Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations

Machine Learning


  • Francisco, A., Pascoal, C., Lamborne, P., Morais, H. & Gonçalves, M. Wearables and atrial fibrillation: Advances in detection, clinical impact, ethical concerns, and future perspectives. Cureus 17, e77404. https://doi.org/10.7759/cureus.77404 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Luo, K., Li, J., Wang, Z. & Cuschieri, A. Patient-specific deep architectural model for ECG classification. J. Healthc. Eng. 2017, 4108720. https://doi.org/10.1155/2017/4108720 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Majumdar, A. & Ward, R. Robust greedy deep dictionary learning for ECG arrhythmia classification. In 2017 International Joint Conference on Neural Networks (IJCNN), https://doi.org/10.1109/IJCNN.2017.7966413 (IEEE, Anchorage, AK, USA, 2017).

  • Zhang, C. et al. Patient-specific ECG classification based on recurrent neural networks and clustering technique. In 2017 13th IASTED International Conference on Biomedical Engineering, https://doi.org/10.2316/P.2017.852-029 (Innsbruck, Austria, 2017).

  • Nguyen, M. T., Nguyen, B. V. & Kim, K. Deep feature learning for sudden cardiac arrest detection in automated external defibrillators. Sci. Reports 8, 17196. https://doi.org/10.1038/s41598-018-33424-9 (2018).

    Article 
    ADS 
    CAS 

    Google Scholar 

  • Sharma, P., Dinkar, S. K. & Gupta, D. A novel hybrid deep learning method with cuckoo search algorithm for classification of arrhythmia disease using ECG signals. Neural Comput. Appl. 33, 13123–13143. https://doi.org/10.1007/s00521-021-06005-7 (2021).

    Article 

    Google Scholar 

  • Sepahvand, M. & Abdali-Mohammadi, F. A novel method for reducing arrhythmia classification from 12-lead ECG signals to single-lead ECG with minimal loss of accuracy through teacher-student knowledge distillation. Inf. Sci. 593, 64–77. https://doi.org/10.1016/j.ins.2022.01.030 (2022).

    Article 

    Google Scholar 

  • Midani, W., Ouarda, W. & Ayed, M. B. DeepArr: An investigative tool for arrhythmia detection using a contextual deep neural network from electrocardiograms (ECG) signals. Biomed. Signal Process. Control. 85, 104954. https://doi.org/10.1016/j.bspc.2023.104954 (2023).

    Article 

    Google Scholar 

  • Kumar, S., Mallik, A., Kumar, A., Del Ser, J. & Yang, G. Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for arrhythmia detection from ECG signals. Comput. Biol. Medicine 153, 106511. https://doi.org/10.1016/j.compbiomed.2022.106511 (2023).

    Article 

    Google Scholar 

  • Linz, D. et al. Atrial fibrillation: epidemiology, screening and digital health. The Lancet Reg. Heal. – Eur. 37, 100786. https://doi.org/10.1016/j.lanepe.2023.100786 (2024).

    Article 

    Google Scholar 

  • Nesheiwat, Z., Goyal, A. & Jagtap, M. Atrial fibrillation. In StatPearls (StatPearls Publishing, Treasure Island (FL), 2024). PMID: 30252328. https://www.ncbi.nlm.nih.gov/books/NBK526072/.

  • Gunning, D. & Aha, D. Darpa’s explainable artificial intelligence (xai) program. AI Mag. 40, 44–58. https://doi.org/10.1609/aimag.v40i2.2850 (2019).

    Article 

    Google Scholar 

  • Ali, S. et al. Explainable artificial intelligence (xai): What we know and what is left to attain trustworthy artificial intelligence. Inf. Fusion 99, 101805. https://doi.org/10.1016/j.inffus.2023.101805 (2023).

    Article 

    Google Scholar 

  • Schmidt, M. From networks to architectures: Trustworthy AI models for medical applications. In Encyclopedia of Exercise Medicine in Health and Disease https://doi.org/10.1007/978-3-642-27830-3_14427-1 (Springer) (2025).

  • Goettling, M., Hammer, A., Malberg, H. & Schmidt, M. xECGArch: A trustworthy deep learning architecture for interpretable ECG analysis considering short-term and long-term features. Sci. Rep. 14, 13122. https://doi.org/10.1038/s41598-024-63656-x (2024).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hammer, A., Malberg, H. & Schmidt, M. Morphology features self-learned by explainable deep learning for atrial fibrillation detection correspond to fibrillatory waves. In Computing in Cardiology Conference (CinC), https://doi.org/10.22489/CinC.2024.305 (2024).

