{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:23:04Z","timestamp":1774797784866,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T00:00:00Z","timestamp":1656460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ITEA INNO4HEALTH","award":["19008"],"award-info":[{"award-number":["19008"]}]},{"name":"ITEA INNO4HEALTH","award":["EINF-2491"],"award-info":[{"award-number":["EINF-2491"]}]},{"name":"ITEA INNO4HEALTH","award":["952215"],"award-info":[{"award-number":["952215"]}]},{"name":"SURF Cooperative","award":["19008"],"award-info":[{"award-number":["19008"]}]},{"name":"SURF Cooperative","award":["EINF-2491"],"award-info":[{"award-number":["EINF-2491"]}]},{"name":"SURF Cooperative","award":["952215"],"award-info":[{"award-number":["952215"]}]},{"name":"EU Horizon 2020 research and innovation programme","award":["19008"],"award-info":[{"award-number":["19008"]}]},{"name":"EU Horizon 2020 research and innovation programme","award":["EINF-2491"],"award-info":[{"award-number":["EINF-2491"]}]},{"name":"EU Horizon 2020 research and innovation programme","award":["952215"],"award-info":[{"award-number":["952215"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays, even with all the tremendous advances in medicine and health protocols, cardiovascular diseases (CVD) continue to be one of the major causes of death. In the present work, we focus on a specific abnormality: ST-segment deviation, which occurs regularly in high-performance athletes and elderly people, serving as a myocardial infarction (MI) indicator. It is usually diagnosed manually by experts, through visual interpretation of the printed electrocardiography (ECG) signal. We propose a methodology to detect ST-segment deviation and quantify its scale up to 1 mV by extracting statistical, point-to-point beat characteristics and signal quality indexes (SQIs) from single-lead ECG. We do so by applying automated machine learning methods to find the best hyperparameter configuration for classification and regression models. For validation of our method, we use the ST-T database from Physionet; the results show that our method obtains 98.30% accuracy in the case of a multiclass problem and 99.87% accuracy in the case of binarization.<\/jats:p>","DOI":"10.3390\/s22134919","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T22:43:28Z","timestamp":1656542608000},"page":"4919","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Interpretable Assessment of ST-Segment Deviation in ECG Time Series"],"prefix":"10.3390","volume":"22","author":[{"given":"Israel","family":"Campero Jurado","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3047-9329","authenticated-orcid":false,"given":"Andrejs","family":"Fedjajevs","sequence":"additional","affiliation":[{"name":"IMEC the Netherlands, Holst Centre, 5656 AE Eindhoven, The Netherlands"},{"name":"Philips, High Tech Campus 34, 5656 AE Eindhoven, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7044-9805","authenticated-orcid":false,"given":"Joaquin","family":"Vanschoren","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands"}]},{"given":"Aarnout","family":"Brombacher","sequence":"additional","affiliation":[{"name":"Department of Industrial Design, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1016\/S0140-6736(18)32203-7","article-title":"Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980\u20132017: A systematic analysis for the Global Burden of Disease Study 2017","volume":"392","author":"Harikrishnan","year":"2018","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.bspc.2018.03.003","article-title":"A survey on ECG analysis","volume":"43","author":"Berkaya","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1049\/htl.2014.0072","article-title":"Patient-specific ECG beat classification technique","volume":"1","author":"Das","year":"2014","journal-title":"Healthc. Technol. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.3758\/s13428-020-01516-y","article-title":"NeuroKit2: A Python toolbox for neurophysiological signal processing","volume":"53","author":"Makowski","year":"2021","journal-title":"Behav. Res. Methods"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1016\/j.jacc.2007.09.011","article-title":"Universal definition of myocardial infarction","volume":"50","author":"Thygesen","year":"2007","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1161\/01.CIR.82.4.1147","article-title":"Significance of initial ST segment elevation and depression for the management of thrombolytic therapy in acute myocardial infarction. European Cooperative Study Group for Recombinant Tissue-Type Plasminogen Activator","volume":"82","author":"Willems","year":"1990","journal-title":"Circulation"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ccep.2012.12.003","article-title":"ST-segment elevation and sudden death in the