 618–651. [Google Scholar] [CrossRef]
- Chen, T.-M.; Huang, C.-H.; Shih, E.S.C.; Hu, Y.-F.; Hwang, M.-J. Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model. iScience 2020, 23, 100886. [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]
- Feeny, A.K.; Chung, M.K.; Madabhushi, A.; Attia, Z.I.; Cikes, M.; Firouznia, M.; Friedman, P.A.; Kalscheur, M.M.; Kapa, S.; Narayan, S.M.; et al. Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circ. Arrhythmia Electrophysiol. 2020, 13, e007952. [Google Scholar] [CrossRef]
- Georgieva-Tsaneva, G.; Gospodinova, E. Heart rate variability analysis of healthy individuals and patients with ischemia and arrhythmia. Diagnostics 2023, 13, 2549. [Google Scholar] [CrossRef]
- Wang, L.-H.; Yan, Z.-H.; Yang, Y.-T.; Chen, J.-Y.; Yang, T.; Kuo, I.-C.; Abu, P.A.R.; Huang, P.-C.; Chen, C.-A.; Chen, S.-L. A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia. Sensors 2021, 21, 5222. [Google Scholar] [CrossRef]
- Raj, S.; Ray, K.C. ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM. IEEE Trans. Instrum. Meas. 2017, 66, 470–478. [Google Scholar] [CrossRef]
- Kung, B.-H.; Hu, P.-Y.; Huang, C.-C.; Lee, C.-C.; Yao, C.-Y.; Kuan, C.-H. An Efficient ECG Classification System using Resource-Saving Architecture and Random Forest. IEEE J. Biomed. Health Inform. 2021, 25, 1904–1914. [Google Scholar] [CrossRef]
- Xie, C.-X.; Wang, L.-H.; Yu, Y.-T.; Ding, L.-J.; Yang, T.; Kuo, I.-C.; Wang, X.-K.; Gao, J.; Abu, P.A.R. Clinical Sudden Cardiac Death Risk Prediction: A Grid Search Support Vector Machine Multimodel Base on Ventricular Fibrillation Visualization Features. Comput. Electr. Eng. 2025, 123, 110022. [Google Scholar] [CrossRef]
- Acharya, U.R.; Oh, S.L.; Hagiwara, Y.; Tan, J.H.; Adam, M.; Gertych, A.; San Tan, R. A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 2017, 89, 389–396. [Google Scholar] [CrossRef]
- Yao, Q.H.; Wang, R.X.; Fan, X.M.; Liu, J.; Li, Y. Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network. Inf. Fusion 2020, 53, 174–182. [Google Scholar] [CrossRef]
- Prabhakararao, E.; Dandapat, S. Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification. IEEE J. Biomed. Health Inform. 2022, 26, 3802–3812. [Google Scholar] [CrossRef] [PubMed]
- Hu, R.; Chen, J.; Zhou, L. A transformer-based deep neural network for arrhythmia detection using continuous ECG signals. Comput. Biol. Med. 2022, 144, 105325. [Google Scholar] [CrossRef] [PubMed]
- Moody, G.B.; Mark, R.G. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng. Med. Biol. Mag. 2001, 20, 45–50. [Google Scholar] [CrossRef]
- Wagner, P.; Strodthoff, N.; Bousseljot, R.-D.; Kreiseler, D.; Lunze, F.I.; Samek, W.; Schaeffter, T. PTB-XL, a large publicly available electrocardiography dataset. Sci. Data 2020, 7, 154. [Google Scholar] [CrossRef]
- Xing, Z.Z.; Ma, G.L.; Wang, L.Y.; Yang, L.; Guo, X.; Chen, S. Toward Visual Interaction: Hand Segmentation by Combining 3-d Graph Deep Learning and Laser Point Cloud for Intelligent Rehabilitation. IEEE Internet Things J. 2025, 12, 21328–21338. [Google Scholar] [CrossRef]
