@inproceedings{jielin2026-harmonized,
 abstract = {Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences. We benchmark cross-dataset generalization on three tasks - heart failure classification, 30-day all-cause mortality, and 30-day mortality among sinus-rhythm ECGs - using two large cohorts (MIMIC-IV and the Alberta Cohort). To reduce vendor-specific measurement mismatch, we build a harmonized, interpretable feature representation computed directly from raw waveforms: FeatureDB morphology/heart-rate-variability summaries plus compact time-frequency descriptors (autoregressive and wavelet features). We train XGBoost models on this unified feature space and evaluate with patient-disjoint internal and bidirectional external testing. We pre-specify},
 accepted = {2026-04-20},
 author = {Jie Lin and Weijie Sun and Sunil V Kalmady and Anita Khalafbeigi and Abram Hindle and Padma Kaul and Russell Greiner},
 authors = {Jie Lin, Weijie Sun, Sunil V Kalmady, Anita Khalafbeigi, Abram Hindle, Padma Kaul, Russell Greiner},
 booktitle = {39th IEEE International Symposium on Computer-Based Medical Systems (CBMS)},
 code = {jielin2026-harmonized},
 date = {2026-06-03},
 doi = {https://doi.org/10.48550/arXiv.2607.23412},
 funding = {NSERC Discovery},
 location = {Limassol, Cyprus},
 pagerange = {1--2},
 pages = {1--2},
 role = {Co-Author},
 title = {Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction},
 type = {inproceedings},
 url = {http://softwareprocess.ca/pubs/jielin2026-harmonized.pdf},
 venue = {39th IEEE International Symposium on Computer-Based Medical Systems (CBMS)},
 year = {2026}
}