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Navigable Slide Index

1. Introduction
2. Agenda
3. Machine learning for biomedical applications
4. Challenges
5. Electrocardiograms denoising
6. Heart disease and death
7. Electrocardiograms
8. AI using ECG
9. ECG noise
10. Machine learning for denoising
11. ECG denoising
12. Autoencoder and attention
13. Convolutional Denoising Autoencoder with Block Attention Module (CDAE-BAM)
14. Datasets (1)
15. Experimental design (1)
16. Experimental design (2)
17. Performance comparison (1)
18. Performance comparison (2)
19. Comparison
20. Machine learning with data privacy protection
21. Patient privacy vs. big data
22. Data heterogeneity and non-independent and identically distributed (non-I.I.D.)
23. Challenges of data heterogeneity and non-I.I.D.
24. Autoencoder-based data compression
25. Federated learning
26. Clustered hyperparameter optimization
27. Datasets (2)
28. Experimental results (1)
29. Experimental results (2)
30. Conclusion and future work
31. References
32. Thank you!
33. Financial disclosures

Topics Covered

• Biomedical data analysis
• Challenges in biomedical data management
• Electrocardiogram denoising
• Autoencoders
• Machine learning with data privacy protection
• Federated hyperparameter tuning method

Links

Series:

• Introduction to Computational Biology

Categories:

• Clinical Practice
• Neuroscience

Therapeutic Areas:

• Neurology

Talk Citation

Publication History

• Published on December 31, 2025

Financial Disclosures

• There are no commercial/financial matters to disclose.