- Zhou, P.C.; Fang, Z.Y.; Yang, Z.L.; Zhou, Z.; Zhou, L. Efficient Streaming Voice Steganalysis in Challenging Detection Scenarios. IEEE Trans. Inf. Forensics Secur. 2025, 20, 5966–5977. [Google Scholar] [CrossRef]
- Xie, X.Y.; Liu, H.; Chen, D.; Shu, M.; Wang, Y. Multilabel 12-Lead ECG Classification Based on Leadwise Grouping Multibranch Network. IEEE Trans. Instrum. Meas. 2022, 71, 4004111. [Google Scholar] [CrossRef]
- Liu, F.F.; Liu, C.Y.; Zhao, L.; Zhang, X.; Wu, X.; Xu, X.; Liu, Y.; Ma, C.; Wei, S.; He, Z.; et al. An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection. J. Med. Imaging Health Inform. 2018, 8, 1368–1373. [Google Scholar] [CrossRef]
- He, R.N.; Liu, Y.; Wang, K.Q.; Zhao, N.; Yuan, Y.; Li, Q.; Zhang, H. Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM. IEEE Access 2019, 7, 102119–102135. [Google Scholar] [CrossRef]
- Zhang, D.D.; Yang, S.; Yuan, X.H.; Zhang, P. Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram. iScience 2021, 24, 102373. [Google Scholar] [CrossRef] [PubMed]
- Krasteva, V.; Jekova, I.; Abächerli, R. Biometric verification by cross-correlation analysis of 12-lead ECG patterns: Ranking of the most reliable peripheral and chest leads. J. Electrocardiol. 2017, 50, 847–854. [Google Scholar] [CrossRef] [PubMed]
- Matyschik, M.; Mauranen, H.; Karel, J.; Bonizzi, P. Feasibility of ECG Reconstruction From Minimal Lead Sets Using Convolutional Neural Networks. In Proceedings of the 2020 Computing in Cardiology, Rimini, Italy, 13–16 September 2020; pp. 1–4. [Google Scholar] [CrossRef]
- Mousa, A.; Elgazzar, K. Six Leads Are All You Need for Efficient Cardiac Analysis. In Proceedings of the 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), Dubai, United Arab Emirates, 10–11 December 2023; pp. 153–160. [Google Scholar] [CrossRef]
- Reddy, L.; Talwar, V.; Alle, S.; Bapi, R.S.; Priyakumar, U.D. IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification. In Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia, 17–20 October 2021; pp. 1068–1074. [Google Scholar] [CrossRef]
- Perez Alday, E.A.; Gu, A.; Shah, A.J.; Robichaux, C.; Wong, A.-K.I.; Liu, C.; Liu, F.; Rad, A.B.; Elola, A.; Seyedi, S.; et al. Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020. Physiol. Meas. 2020, 41, 124003. [Google Scholar] [CrossRef] [PubMed]
- Liu, H.; Chen, D.; Zhang, X.; Li, H.; Bian, L.; Shu, M.; Wang, Y. A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements. Sci. Data 2022, 9, 272. [Google Scholar] [CrossRef] [PubMed]
- Azzem, Y.C.H.; Harrag, F. Explainable Deep Learning Based-System for Multilabel Classification of 12-Lead ECG. In Proceedings of the 2023 International Conference on Networking and Advanced Systems (ICNAS), Algiers, Algeria, 21–23 October 2023; pp. 1–6. [Google Scholar] [CrossRef]
- Tao, R.; Wang, L.; Xiong, Y.N.; Zeng, Y.-R. IM-ECG: An interpretable framework for arrhythmia detection using multi-lead ECG. Expert Syst. Appl. 2024, 237, 121497. [Google Scholar] [CrossRef]
- Zhou, F.Y.; Chen, L.Z. Leadwise clustering multi-branch network for multi-label ECG classification. Med. Eng. Phys. 2024, 130, 104196. [Google Scholar] [CrossRef]
