{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:27:22Z","timestamp":1783132042121,"version":"3.54.6"},"reference-count":38,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry for University and Research","award":["ARS01_00860 - RNA\/COR 576347"],"award-info":[{"award-number":["ARS01_00860 - RNA\/COR 576347"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an innovative method for multiple lead electrocardiogram (ECG) monitoring based on Compressed Sensing (CS). The proposed method extends to multiple leads signals, a dynamic Compressed Sensing method, that were previously developed on a single lead. The dynamic sensing method makes use of a sensing matrix in which its elements are dynamically obtained from the signal to be compressed. In this method, for the application to multiple leads, it is proposed to use a single sensing matrix for which its elements are obtained from a combination of multiple leads. The proposed method is evaluated on a wide set of signals and acquired on healthy subjects and on subjects affected by different pathologies, such as myocardial infarction, cardiomyopathy, and bundle branch block. The experimental results demonstrated that the proposed method can be adopted for a Compression Ratio (CR) up to 10, without compromising signal quality. In particular, for CR= 10, it exhibits a percentage of root-mean-squared difference average among a wide set of ECG signals lower than 3%.<\/jats:p>","DOI":"10.3390\/s21217003","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:07:11Z","timestamp":1635113231000},"page":"7003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["ECG Monitoring Based on Dynamic Compressed Sensing of Multi-Lead Signals"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5215-3661","authenticated-orcid":false,"given":"Pasquale","family":"Daponte","sequence":"first","affiliation":[{"name":"Department of Engineering, University of Sannio, Corso Garibaldi, 107, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1896-2614","authenticated-orcid":false,"given":"Luca","family":"De Vito","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, Corso Garibaldi, 107, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7093-2733","authenticated-orcid":false,"given":"Grazia","family":"Iadarola","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, Corso Garibaldi, 107, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6854-3026","authenticated-orcid":false,"given":"Francesco","family":"Picariello","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, Corso Garibaldi, 107, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hammad, M., Iliyasu, A.M., Subasi, A., Ho, E.S.L., and El-Latif, A.A.A. (2021). A Multitier Deep Learning Model for Arrhythmia Detection. IEEE Trans. Instrum. Meas., 70.","DOI":"10.1109\/TIM.2020.3033072"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, J., Spicher, N., Warnecke, J.M., Haghi, M., Schwartze, J., and Deserno, T.M. (2021). Unobtrusive health monitoring in private spaces: The smart home. Sensors, 21.","DOI":"10.3390\/s21030864"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Frick, K. (2019, January 20\u201322). All-ECG: A least-number of leads ECG monitor for standard 12-lead ECG Tracking during Motion. Proceedings of the 2019 IEEE Healthcare Innovations and Point of Care Technologies, (HI-POCT), Bethesda, MD, USA.","DOI":"10.1109\/HI-POCT45284.2019.8962742"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1054\/jelc.2000.20347","article-title":"Uncertainty of the electrocardiogram: Old and new ideas for assessment and interpretation","volume":"33","author":"Lux","year":"2000","journal-title":"J. Electrocardiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1177\/0885066602017004011","article-title":"Book Review: Chou\u2019s electrocardiography in clinical practice: Adult and pediatric, 5th Edition By Borys Surawicz and Timothy K. Knilans WB Saunders, 2001","volume":"17","author":"Starobin","year":"2002","journal-title":"J. Intensive Care Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, J., Huang, Z., Zhang, W., Patil, A., Patil, K., Zhu, T., Shiroma, E.J., Schepps, M.A., and Harris, T.B. (2016, January 5\u20138). Wearable sensor based human posture recognition. Proceedings of the 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA.","DOI":"10.1109\/BigData.2016.7841004"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.bspc.2016.05.008","article-title":"Exploiting multi-scale signal information in joint compressed sensing recovery of multi-channel ECG signals","volume":"29","author":"Singh","year":"2016","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"553","DOI":"10.30638\/eemj.2011.077","article-title":"Methods of electromagnetic interference reduction in electrocardiographic signal acquisition","volume":"10","author":"Adochiei","year":"2011","journal-title":"Environ. Eng. Manag. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/JBHI.2014.2327194","article-title":"Compressed Sensing for bioelectric signals: A review","volume":"19","author":"Craven","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Iadarola, G., Poli, A., and Spinsante, S. (2021, January 17\u201320). Reconstruction of galvanic skin Response peaks via sparse representation. Proceedings of the 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Glasgow, UK.","DOI":"10.1109\/I2MTC50364.2021.9459905"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Daponte, P., De Vito, L., Iadarola, G., Picariello, F., and Rapuano, S. (2021, January 23\u201325). Deterministic Compressed Sensing of heart sound signals. Proceedings of the 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lausanne, Switzerland.","DOI":"10.1109\/MeMeA52024.2021.9478766"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2456","DOI":"10.1109\/TBME.2011.2156795","article-title":"Compressed Sensing for real-time energy-efficient ECG compression on wireless body sensor nodes","volume":"58","author":"Mamaghanian","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Surekha, K.S., and Patil, B.P. (2014, January 27\u201329). ECG signal compression using hybrid 1D and 2D wavelet transform. Proceedings of the 2014 Science and Information Conference, London, UK.","DOI":"10.1109\/SAI.2014.6918229"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.measurement.2016.05.085","article-title":"Hybrid encoding algorithm for real time compressed electrocardiogram