Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification. - Lucas Bickmann; Lucas Plagwitz; Julian Varghese (MIE, 2023)

The Necessity of Multiple Data Sources for ECG-Based Machine Learning Models. - Lucas Plagwitz; Tobias Vogelsang; Florian Doldi; Lucas Bickmann; Michael Fujarski; Lars Eckardt; Julian Varghese (MIE, 2023)

Benchmarking Approaches: Time Series Versus Feature-Based Machine Learning in ECG Analysis on the PTB-XL Dataset. - Lucas Bickmann; Lucas Plagwitz; Julian Varghese (MIE, 2024)

Assessing the Reliability of Machine Learning Explanations in ECG Analysis Through Feature Attribution. - Lucas Plagwitz; Lucas Bickmann; Antonius Büscher; Julian Varghese (MIE, 2024)

The Rlign Algorithm for Enhanced Electrocardiogram Analysis through R-Peak Alignment for Explainable Classification and Clustering. - Lucas Plagwitz; Lucas Bickmann; Michael Fujarski; Alexander Brenner; Warnes Gobalakrishnan; Lars Eckardt; Antonius Büscher; Julian Varghese (CoRR, 2024)

ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning. - Lucas Bickmann; Lucas Plagwitz; Antonius Büscher; Lars Eckardt; Julian Varghese (CoRR, 2025)