athlete","volume":"5","author":"Zorzi","year":"2013","journal-title":"Card. Electrophysiol. Clin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1136\/bmj.324.7344.1023","article-title":"Myocardial ischaemia","volume":"324","author":"Channer","year":"2002","journal-title":"BMJ"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1002\/clc.4960180409","article-title":"Effect of precordial electrocardiographic electrode placement on st-segment measurement during exercise","volume":"18","author":"Bertolet","year":"1995","journal-title":"Clin. Cardiol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Salem, M., Taheri, S., and Yuan, J.S. (2018, January 17\u201319). ECG arrhythmia classification using transfer learning from 2-dimensional deep CNN features. Proceedings of the 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), Cleveland, OH, USA.","DOI":"10.1109\/BIOCAS.2018.8584808"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.patrec.2019.02.016","article-title":"Classification of myocardial infarction with multi-lead ECG signals and deep CNN","volume":"122","author":"Baloglu","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wasimuddin, M., Elleithy, K., Abuzneid, A., Faezipour, M., and Abuzaghleh, O. (2021). Multiclass ECG signal analysis using global average-based 2-D convolutional neural network modeling. Electronics, 10.","DOI":"10.3390\/electronics10020170"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1109\/TBCAS.2018.2823275","article-title":"A real-time QRS detection system with PR\/RT interval and ST segment measurements for wearable ECG sensors using parallel delta modulators","volume":"12","author":"Tang","year":"2018","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1164","DOI":"10.1093\/oxfordjournals.eurheartj.a060332","article-title":"The European ST-T database: Standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography","volume":"13","author":"Taddei","year":"1992","journal-title":"Eur. Heart J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TITB.2010.2094197","article-title":"Diagnosis of cardiovascular abnormalities from compressed ECG: A data mining-based approach","volume":"15","author":"Sufi","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"727","DOI":"10.3389\/fphys.2018.00727","article-title":"SQI quality evaluation mechanism of single-lead ECG signal based on simple heuristic fusion and fuzzy comprehensive evaluation","volume":"9","author":"Zhao","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Campero Jurado, I., and Vanschoren, J. (2022, January 9\u201313). Multi-fidelity optimization method with Asynchronous Generalized Island Model for AutoML. Proceedings of the Genetic and Evolutionary Computation Conference Companion, Boston, MA, USA.","DOI":"10.1145\/3520304.3528917"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., and Leyton-Brown, K. (2013, January 11\u201314). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA.","DOI":"10.1145\/2487575.2487629"},{"key":"ref_20","unstructured":"Kohavi, R. (1995, January 20\u201325). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, QC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gijsbers, P., and Vanschoren, J. (2021). GAMA: A General Automated Machine Learning Assistant. Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track, Proceedings of the ECML PKDD 2020, Ghent, Belgium, 14\u201318 September 2020, Springer.","DOI":"10.1007\/978-3-030-67670-4_39"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bellido-Jim\u00e9nez, J.A., Est\u00e9vez, J., Vanschoren, J., and Garc\u00eda-Mar\u00edn, A.P. (2022). AgroML: An Open-Source Repository to Forecast Reference Evapotranspiration in Different Geo-Climatic Conditions Using Machine Learning and Transformer-Based Models. Agronomy, 12.","DOI":"10.3390\/agronomy12030656"},{"key":"ref_23","unstructured":"Li, L., Jamieson, K., Rostamizadeh, A., Gonina, E., Hardt, M., Recht, B., and Talwalkar, A. (2018, January 6\u20139). Massively parallel hyperparameter tuning. Proceedings of the ICLR 2019 Conference, Orleans, LA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Izzo, D., Ruci\u0144ski, M., and Biscani, F. (2012). The generalized island model. Parallel Architectures and Bioinspired Algorithms, Springer.","DOI":"10.1007\/978-3-642-28789-3_7"},{"key":"ref_25","unstructured":"(2022, June 24). Nvidia A100 GPUs Power the Modern Data Center. Available online: https:\/\/www.nvidia.com\/en-us\/data-center\/a100\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4919\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:40:38Z","timestamp":1760139638000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/13\/4919"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,29]]},"references-count":25,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22134919"],"URL":"https:\/\/doi.org\/10.3390\/s22134919","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,29]]}}}