  • Hammer, A. et al. Fusion of automatically learned rhythm and morphology features matches diagnostic criteria and enhances AI explainability. npj Artificial Intelligence 1, https://doi.org/10.1038/s44387-025-00022-w (2025).

  • Montavon, G., Lapuschkin, S., Binder, A., Samek, W. & Müller, K.-R. Explaining nonlinear classification decisions with deep taylor decomposition. Pattern Recognit. 65, 211–222. https://doi.org/10.1016/j.patcog.2016.11.008 (2017).

    Article 
    ADS 

    Google Scholar 

  • Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359. https://doi.org/10.1109/TKDE.2009.191 (2010).

    Article 

    Google Scholar 

  • Ribani, R. & Marengoni, M. A survey of transfer learning for convolutional neural networks. In 2019 32nd SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), https://doi.org/10.1109/SIBGRAPI-T.2019.00010 (IEEE, 2019).

  • Tan, C. et al. A survey on deep transfer learning. In Artificial Neural Networks and Machine Learning – ICANN 270–279, 2018. https://doi.org/10.1007/978-3-030-01424-7_27 (Springer International Publishing, Cham) (2018).

  • Murugesan, B. et al. ECGNet: Deep network for arrhythmia classification. In 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA),https://doi.org/10.1109/MeMeA.2018.8438739 (IEEE, 2018).

  • Shi, H., Wang, H., Qin, C., Zhao, L. & Liu, C. An incremental learning system for atrial fibrillation detection based on transfer learning and active learning. Comput. Methods Programs Biomed. 187, 105219. https://doi.org/10.1016/j.cmpb.2019.105219 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Bos, M. Automated comprehensive interpretation of 12-lead electrocardiograms using pre-trained exponentially dilated causal convolutional neural networks. In 2020 Computing in Cardiology Conference (CinC (2020). https://doi.org/10.22489/CinC.2020.253 (Computing in Cardiology).

  • Ansari, S. Classification of 12-lead electrocardiograms using residual neural networks and transfer learning. In 2020 Computing in Cardiology Conference (CinC (2020). https://doi.org/10.22489/CinC.2020.374 (Computing in Cardiology).

  • Mousavi, S., Fotoohinasab, A. & Afghah, F. Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks. PLoS One 15, e0226990. https://doi.org/10.1371/journal.pone.0226990 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Antoni, L. A two-phase multilabel ecg classification using one-dimensional convolutional neural network and modified labels. Computing in Cardiology (CinC) 48, 1–4. https://doi.org/10.23919/CinC53138.2021.9662878 (2021).

    Article 

    Google Scholar 

  • Bizzego, A., Gabrieli, G., Neoh, M. J. Y. & Esposito, G. Improving the efficacy of deep-learning models for heart beat detection on heterogeneous datasets. Bioengineering 8, 193. https://doi.org/10.3390/bioengineering8120193 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Montenegro, L., Peixoto, H. & Machado, J. M. Evaluation of transfer learning to improve arrhythmia classification for a small ecg database. In Advances in Artificial Intelligence – IBERAMIA 2022: 17th Ibero-American Conference on AI, Cartagena de Indias, Colombia, November 23–25, 2022, Proceedings 231–242 (Springer-Verlag, 2022). https://doi.org/10.1007/978-3-031-22419-5_20.

  • Zhang, X. et al. Detection of atrial fibrillation from variable-duration ecg signal based on time-adaptive densely network and feature enhancement strategy. IEEE J. Biomed. Heal. Informatics 27, 944–955,https://doi.org/10.1109/JBHI.2022.3221464 (2023). Epub 2023 Feb 3.

  • Wang, Z., Stavrakis, S. & Yao, B. Hierarchical deep learning with generative adversarial network for automatic cardiac diagnosis from ECG signals. Comput. Biol. Medicine 155, 106641. https://doi.org/10.1016/j.compbiomed.2023.106641 (2023).

    Article 

    Google Scholar 

  • Chon, S., Ha, K.-W., Park, S. & Jung, S. An ECG beat classification method using multi-kernel ResNet with transformer. In 2023 IEEE International Conference on Big Data and Smart Computing (BigComp), https://doi.org/10.1109/BigComp57234.2023.00031 (IEEE, 2023).

  • Avetisyan, A. et al. Deep neural networks generalization and fine-tuning for 12-lead ECG classification. Biomed. Signal Process. Control. 93, 106160. https://doi.org/10.1016/j.bspc.2024.106160 (2024).