acquisition","volume":"91","author":"Bera","year":"2016","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107252","DOI":"10.1016\/j.measurement.2019.107252","article-title":"Quality controlled ECG data compression based on 2D discrete cosine coefficient filtering and iterative JPEG2000 encoding","volume":"152","author":"Pandey","year":"2020","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Abdulbaqi, A.S., Saif, S.A.D.M.N., Falath, F.M.M., and Nawar, N.A.I. (2018, January 20\u201321). A proposed technique based on wavelet transform for electrocardiogram signal compression. Proceedings of the 2018 1st Annual International Conference on Information and Sciences (AiCIS), Fallujah, Iraq.","DOI":"10.1109\/AiCIS.2018.00049"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.irbm.2020.05.004","article-title":"A comparative analysis of performance of several wavelet based ECG data compression methodologies","volume":"42","author":"Sharma","year":"2021","journal-title":"IRBM"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-72656-6","article-title":"Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT","volume":"10","author":"Nemcova","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Laudato, G., Picariello, F., Scalabrino, S., Tudosa, I., De Vito, L., and Oliveto, R. (2021, January 11\u201313). Morphological classification of heartbeats in compressed ECG. Proceedings of the 14th International Conference on Health Informatics (HEALTHINF 2021), Vienna, Austria.","DOI":"10.5220\/0010236003860393"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9629","DOI":"10.1109\/JSEN.2018.2871411","article-title":"System-on-Chip solution for patients biometric: A Compressive Sensing-based approach","volume":"18","author":"Djelouat","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2973","DOI":"10.1109\/TIM.2014.2320393","article-title":"Simple and efficient Compressed Sensing encoder for wireless body area network","volume":"63","author":"Ravelomanantsoa","year":"2014","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3642","DOI":"10.1109\/TIM.2019.2936776","article-title":"Investigation of Kronecker-based recovery of compressed ECG signal","volume":"69","author":"Mitra","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108031","DOI":"10.1016\/j.measurement.2020.108031","article-title":"On the wavelet-based compressibility of continuous-time sampled ECG signal for e-health applications","volume":"164","author":"Maalej","year":"2020","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109803","DOI":"10.1016\/j.measurement.2021.109803","article-title":"ECG compressed sensing method with high compression ratio and dynamic model reconstruction","volume":"183","author":"Michaeli","year":"2021","journal-title":"Measurement"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"108259","DOI":"10.1016\/j.measurement.2020.108259","article-title":"A novel compressive sampling method for ECG wearable measurement systems","volume":"167","author":"Picariello","year":"2021","journal-title":"Measurement"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3389\/fhumd.2021.614309","article-title":"ATTICUS: Ambient-Intelligent Tele-monitoring and Telemetry for Incepting and Catering Over hUman Sustainability","volume":"3","author":"Laudato","year":"2021","journal-title":"Front. Hum. Dyn."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Iadarola, G., Daponte, P., Picariello, F., and De Vito, L. (July, January 1). A dynamic approach for Compressed Sensing of multi\u2013lead ECG signals. Proceedings of the 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Bari, Italy.","DOI":"10.1109\/MeMeA49120.2020.9137307"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2024","DOI":"10.1109\/TIM.2018.2811438","article-title":"Multichannel electrocardiogram reconstruction in wireless body sensor networks through weighted \u21131,2 minimization","volume":"67","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mamaghanian, H., Ansaloni, G., Atienza, D., and Vandergheynst, P. (2014, January 4\u20139). Power-efficient joint compressed sensing of multi-lead ECG signals. Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854435"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1515\/cdbme-2015-0017","article-title":"Compressed sensing of multi\u2013lead ECG signals by compressive multiplexing","volume":"1","author":"Tigges","year":"2015","journal-title":"Curr. Dir. Biomed. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Balestrieri, E., De Vito, L., Picariello, F., and Tudosa, I. (2019, January 26\u201328). A novel method for Compressed Sensing based Ssampling of ECG signals in Medical-IoT era. Proceedings of the 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Istanbul, Turkey.","DOI":"10.1109\/MeMeA.2019.8802184"},{"key":"ref_33","unstructured":"Burke, M.J., and Nasor, M. (2001, January 8\u201315). ECG Analysis using the mexican-hat wavelet. Proceedings of the 5th WSES International Conference on Circuits, Systems, Communications and Computers (CSCC 2001), Rethymno, Greece."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1136\/bmj.324.7334.415","article-title":"Introduction. I\u2014Leads, rate, rhythm, and cardiac axis","volume":"324","author":"Meek","year":"2002","journal-title":"BMJ"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3704","DOI":"10.1109\/TSP.2007.894265","article-title":"An empirical Bayesian strategy for solving the simultaneous sparse approximation problem","volume":"55","author":"Wipf","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2477","DOI":"10.1109\/TSP.2005.849172","article-title":"Sparse solutions to linear inverse problems with multiple measurement vectors","volume":"53","author":"Cotter","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_37","unstructured":"(2021, October 13). PTB Diagnostic ECG Database, PhysioBank Clinical Database. Available online: https:\/\/physionet.org\/content\/ptbdb\/."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Rao, B.D. (2011, January 22\u201327). Iterative reweighted algorithms for sparse signal recovery with temporally correlated source vectors. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947212"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7003\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:20:57Z","timestamp":1760167257000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,22]]},"references-count":38,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217003"],"URL":"https:\/\/doi.org\/10.3390\/s21217003","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,22]]}}}