- Kapfo, A.; Datta, S.; Dandapat, S.; Bora, P.K. LSTM based Synthesis of 12-lead ECG Signal from a Reduced Lead Set. In Proceedings of the 2022 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), Trivandrum, India, 10–12 March 2022; pp. 296–301. [Google Scholar] [CrossRef]
- Wang, L.-H.; Zou, Y.-Y.; Xie, C.-X.; Yang, T.; Abu, P.A.R. Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three-lead signals. J. Electrocardiol. 2024, 84, 27–31. [Google Scholar] [CrossRef]
- Strodthoff, N.; Wagner, P.; Schaeffter, T.; Samek, W. Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL. IEEE J. Biomed. Health Inform. 2021, 25, 1519–1528. [Google Scholar] [CrossRef] [PubMed]
- Drew, B.J.; Finlay, D.D. Standardization of reduced and optimal lead sets for continuous electrocardiogram monitoring: Where do we stand? J. Electrocardiol. 2008, 41, 458–465. [Google Scholar] [CrossRef] [PubMed]
- Zhang, J.; Liang, D.; Liu, A.P.; Gao, M.; Chen, X.; Zhang, X.; Chen, X. MLBF-Net: A Multi-Lead-Branch Fusion Network for Multi-Class Arrhythmia Classification Using 12-Lead ECG. IEEE J. Transl. Eng. Health Med. 2021, 9, 1900211. [Google Scholar] [CrossRef] [PubMed]
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | ||
---|---|---|---|---|---|---|---|---|---|---|
Superclasses | Train | 12,978 | 3279 | 730 | 124 | - | - | - | - | - |
Val | 1642 | 400 | 95 | 19 | - | - | - | - | - | |
Test | 1652 | 400 | 95 | 16 | - | - | - | - | - | |
Total | 16,272 | 4079 | 920 | 159 | - | - | - | - | - | |
All | Train | 550 | 9005 | 4014 | 2138 | 985 | 485 | 208 | 50 | 6 |
Val | 63 | 1114 | 571 | 230 | 136 | 52 | 20 | 6 | 1 | |
Test | 92 | 1128 | 529 | 229 | 133 | 60 | 25 | 7 | - | |
Total | 705 | 11,247 | 5114 | 2597 | 1254 | 597 | 253 | 63 | 7 |
Layer | Input Shape | Output Shape | Kernel Size |
---|---|---|---|
Conv1D + BN + Act Conv1D + BN + Act | 1000,2 | 1000,36 | 5 3 |
Max_Pool | 1000,36 | 500,36 | |
Conv1D + BN + Act Conv1D + BN + Act | 500,36 | 500,72 | 3 3 |
Max_Pool | 500,72 | 250,72 | |
Conv1D + BN + Act Conv1D + BN + Act | 250,72 | 250,144 | 3 3 |
Max_Pool | 250,144 | 125,144 | |
Conv1D + BN + Act Conv1D + BN + Act | 125,144 | 125,216 | 3 3 |
Max_Pool | 125,216 | 62,216 | |
Conv1D + BN + Act Conv1D + BN + Act | 62,216 | 62,288 | 3 3 |
Max_Pool | 62,288 | 31,288 | |
Conv1D + BN + Act Conv1D + BN + Act | 31,288 | 31,288 | 3 3 |
Global Average Pool + Reshape | 31,288 | 1,288 | |
Conv1D + BN + Act | 1,288 | 1,72 | 1 |
Conv1D + BN + Sigmoid | 1,72 | 1,5 | 1 |
Input Leads | Reconstructed Limb Leads | Linear Regression Reconstruction Model |
---|---|---|
I + II | III | |
aVR | ||
aVL | ||
aVF | ||
Recommended Grouping Strategy | ||
[V1, V2]—Septal wall [V3, V4]—Anterior wall [V5, V6, I, aVL]—Lateral and High-lateral wall [II, III, aVF]—Inferior wall [aVR]—Posterior wall |
Lead | AUC Average (Max) | F1-Score Average (Max) | ACC Average (Max) | Rank by AUC |
---|---|---|---|---|
I | 0.8357 (0.8396) | 0.6151 (0.6179) | 0.8184 (0.8202) | 6 |
II | 0.8525 (0.8562) | 0.6301 (0.6357) | 0.8343 (0.8393) | 4 |