    Article 

    Google Scholar 

  • Weimann, K. & Conrad, T. O. F. Transfer learning for ECG classification. Scientific Reports 11, 5251. https://doi.org/10.1038/s41598-021-84374-8 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Islam, R., Rahman, M., Ismail, S. M. & Akter, S. Transfer learning in deep neural network model of ECG signal classification. In 2022 International Conference on Recent Progresses in Science, Engineering and Technology (ICRPSET), 1–4, https://doi.org/10.1109/ICRPSET57982.2022.10188563 (IEEE, 2022).

  • Gong, Y. et al. Transfer learning based deep network for signal restoration and rhythm analysis during cardiopulmonary resuscitation using only the ECG waveform. Inf. Sci. 626, 754–772. https://doi.org/10.1016/j.ins.2023.01.055 (2023).

    Article 

    Google Scholar 

  • Park, Y. et al. Development and validation of a real-time service model for noise removal and arrhythmia classification using electrocardiogram signals. Sensors 24, 5222. https://doi.org/10.3390/s24165222 (2024).

    Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nguyen, C. V., Duong, H. M. & Do, C. D. MELEP: A novel predictive measure of transferability in multi-label ECG diagnosis. J. Healthc. Informatics Res. 8, 506–522. https://doi.org/10.1007/s41666-024-00168-3 (2024).

    Article 

    Google Scholar 

  • Xu, H. et al. A dynamic transfer network for cross-database atrial fibrillation detection. Biomed. Signal Process. Control. 90, 105799. https://doi.org/10.1016/j.bspc.2023.105799 (2024).

    Article 

    Google Scholar 

  • Argha, A. et al. Assessing the generalizability of a deep learning-based automated atrial fibrillation algorithm. In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), vol. 2023, 1–6, https://doi.org/10.1109/EMBC40787.2023.10341108 (2023).

  • Zhang, H. et al. MaeFE: Masked Autoencoders Family of Electrocardiogram for Self-Supervised Pretraining and Transfer Learning. IEEE Transactions on Instrumentation and Measurement 72, 1–15. https://doi.org/10.1109/TIM.2022.3228267 (2023).

    Article 

    Google Scholar 

  • Ng, Y. et al. Few-shot transfer learning for personalized atrial fibrillation detection using patient-based siamese network with single-lead ECG records. Artif. Intell. Medicine 144, 102644. https://doi.org/10.1016/j.artmed.2023.102644 (2023).

    Article 

    Google Scholar 

  • Wang, G., Wang, Q., Iyer, G. N., Nag, A. & John, D. Unsupervised pre-training using masked autoencoders for ecg analysis. In 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), 1–5, https://doi.org/10.1109/BioCAS58349.2023.10388636 (2023).

  • Sanjana, K., Sowmya, V., Gopalakrishnan, E. A. & Soman, K. P. Explainable artificial intelligence for heart rate variability in ECG signal. Healthc. Technol. Lett. 7, 146–154. https://doi.org/10.1049/htl.2020.0033 (2020).

    Article 

    Google Scholar 

  • Peimankar, A., Ebrahimi, A. & Wiil, U. K. xECG-Beats: an explainable deep transfer learning approach for ECG-based heartbeat classification. Netw. Model. Analysis Heal. Informatics Bioinforma. 13, https://doi.org/10.1007/s13721-024-00481-2 (2024).

  • Wagner, P., Strodthoff, N., Bousseljot, R.-D., Samek, W. & Schaeffter, T. PTB-XL, a large publicly available electrocardiography dataset. PhysioNet https://doi.org/10.13026/x4td-x982 (2022). Version 1.0.3.

  • Wagner, P. et al. PTB-XL, a large publicly available electrocardiography dataset. Sci. Data 7, 154. https://doi.org/10.1038/s41597-020-0495-6 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Perez Alday, E. A. et al. Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology challenge 2020. Physiol. Meas. 41, 124003. https://doi.org/10.1088/1361-6579/abc960 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu, F. et al. An open access database for evaluating the algorithms of ECG rhythm and morphology abnormal detection. J. Med. Imaging Heal. Informatics 8, 1368–1373. https://doi.org/10.1166/jmihi.2018.2442 (2018).

    Article 

    Google Scholar 

  • Zheng, J. et al. A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients. Sci. Data 7, 48. https://doi.org/10.1038/s41597-020-0386-x (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Maršánová, L. et al. MIT-BIH arrhythmia database P-Wave annotations. PhysioNet https://doi.org/10.13026/C2108F (2018). Version 1.0.0.

  • Goldberger, A. L. et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101, e215–e220. https://doi.org/10.1161/01.CIR.101.23.e215 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Kalyakulina, A. et al. Lobachevsky university electrocardiography database. PhysioNet https://doi.org/10.13026/eegm-h675 (2021). Version 1.0.1.

