{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:29:16Z","timestamp":1776277756295,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The electrocardiogram (ECG) is considered a fundamental of cardiology. The ECG consists of P, QRS, and T waves. Information provided from the signal based on the intervals and amplitudes of these waves is associated with various heart diseases. The first step in isolating the features of an ECG begins with the accurate detection of the R-peaks in the QRS complex. The database was based on the PTB-XL database, and the signals from Lead I\u2013XII were analyzed. This research focuses on determining the Few-Shot Learning (FSL) applicability for ECG signal proximity-based classification. The study was conducted by training Deep Convolutional Neural Networks to recognize 2, 5, and 20 different heart disease classes. The results of the FSL network were compared with the evaluation score of the neural network performing softmax-based classification. The neural network proposed for this task interprets a set of QRS complexes extracted from ECG signals. The FSL network proved to have higher accuracy in classifying healthy\/sick patients ranging from 93.2% to 89.2% than the softmax-based classification network, which achieved 90.5\u201389.2% accuracy. The proposed network also achieved better results in classifying five different disease classes than softmax-based counterparts with an accuracy of 80.2\u201377.9% as opposed to 77.1% to 75.1%. In addition, the method of R-peaks labeling and QRS complexes extraction has been implemented. This procedure converts a 12-lead signal into a set of R waves by using the detection algorithms and the k-mean algorithm.<\/jats:p>","DOI":"10.3390\/s22030904","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T21:07:11Z","timestamp":1643144831000},"page":"904","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3989-3400","authenticated-orcid":false,"given":"Krzysztof","family":"Pa\u0142czy\u0144ski","sequence":"first","affiliation":[{"name":"Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2459-5494","authenticated-orcid":false,"given":"Sandra","family":"\u015amigiel","sequence":"additional","affiliation":[{"name":"Faculty of Mechanical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0796-4390","authenticated-orcid":false,"given":"Damian","family":"Ledzi\u0144ski","sequence":"additional","affiliation":[{"name":"Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9626-5905","authenticated-orcid":false,"given":"S\u0142awomir","family":"Bujnowski","sequence":"additional","affiliation":[{"name":"Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, 85-796 Bydgoszcz, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2085","DOI":"10.1007\/s11277-020-07960-5","article-title":"Design and Modeling of a Compact Power Divider with Squared Resonators Using Artificial Intelligence","volume":"117","author":"Roshani","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1108\/IJHMA-05-2020-0067","article-title":"Forecasting house prices in Iran using GMDH","volume":"14","author":"Nazemi","year":"2020","journal-title":"Int. J. Hous. Mark. Anal."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101804","DOI":"10.1016\/j.flowmeasinst.2020.101804","article-title":"Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter","volume":"75","author":"Roshani","year":"2020","journal-title":"Flow Meas. Instrum."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Narwariya, J., Malhotra, P., Vig, L., Shroff, G., and Vishnu, T.V. (2020, January 5\u20137). Meta-learning for few-shot time series classification. Proceedings of the 7th ACM IKDD CoDS and 25th COMAD, Hyderabad, India.","DOI":"10.1145\/3371158.3371162"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Che, Z., Purushotham, S., Cho, K., Sontag, D., and Liu, Y. (2018). Recurrent neural networks for multivariate time series with missing values. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-24271-9"},{"key":"ref_6","unstructured":"Rajpurkar, P., Hannun, A.Y., Haghpanahi, M., Bourn, C., and Ng, A.Y. (2017). Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mahajan, R., Kamaleswaran, R., Howe, J.A., and Akbilgicm, O. (2017, January 24\u201327). Cardiac rhythm classification from a short single lead ECG recording via random forest. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France.","DOI":"10.22489\/CinC.2017.179-403"},{"key":"ref_8","unstructured":"Yang, J., Nguyen, M.N., San, P.P., Li, X.L., and Krishnaswamy, S. (2015, January 25\u201331). Deep convolutional neural networks on multichannel time series for human activity recognition. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/RBME.2020.2976507","article-title":"A review on the state of the art in atrial fibrillation detection enabled by machine learning","volume":"14","author":"Rizwan","year":"2020","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1109\/RBME.2018.2885714","article-title":"Deep learning in cardiology","volume":"12","author":"Bizopoulos","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_11","unstructured":"Chandra, B.S., Sastry, C.S., Jana, S., and Patidar, S. (2017, January 24\u201327). Atrial fibrillation detection using convolutional neural networks. Proceedings of the 2017 Computing in Cardiology (CinC), Rennes, France."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Rundo, F., Conoci, S., Ortis, A., and Battiato, S. (2018). An advanced bio-inspired photoplethysmography (PPG) and ECG pattern recognition system for medical assessment. Sensors, 18.","DOI":"10.3390\/s18020405"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/ACCESS.2017.2779939","article-title":"LSTM fully convolutional networks for time series classification","volume":"6","author":"Karim","year":"2017","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Fawaz","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kashiparekh, K., Narwariya, J., Malhotra, P., Vig, L., and Shroff, G. (2019, January 14\u201319). ConvTimeNet: A pre-trained deep convolutional neural network for time series classification. Proceedings of the 2019 International Joint Conference on Neural Networks, Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852105"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"\u015amigiel, S., Pa\u0142czy\u0144ski, K., and Ledzi\u0144ski, D. (2021). ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset. Entropy, 23.","DOI":"10.3390\/e23091121"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e146","DOI":"10.1161\/CIR.0000000000000485","article-title":"Heart disease and stroke statistics\u20142017 update: A report from the American Heart Association","volume":"135","author":"Benjamin","year":"2017","journal-title":"Circulation"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.jelectrocard.2018.02.007","article-title":"Learning and teaching electrocardiography in the 21st century: A neglected art","volume":"51","author":"Shenasa","year":"2018","journal-title":"J. Electrocardiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"97082","DOI":"10.1109\/ACCESS.2020.2997473","article-title":"QRS complex detection using novel deep learning neural networks","volume":"8","author":"Cai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Rashkovska, A., Depolli, M., Toma\u0161i\u0107, I., Avbelj, V., and Trobec, R. (2020). Medical-grade ECG sensor for long-term monitoring. Sensors, 20.","DOI":"10.3390\/s20061695"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"\u0160arlija, M., Juri\u0161i\u0107, F., and Popovi\u0107, S. (2017, January 18\u201320). A convolutional neural network based approach to QRS detection. Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, Ljubljana, Slovenia.","DOI":"10.1109\/ISPA.2017.8073581"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"045004","DOI":"10.1088\/1361-6579\/aab297","article-title":"A deep learning approach for fetal QRS complex detection","volume":"39","author":"Zhong","year":"2018","journal-title":"Physiol. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-018-0441-4","article-title":"Automatic QRS complex detection using two-level convolutional neural network","volume":"17","author":"Xiang","year":"2018","journal-title":"Biomed. Eng. Online"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"115528","DOI":"10.1016\/j.eswa.2021.115528","article-title":"A deep neural network approach to QRS detection using autoencoders","volume":"184","author":"Belkadi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guo, Z., Wang, Y., Liu, L., Sun, S., Feng, B., and Zhao, X. (2021, January 18\u201320). Siamese Network-Based Few-Shot Learning for Classification of Human Peripheral Blood Leukocyte. Proceedings of the 2021 IEEE 4th International Conference on Electronic Information and Communication Technology (ICEICT), Xi\u2019an, China.","DOI":"10.1109\/ICEICT53123.2021.9531084"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Voulodimos, A., Protopapadakis, E., Katsamenis, I., Doulamis, A., and Doulamis, N. (2021). A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images. Sensors, 21.","DOI":"10.3390\/s21062215"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"194158","DOI":"10.1109\/ACCESS.2020.3033069","article-title":"2019 Novel coronavirus-infected pneumonia on CT: A feasibility study of few-shot learning for computerized diagnosis of emergency diseases","volume":"8","author":"Lai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"26","DOI":"10.36244\/ICJ.2021.1.4","article-title":"Double-View Matching Network for Few-Shot Learning to Classify Covid-19 in X-ray images","volume":"13","year":"2021","journal-title":"Infocommun. J."},{"key":"ref_29","unstructured":"Prabhu, V., Kannan, A., Ravuri, M., Chaplain, M., Sontag, D., and Amatriain, X. (2019, January 8\u201310). Few-shot learning for dermatological disease diagnosis. Proceedings of the Machine Learning for Healthcare Conference, Ann Arbor, MI, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Xiao, J., Xu, H., Zhao, W., Cheng, C., and Gao, H. (2021). A Prior-mask-guided Few-shot Learning for Skin Lesion Segmentation. Computing, 1\u201323.","DOI":"10.1007\/s00607-021-00907-z"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1038\/s43018-020-00169-2","article-title":"Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients","volume":"2","author":"Ma","year":"2021","journal-title":"Nat. Cancer"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"An, S., Kim, S., Chikontwe, P., and Park, S.H. (January, January 24). Few-shot relation learning with attention for EEG-based motor imagery classification. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9340933"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103094","DOI":"10.1016\/j.dsp.2021.103094","article-title":"Few-shot learning for cardiac arrhythmia detection based on electrocardiogram data from wearable devices","volume":"116","author":"Liu","year":"2021","journal-title":"Digit. Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-020-0495-6","article-title":"PTB-XL, a large publicly available electrocardiography dataset (version 1.0.1)","volume":"7","author":"Wagner","year":"2020","journal-title":"Sci. Data"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_36","unstructured":"Hamilton, P.S. (2002). Open Source ECG Analysis Software Documentation, E.P. Limited."},{"key":"ref_37","unstructured":"Elgendi, M., Jonkman, M., and De Boer, F. (2010, January 20\u201323). Frequency Bands Effects on QRS Detection. Proceedings of the 3rd International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS2010), Valencia, Spain."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kalidas, V., and Tami, L. (2017, January 23\u201325). Real-time QRS detector using Stationary Wavelet Transform for Automated ECG Analysis. Proceedings of the 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE), Washington, DC, USA.","DOI":"10.1109\/BIBE.2017.00-12"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/1475-925X-3-28","article-title":"Real time electrocardiogram QRS detection using combined adaptive threshold","volume":"3","author":"Christov","year":"2004","journal-title":"Biomed. Eng. Online"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TBME.1985.325532","article-title":"A Real-Time QRS Detection Algorithm","volume":"BME-32","author":"Pan","year":"1985","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_41","first-page":"37","article-title":"A single scan algorithm for QRS detection and feature extraction","volume":"6","author":"Zeelenberg","year":"1979","journal-title":"IEEE Comp. Cardiol."},{"key":"ref_42","unstructured":"Lourenco, A., Silva, H., Leite, P., Lourenco, R., and Fred, A. (2012). Real Time Electrocardiogram Segmentation for Finger Based ECG Biometrics. Biosignals, 49\u201354."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_44","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_45","unstructured":"Caruana, R., Lawrence, S., and Giles, L. (December, January 27). Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. Proceedings of the 14th Annual Neural Information Processing Systems Conference, Denver, CO, USA."},{"key":"ref_46","unstructured":"Ha, M.L., and Blanz, V. (2021). Deep Ranking with Adaptive Margin Triplet Loss. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/904\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:07:07Z","timestamp":1760134027000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/904"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,25]]},"references-count":46,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030904"],"URL":"https:\/\/doi.org\/10.3390\/s22030904","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,25]]}}}