References¶

Papers behind the metrics, models and features implemented in ECGEN-Eval.

Metrics¶

Metric	Reference
`mmd`	Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. JMLR, 13, 723–773. Link
`dtw`	Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. KDD Workshop, 359–370. PDF
`prd`, `mae`	Zigel, Y., Cohen, A., & Katz, A. (2000). The weighted diagnostic distortion (WDD) measure for ECG signal compression. IEEE TBME, 47(11), 1422–1430. doi:10.1109/TBME.2000.880093
`psd`	Welch, P. D. (1967). The use of fast Fourier transform for the estimation of power spectra. IEEE Trans. Audio Electroacoust., 15(2), 70–73. doi:10.1109/TAU.1967.1161901
`fd`, `fed`	Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017). GANs trained by a two time-scale update rule converge to a local Nash equilibrium. NeurIPS, 30. arXiv:1706.08500
`fed`	Kilgour, K., Zuluaga, M., Roblek, D., & Sharifi, M. (2019). Fréchet Audio Distance: A reference-free metric for evaluating music enhancement algorithms. Interspeech. arXiv:1812.08466
`psnr`	Huynh-Thu, Q., & Ghanbari, M. (2008). Scope of validity of PSNR in image/video quality assessment. Electronics Letters, 44(13), 800–801. doi:10.1049/el:20080522
`ssim`	Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4), 600–612. doi:10.1109/TIP.2003.819861. Applied to ECG: Shahriari, Y., et al. (2018). Electrocardiogram signal quality assessment based on structural image similarity metric. IEEE TBME, 65(4), 745–753. doi:10.1109/TBME.2017.2717876
`swd`	Kolouri, S., Nadjahi, K., Simsekli, U., Badeau, R., & Rohde, G. (2019). Generalized sliced Wasserstein distances. NeurIPS, 32. arXiv:1902.00434
`pr_dist`	Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., & Aila, T. (2019). Improved precision and recall metric for assessing generative models. NeurIPS, 32. arXiv:1904.06991
`hrv`	Task Force of the ESC and NASPE (1996). Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Circulation, 93(5), 1043–1065. doi:10.1161/01.CIR.93.5.1043
`heart_rate`	Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE TBME, 32(3), 230–236. doi:10.1109/TBME.1985.325532
`sqi`	Clifford, G. D., et al. (2017). AF classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017. CinC, 44. doi:10.22489/CinC.2017.065-469
`nn_dist`	Meehan, C., Chaudhuri, K., & Dasgupta, S. (2020). A non-parametric test to detect data-copying in generative models. AISTATS. arXiv:2004.05675
`spectral_entropy`	Inouye, T., et al. (1991). Quantification of EEG irregularity by use of the entropy of the power spectrum. EEG Clin. Neurophysiol., 79(3), 204–210. doi:10.1016/0013-4694(91)90138-T

ECG generation and evaluation¶

Thambawita, V., et al. (2021). DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine. Scientific Reports, 11, 21896. doi:10.1038/s41598-021-01295-2
Golany, T., & Radinsky, K. (2019). PGANs: Personalized generative adversarial networks for ECG synthesis to improve patient-specific deep ECG classification. AAAI, 33, 557–564. doi:10.1609/aaai.v33i01.3301557

Foundation models (FED backbones)¶

ECGFounder — Li, J., et al. ECGFounder: An ECG foundation model built on over 10 million recordings with expert annotations. NEJM AI (2025). arXiv:2410.04133
HuBERT-ECG — Coppola, E., et al. HuBERT-ECG: a foundation model for broad-scope ECG tasks. medRxiv 2024.11.14.24317328.
ECG-FM — McKeen, K., et al. ECG-FM: An open electrocardiogram foundation model. JAMIA Open (2025). arXiv:2408.05178

Clinical features¶

Makowski, D., et al. (2021). NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior Research Methods, 53, 1689–1696.
Bazett, H. C. (1920). An analysis of the time-relations of electrocardiograms. Heart, 7, 353–370.