III | 0.8011 (0.8044) | 0.5712 (0.5784) | 0.7912 (0.7953) | 12 |
aVR | 0.8645 (0.8650) | 0.6479 (0.6498) | 0.8367 (0.8380) | 1 |
aVL | 0.8129 (0.8167) | 0.5806 (0.5853) | 0.8024 (0.8040) | 10 |
aVF | 0.8287 (0.8306) | 0.6004 (0.6040) | 0.8121 (0.8154) | 7 |
V1 | 0.8223 (0.8228) | 0.6032 (0.6058) | 0.8015 (0.8047) | 9 |
V2 | 0.8061 (0.8071) | 0.5875 (0.5890) | 0.8048 (0.8079) | 11 |
V3 | 0.8224 (0.8233) | 0.6003 (0.6019) | 0.8139 (0.8171) | 8 |
V4 | 0.8429 (0.8461) | 0.6205 (0.6225) | 0.8219 (0.8246) | 5 |
V5 | 0.8624 (0.8630) | 0.6556 (0.6574) | 0.8399 (0.8415) | 3 |
V6 | 0.8629 (0.8637) | 0.6543 (0.6571) | 0.8398 (0.8401) | 2 |
Leads | Selection Strategy | AUC Average (Max) | F1-Score Average (Max) | ACC Average (Max) |
---|---|---|---|---|
[aVR, V5, V6] | Rank | 0.8850 (0.8883) | 0.6859 (0.6894) | 0.8585 (0.8595) |
[III, aVL, V2] | Rank | 0.9131 (0.9141) | 0.7189 (0.7224) | 0.8763 (0.8770) |
[I, II, V1] | Complementarity | 0.9089 (0.9124) | 0.7102 (0.7151) | 0.8729 (0.8760) |
[I, II, V2] | Complementarity | 0.9128 (0.9150) | 0.7178 (0.7209) | 0.8761 (0.8779) |
[V1, V4, V6] | Region | 0.8925 (0.8961) | 0.6978 (0.7038) | 0.8644 (0.8667) |
[II, III, aVF] | Region | 0.8895 (0.8944) | 0.6856 (0.6893) | 0.8607 (0.8629) |
[III, AVL, aVF, V1, V2, V3] | Complementarity | 0.9171 (0.9185) | 0.7187 (0.7273) | 0.8775 (0.8808) |
[I, II, aVR, V4, V5, V6] | Complementarity | 0.9092 (0.9109) | 0.7220 (0.7248) | 0.8763 (0.8779) |
[V1, V2, V3, V4, V5, V6] | Region | 0.9011 (0.9032) | 0.7098 (0.7158) | 0.8708 (0.8730) |
[I, II, III, aVR, aVL, aVF] | Region | 0.8906 (0.8923) | 0.6836 (0.6884) | 0.8605 (0.8622) |
[I, II, aVR, aVF, V1, V3, V4, V5, V6] | Random | 0.9207 (0.9224) | 0.7380 (0.7413) | 0.8841 (0.8857) |
[I, II, III, aVL, aVF, V1, V2, V3, V4] | Random | 0.9196 (0.9218) | 0.7316 (0.7367) | 0.8814 (0.8851) |
[I, II, III, aVR, aVF, V2, V4, V5, V6] | Random | 0.9231 (0.9257) | 0.7423 (0.7463) | 0.8861 (0.8895) |
[II, III, aVR, aVL, aVF, V1, V2, V3, V6] | Random | 0.9241 (0.9256) | 0.7427 (0.7488) | 0.8864 (0.8885) |
12 leads | All leads | 0.9234 (0.9250) | 0.7452 (0.7497) | 0.8865 (0.8882) |
Structure | AUC Average (Max) | F1-Score Average (Max) | ACC Average (Max) |
---|---|---|---|
concat 4 | 0.9058 (0.9087) | 0.7056 (0.7111) | 0.8729 (0.8737) |
concat 3 | 0.9101 (0.9112) | 0.7111 (0.7145) | 0.8758 (0.8774) |
concat 2 | 0.9106 (0.9121) | 0.7127 (0.7193) | 0.8769 (0.8796) |
concat 1 | 0.9137 (0.9157) | 0.7187 (0.7225) | 0.8768 (0.8779) |
concat 0 | 0.9161 (0.9182) | 0.7247 (0.7312) | 0.8803 (0.8821) |
add 4 | 0.9038 (0.9060) | 0.7081 (0.7166) | 0.8731 (0.8755) |
add 3 | 0.8989 (0.9065) | 0.7020 (0.7116) | 0.8716 (0.8758) |
add 2 | 0.9068 (0.9093) | 0.7113 (0.7174) | 0.8746 (0.8765) |
add 1 | 0.9134 (0.9144) | 0.7225 (0.7232) | 0.8791 (0.8817) |
add 0 | 0.9147 (0.9159) | 0.7177 (0.7215) | 0.8775 (0.8790) |
Structure | AUC Average (Max) | F1-Score Average (Max) | ACC Average (Max) |
---|---|---|---|
12-lead Baseline Model | 0.9234 (0.9250) | 0.7452 (0.7497) | 0.8865 (0.8882) |
concat 4 | 0.9339 (0.9346) | 0.7671 (0.7691) | 0.8932 (0.8944) |
