{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T19:29:08Z","timestamp":1770838148769,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T00:00:00Z","timestamp":1736812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Billions of paper Electrocardiograms (ECGs) are recorded annually worldwide, particularly in the Global South. Manual review of this massive dataset is time-consuming and inefficient. Accurate digital reconstruction of these records is essential for efficient cardiac disease diagnosis. This paper proposes a systematic framework for digitizing paper ECGs with 12 symmetrically distributed leads and identifying abnormal samples. This method consists of three main components. First, we introduce an adaptive rotated convolution network to detect the positions of lead waveforms. By exploiting the symmetric distribution of 12 leads, a novel loss is proposed to improve the detection model\u2019s performance. Second, image processing techniques, including denoising and connected component analysis, are employed to digitize ECG waveforms. Finally, we propose a transformer-based classification method combined with a state space model. Our process is evaluated on a large synthetic dataset, including ECG images characterized by rotations, noise, and creases. The results demonstrate that the proposed detection method can effectively reconstruct paper ECGs, achieving an 11% improvement in SNR compared to the baseline. Moreover, our classification model exhibits slightly higher performance than other counterparts. The proposed approach offers a promising solution for the automated analysis of paper ECGs, supporting clinical decision-making.<\/jats:p>","DOI":"10.3390\/sym17010120","type":"journal-article","created":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T06:13:07Z","timestamp":1736835187000},"page":"120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Systematic Method Combining Rotated Convolution and State Space Augmented Transformer for Digitizing and Classifying Paper ECGs"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3680-9947","authenticated-orcid":false,"given":"Xiang","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, H., Lin, Z., An, Z., Zuo, S., Zhu, W., Zhang, Z., Mu, Y., Cao, L., and Garcia, J.D.P. (2022). Automatic electrocardiogram detection and classification using bidirectional long short-term memory network improved by Bayesian optimization. Biomed. Signal Process. Control., 73.","DOI":"10.1016\/j.bspc.2021.103424"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Murat, F., Yildirim, O., Talo, M., Baloglu, U.B., Demir, Y., and Acharya, U.R. (2020). Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Comput. Biol. Med., 120.","DOI":"10.1016\/j.compbiomed.2020.103726"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e005289","DOI":"10.1161\/CIRCOUTCOMES.118.005289","article-title":"Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery","volume":"12","author":"Tison","year":"2019","journal-title":"Circ. Cardiovasc. Qual. Outcomes"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"055019","DOI":"10.1088\/1361-6579\/ad4954","article-title":"ECG-Image-Kit: A synthetic image generation toolbox to facilitate deep learning-based electrocardiogram digitization","volume":"45","author":"Shivashankara","year":"2024","journal-title":"Physiol. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"42521","DOI":"10.1109\/ACCESS.2023.3271137","article-title":"A novel approach for cardiotocography paper digitization and classification for abnormality detection","volume":"11","author":"Aksoy","year":"2023","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sun, X., Li, Q., Wang, K., He, R., and Zhang, H. (2019, January 8\u201311). A Novel Method for ECG Paper Records Digitization. Proceedings of the 2019 Computing in Cardiology (CinC), Singapore.","DOI":"10.22489\/CinC.2019.264"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wu, H., Patel, K.H.K., Li, X., Zhang, B., Galazis, C., Bajaj, N., Sau, A., Shi, X., Sun, L., and Tao, Y. (2022). A fully-automated paper ECG digitisation algorithm using deep learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-25284-1"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Randazzo, V., Puleo, E., Paviglianiti, A., Vallan, A., and Pasero, E. (2022). Development and Validation of an Algorithm for the Digitization of ECG Paper Images. Sensors, 22.","DOI":"10.3390\/s22197138"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1800107","DOI":"10.1109\/JTEHM.2013.2262024","article-title":"Novel tool for complete digitization of paper electrocardiography data","volume":"1","author":"Ravichandran","year":"2013","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2019.2949784","article-title":"High precision digitization of paper-based ECG records: A step toward machine learning","volume":"7","author":"Baydoun","year":"2019","journal-title":"IEEE J. Transl. Eng. Health Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1007\/s40846-021-00632-0","article-title":"ECG paper record digitization and diagnosis using deep learning","volume":"41","author":"Mishra","year":"2021","journal-title":"J. Med Biol. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yu, X., Huang, Y., Wu, J., Wang, J., and Cai, W. (2024, January 8\u201311). From Paper to Digital: ECG Processing with U-Net Digitization and ResNet Classification. Proceedings of the 51st Computing in Cardiology Conference, Karlsruhe, Germany.","DOI":"10.22489\/CinC.2024.134"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, Y., Qu, Q., Wang, M., Yu, L., Wang, J., Shen, L., and He, K. (2020). Deep learning for digitizing highly noisy paper-based ECG records. Comput. Biol. Med., 127.","DOI":"10.1016\/j.compbiomed.2020.104077"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Petmezas, G., Papageorgiou, V.E., Vassilikos, V., Pagourelias, E., Tsaklidis, G., Katsaggelos, A.K., and Maglaveras, N. (2024). Recent advancements and applications of deep learning in heart failure: A systematic review. Comput. Biol. Med., 176.","DOI":"10.1016\/j.compbiomed.2024.108557"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100033","DOI":"10.1016\/j.eswax.2020.100033","article-title":"A review on deep learning methods for ECG arrhythmia classification","volume":"7","author":"Ebrahimi","year":"2020","journal-title":"Expert Systems with Applications: X"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pessoa, D., Petmezas, G., Papageorgiou, V.E., Rocha, B.M., Stefanopoulos, L., Kilintzis, V., Maglaveras, N., Frerichs, I., de Carvalho, P., and Paiva, R.P. (2023, January 19\u201321). Pediatric Respiratory Sound Classification Using a Dual Input Deep Learning Architecture. Proceedings of the 2023 IEEE Biomedical Circuits and Systems Conference (BioCAS), Toronto, ON, Canada.","DOI":"10.1109\/BioCAS58349.2023.10388733"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Natarajan, A., Chang, Y., Mariani, S., Rahman, A., Boverman, G., Vij, S., and Rubin, J. (2020, January 13\u201316). A wide and deep transformer neural network for 12-lead ECG classification. Proceedings of the 2020 Computing in Cardiology, Rimini, Italy.","DOI":"10.22489\/CinC.2020.107"},{"key":"ref_18","unstructured":"Mehari, T., and Strodthoff, N. (2022). Advancing the state-of-the-art for ECG analysis through structured state space models. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/TETCI.2023.3235374","article-title":"A multi-view multi-scale neural network for multi-label ECG classification","volume":"7","author":"Yang","year":"2023","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, S.W., Wang, S.L., Qi, X.Z., Samuri, S.M., and Yang, C. (2022). Review of ECG detection and classification based on deep learning: Coherent taxonomy, motivation, open challenges and recommendations. Biomed. Signal Process. Control., 74.","DOI":"10.1016\/j.bspc.2022.103493"},{"key":"ref_21","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, S., Luo, P., Loy, C.C., and Tang, X. (2016, January 27\u201330). Wider face: A face detection benchmark. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.596"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201323). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding vertex on the horizontal bounding box for multi-oriented object detection","volume":"43","author":"Xu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, W., Chen, Y., Hu, K., and Zhu, J. (2022, January 18\u201324). Oriented reppoints for aerial object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00187"},{"key":"ref_26","first-page":"4335","article-title":"Detecting rotated objects as gaussian distributions and its 3-d generalization","volume":"45","author":"Yang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Qian, W., Yang, X., Peng, S., Yan, J., and Guo, Y. (2021, January 2\u20139). Learning modulated loss for rotated object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i3.16347"},{"key":"ref_28","first-page":"18381","article-title":"Learning high-precision bounding box for rotated object detection via kullback-leibler divergence","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_29","unstructured":"Yang, X., Zhou, Y., Zhang, G., Yang, J., Wang, W., Yan, J., Zhang, X., and Tian, Q. (2022). The KFIoU loss for rotated object detection. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Feng, Z., and He, T. (2021, January 2\u20139). R3det: Refined single-stage detector with feature refinement for rotating object. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/TPAMI.2022.3166956","article-title":"Scrdet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing","volume":"45","author":"Yang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Hou, L., Lu, K., Xue, J., and Li, Y. (March, January 27). Shape-adaptive selection and measurement for oriented object detection. Proceedings of the AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5625411","DOI":"10.1109\/TGRS.2022.3183022","article-title":"Anchor-free oriented proposal generator for object detection","volume":"60","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pu, Y., Wang, Y., Xia, Z., Han, Y., Wang, Y., Gan, W., Wang, Z., Song, S., and Huang, G. (2023, January 1\u20136). Adaptive rotated convolution for rotated object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.00606"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_37","unstructured":"Gu, A., Goel, K., and R\u00e9, C. (2021). Efficiently modeling long sequences with structured state spaces. arXiv."},{"key":"ref_38","first-page":"1474","article-title":"Hippo: Recurrent memory with optimal polynomial projections","volume":"33","author":"Gu","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Alcaraz, J.M.L., and Strodthoff, N. (2023). Diffusion-based conditional ECG generation with structured state space models. Comput. Biol. Med., 163.","DOI":"10.1016\/j.compbiomed.2023.107115"},{"key":"ref_40","unstructured":"Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., and Wang, X. (2024). Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1049\/cit2.12085","article-title":"Probabilistic time series forecasting with deep non-linear state space models","volume":"8","author":"Du","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_42","unstructured":"Alcaraz, J.M.L., and Strodthoff, N. (2022). Diffusion-based time series imputation and forecasting with structured state space models. arXiv."},{"key":"ref_43","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.aiopen.2022.10.001","article-title":"A survey of transformers","volume":"3","author":"Lin","year":"2022","journal-title":"AI Open"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (2021, January 2\u20139). Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Virtually.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"ref_46","unstructured":"Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R. (2022, January 17\u201323). Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. Proceedings of the International Conference on Machine Learning. PMLR, Baltimore, MD, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, W., Wang, W., Peng, B., Wen, Q., Zhou, T., and Sun, L. (2022, January 14\u201318). Learning to rotate: Quaternion transformer for complicated periodical time series forecasting. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/3534678.3539234"},{"key":"ref_48","unstructured":"Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A.X., and Dustdar, S. (2021, January 3\u20137). Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_49","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_50","unstructured":"Beltagy, I., Peters, M.E., and Cohan, A. (2020). Longformer: The long-document transformer. arXiv."},{"key":"ref_51","unstructured":"Kitaev, N., Kaiser, \u0141., and Levskaya, A. (2020). Reformer: The efficient transformer. arXiv."},{"key":"ref_52","unstructured":"Zuo, S., Liu, X., Jiao, J., Charles, D., Manavoglu, E., Zhao, T., and Gao, J. (2022). Efficient long sequence modeling via state space augmented transformer. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL, a large publicly available electrocardiography dataset","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci. Data"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Krull, A., Buchholz, T.O., and Jug, F. (2019, January 15\u201320). Noise2void-learning denoising from single noisy images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00223"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Papageorgiou, V.E., Zegkos, T., Efthimiadis, G., and Tsaklidis, G. (2022). Analysis of digitalized ECG signals based on artificial intelligence and spectral analysis methods specialized in ARVC. Int. J. Numer. Methods Biomed. Eng., 38.","DOI":"10.1002\/cnm.3644"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s43069-023-00288-3","article-title":"Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume","volume":"5","author":"Georgakis","year":"2024","journal-title":"Oper. Res. 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