  • Němcová, A. et al. Brno university of technology ECG quality database (BUT QDB). PhysioNet https://doi.org/10.13026/kah4-0w24 (2020). Version 1.0.0.

  • Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Lawrence Erlbaum Associates, Hillsdale, NJ, 1988), 2nd edn. https://doi.org/10.4324/9780203771587.

  • Hammer, A., Zannini, M., Malberg, H. & Schmidt, M. Transfer learning to focus self-learning ai on rhythm improves interpretability in atrial fibrillation detection. In Computing in Cardiology Conference (CinC), vol. 52, https://doi.org/10.22489/CinC.2025.396 (2025).

  • Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 27, vol. 27, 3320–3328, https://doi.org/10.48550/arXiv.1411.1792 (Curran Associates, Inc., 2014).

  • Tan, M. J. T., Kasireddy, H. R., Satriya, A. B., Abdul Karim, H. & AlDahoul, N. Health is beyond genetics: on the integration of lifestyle and environment in real-time for hyper-personalized medicine. Front. Public Heal. 12, https://doi.org/10.3389/fpubh.2024.1522673 (2025).

  • Duarte, R. et al. Lead-i ecg for detecting atrial fibrillation in patients with an irregular pulse using single time point testing: a systematic review and economic evaluation. Heal. Technol. Assess. 24, 1–164. https://doi.org/10.3310/hta24030 (2020).

    Article 

    Google Scholar 

  • Rahul, J., Sora, M. & Sharma, L. D. A novel and lightweight p, QRS, and T peaks detector using adaptive thresholding and template waveform. Comput. Biol. Medicine 132, 104307. https://doi.org/10.1016/j.compbiomed.2021.104307 (2021).

    Article 

    Google Scholar 

  • Makowski, D. et al. NeuroKit2: A python toolbox for neurophysiological signal processing. Behav. Res. Methods 53, 1689–1696. https://doi.org/10.3758/s13428-020-01516-y (2021).

    Article 
    PubMed 

    Google Scholar 

  • Moody, G., Pollard, T. & Moody, B. WFDB software package. PhysioNet https://doi.org/10.13026/zzpx-h016 (2021).

    Article 

    Google Scholar 

  • Butterworth, S. On the theory of filter amplifiers. Experimental Wireless & the Wireless Engineer 7, 536–541 (1930).

    Google Scholar 

  • Johnson, A., Behar, J., Andreotti, F., Clifford, G. & Oster, J. R peak estimation using multimodal lead switching. Computing in Cardiology 41, 281–284 (2014).

    Google Scholar 

  • Khamis, H. et al. Qrs detection algorithm for telehealth electrocardiogram recordings. IEEE Transactions on Biomedical Engineering 63, 1377–1388. https://doi.org/10.1109/TBME.2016.2549060 (2016).

    Article 
    PubMed 

    Google Scholar 

  • Moeyersons, J., Amoni, M., Van Huffel, S., Willems, R. & Varon, C. R-deco: An open-source matlab based graphical user interface for the detection and correction of r-peaks. PeerJ Computer Science 5, e226. https://doi.org/10.7717/peerj-cs.226 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Emrich, J., Koka, T., Wirth, S. & Muma, M. Accelerated sample-accurate r-peak detectors based on visibility graphs. 2023 31st European Signal Processing Conference (EUSIPCO) https://doi.org/10.23919/EUSIPCO58844.2023.10290007 (2023).

    Article 

    Google Scholar 

  • Huber, P. J. Robust estimation of a location parameter. The Annals of Mathematical Statistics 35, 73–101. https://doi.org/10.1214/aoms/1177703732 (1964).

    Article 
    MathSciNet 

    Google Scholar 

  • Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations (ICLR 2015) https://doi.org/10.48550/arXiv.1412.6980 (2015).

    Article 

    Google Scholar 

  • Schmidt, M., Baumert, M., Porta, A., Malberg, H. & Zaunseder, S. Two-dimensional warping for one-dimensional signals: Conceptual framework and application to ecg processing. IEEE Transactions on Signal Processing 62, 5577–5588. https://doi.org/10.1109/TSP.2014.2354313 (2014).

    Article 
    ADS 
    MathSciNet 

    Google Scholar 

  • Schmidt, M., Baumert, M., Malberg, H. & Zaunseder, S. Iterative two-dimensional signal warping–towards a generalized approach for adaption of one-dimensional signals. Biomed. Signal Process. Control. 43, 311–319. https://doi.org/10.1016/j.bspc.2018.03.016 (2018).

    Article 

    Google Scholar 



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