concat 3 | 0.9333 (0.9341) | 0.7650 (0.7685) | 0.8927 (0.8951) |
concat 2 | 0.9344 (0.9357) | 0.7671 (0.7706) | 0.8951 (0.8976) |
concat 1 | 0.9348 (0.9352) | 0.7653 (0.7668) | 0.8936 (0.8958) |
concat 0 | 0.9329 (0.9332) | 0.7644 (0.7682) | 0.8923 (0.8941) |
add 4 | 0.9267 (0.9274) | 0.7487 (0.7511) | 0.8847 (0.8864) |
add 3 | 0.9305 (0.9329) | 0.7562 (0.7612) | 0.8890 (0.8907) |
add 2 | 0.9319 (0.9331) | 0.7630 (0.7651) | 0.8921 (0.8934) |
add 1 | 0.9325 (0.9328) | 0.7626 (0.7631) | 0.8923 (0.8934) |
add 0 | 0.9316 (0.9328) | 0.7599 (0.7624) | 0.8891 (0.8902) |
Structure | AUC Average (Max) | F1-Score Average (Max) | ACC Average (Max) |
---|---|---|---|
DenseNet | 0.9286 (0.9299) | 0.7518 (0.7527) | 0.8883 (0.8893) |
DenseNet_5_branch | 0.9311 (0.9320) | 0.7579 (0.7588) | 0.8902 (0.8914) |
ResNet | 0.9282 (0.9309) | 0.7500 (0.7552) | 0.8874 (0.8892) |
ResNet_5_branch | 0.9342 (0.9351) | 0.7677 (0.7701) | 0.8932 (0.8951) |
Cnn_12_branch | 0.9301 (0.9308) | 0.7584 (0.7617) | 0.8906 (0.8920) |
L5G-ECG | 0.9344 (0.9357) | 0.7671 (0.7706) | 0.8951 (0.8976) |
Model | AUC SUPER | AUC ALL | AUC DIAGNOSTIC | F1-SCORE SUPER | ACC SUPER |
---|---|---|---|---|---|
Lstm_bidir [36] | 0.921 | 0.914 | 0.932 | - | - |
Resnet1d_wang [36] | 0.930 | 0.919 | 0.936 | - | - |
Inception1d [36] | 0.921 | 0.925 | 0.931 | - | - |
Xresnet1d101 [36] | 0.928 | 0.925 | 0.937 | 0.737 | 0.885 |
MLBF-Net [38] | 0.931 | 0.934 | 0.938 | - | - |
IMLE [28] | 0.9216 | - | - | 0.8057 | 0.8885 |
IM-ECG [32] | 0.929 | - | - | 0.770 | 0.892 |
X-ECGNET [31] | 0.936 | - | - | 0.75 | 0.903 |
This work | 0.9357 | 0.9341 | 0.9395 | 0.7706 | 0.8976 |
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. |
© 2025 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
Yang, T.; Xie, C.-X.; Huang, H.-M.; Wang, Y.; Fan, M.-H.; Kuo, I.-C.; Chen, T.-Y.; Chen, S.-L.; Chen, C.-A.; Abu, P.A.R.; et al. Lead Analysis for the Classification of Multi-Label Cardiovascular Diseases and Neural Network Architecture Design. Electronics 2025, 14, 3211. https://doi.org/10.3390/electronics14163211
Yang T, Xie C-X, Huang H-M, Wang Y, Fan M-H, Kuo I-C, Chen T-Y, Chen S-L, Chen C-A, Abu PAR, et al. Lead Analysis for the Classification of Multi-Label Cardiovascular Diseases and Neural Network Architecture Design. Electronics. 2025; 14(16):3211. https://doi.org/10.3390/electronics14163211
Chicago/Turabian StyleYang, Tao, Chao-Xin Xie, Hui-Ming Huang, Yu Wang, Ming-Hui Fan, I-Chun Kuo, Tsung-Yi Chen, Shih-Lun Chen, Chiung-An Chen, Patricia Angela R. Abu, and et al. 2025. "Lead Analysis for the Classification of Multi-Label Cardiovascular Diseases and Neural Network Architecture Design" Electronics 14, no. 16: 3211. https://doi.org/10.3390/electronics14163211
APA StyleYang, T., Xie, C.-X., Huang, H.-M., Wang, Y., Fan, M.-H., Kuo, I.-C., Chen, T.-Y., Chen, S.-L., Chen, C.-A., Abu, P. A. R., & Wang, L.-H. (2025). Lead Analysis for the Classification of Multi-Label Cardiovascular Diseases and Neural Network Architecture Design. Electronics, 14(16), 3211. https://doi.org/10.3390/electronics14163211