{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T16:07:38Z","timestamp":1789834058368,"version":"4.0.1"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T00:00:00Z","timestamp":1639094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008332","name":"Graz University of Technology","doi-asserted-by":"publisher","award":["Lead Project Aortic Dissection"],"award-info":[{"award-number":["Lead Project Aortic Dissection"]}],"id":[{"id":"10.13039\/100008332","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>An aortic dissection, a particular aortic pathology, occurs when blood pushes through a tear between the layers of the aorta and forms a so-called false lumen. Aortic dissection has a low incidence compared to other diseases, but a relatively high mortality that increases with disease progression. An early identification and treatment increases patients\u2019 chances of survival. State-of-the-art medical imaging techniques have several disadvantages; therefore, we propose the detection of aortic dissections through their signatures in impedance cardiography signals. These signatures arise due to pathological blood flow characteristics and a blood conductivity that strongly depends on the flow field, i.e., the proposed method is, in principle, applicable to any aortic pathology that changes the blood flow characteristics. For the signal classification, we trained a convolutional neural network (CNN) with artificial impedance cardiography data based on a simulation model for a healthy virtual patient and a virtual patient with an aortic dissection. The network architecture was tailored to a multi-sensor, multi-channel time-series classification with a categorical cross-entropy loss function as the training objective. The trained network typically yielded a specificity of (93.9\u00b10.1)% and a sensitivity of (97.5\u00b10.1)%. A study of the accuracy as a function of the size of an aortic dissection yielded better results for a small false lumen with larger noise, which emphasizes the question of the feasibility of detecting aortic dissections in an early state.<\/jats:p>","DOI":"10.3390\/e23121661","type":"journal-article","created":{"date-parts":[[2021,12,10]],"date-time":"2021-12-10T08:17:58Z","timestamp":1639124278000},"page":"1661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Cross-Entropy Learning for Aortic Pathology Classification of Artificial Multi-Sensor Impedance Cardiography Signals"],"prefix":"10.3390","volume":"23","author":[{"given":"Tobias","family":"Spindelb\u00f6ck","sequence":"first","affiliation":[{"name":"Institute of Theoretical and Computational Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8956-2576","authenticated-orcid":false,"given":"Sascha","family":"Ranftl","sequence":"additional","affiliation":[{"name":"Institute of Theoretical and Computational Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria"},{"name":"Graz Center of Computational Engineering, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7436-5078","authenticated-orcid":false,"given":"Wolfgang","family":"von der Linden","sequence":"additional","affiliation":[{"name":"Institute of Theoretical and Computational Physics, Graz University of Technology, Petersgasse 16, 8010 Graz, Austria"},{"name":"Graz Center of Computational Engineering, Graz University of Technology, Krenngasse 37, 8010 Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,10]]},"reference":[{"key":"ref_1","first-page":"694ff","article-title":"Aortic dissection: A 250-year perspective","volume":"38","author":"Criado","year":"2011","journal-title":"Tex. Heart Inst. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0140-6736(08)60994-0","article-title":"Acute aortic dissection","volume":"372","author":"Golledge","year":"2008","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1161\/CIRCULATIONAHA.110.958975","article-title":"Acute Aortic Dissection","volume":"122","author":"Braverman","year":"2010","journal-title":"Circulation"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"38","DOI":"10.2478\/joeb-2020-0007","article-title":"Electrode positioning to investigate the changes of the thoracic bioimpedance caused by aortic dissection\u2014A simulation study","volume":"11","author":"Badeli","year":"2020","journal-title":"J. Electr. Bioimpedance"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1053\/euhj.2001.2782","article-title":"Diagnosis and management of aortic dissection Task Force on Aortic Dissection, European Society of Cardiology","volume":"22","author":"Erbel","year":"2001","journal-title":"Eur. Heart J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1016\/S0002-9149(02)02198-7","article-title":"Usefulness of pulse deficit to predict in-hospital complications and mortality in patients with acute type A aortic dissection","volume":"89","author":"Bossone","year":"2002","journal-title":"Am. J. Cardiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Brooks, M. (2020). Review of Studies Reporting the Incidence of Acute Type B Aortic Dissection. Hearts, 1.","DOI":"10.3390\/hearts1030016"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1186\/s40560-018-0287-7","article-title":"Management of acute aortic dissection and thoracic aortic rupture","volume":"6","author":"Fukui","year":"2018","journal-title":"J. Intensive Care"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1378\/chest.122.1.311","article-title":"Clinical, Diagnostic, and Management Perspectives of Aortic Dissection","volume":"122","author":"Khan","year":"2002","journal-title":"Chest"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1093\/ejcts\/ezy313","article-title":"Current options and recommendations for the treatment of thoracic aortic pathologies involving the aortic arch: An expert consensus document of the European Association for Cardio-Thoracic surgery (EACTS) and the European Society for Vascular Surgery (ESVS)","volume":"55","author":"Czerny","year":"2018","journal-title":"Eur. J. Cardio-Thorac. Surg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.jcmg.2013.10.015","article-title":"The Role of Imaging in Aortic Dissection and Related Syndromes","volume":"7","author":"Baliga","year":"2014","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101773","DOI":"10.1016\/j.media.2020.101773","article-title":"Detection, segmentation, simulation and visualization of aortic dissections: A review","volume":"65","author":"Pepe","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1097\/00005382-200101000-00006","article-title":"Imaging of Aortic Aneurysms and Dissection: CT and MRI","volume":"16","author":"Hartnell","year":"2001","journal-title":"J. Thorac. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1053\/j.jvca.2013.12.026","article-title":"Electrical Bioimpedance Cardiography: An Old Technology with New Hopes for the Future","volume":"28","author":"Fellahi","year":"2014","journal-title":"J. Cardiothorac. Vasc. Anesth."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1088\/0967-3334\/27\/9\/005","article-title":"Impedance cardiography revisited","volume":"27","author":"Cotter","year":"2006","journal-title":"Physiol. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1111\/j.1469-8986.1978.tb01340.x","article-title":"Impedance Cardiography","volume":"15","author":"Miller","year":"1978","journal-title":"Psychophysiology"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Badeli, V., Ranftl, S., Melito, G.M., Reinbacher-K\u00f6stinger, A., von der Linden, W., Ellermann, K., and Bir\u00f3, O. (2021). Bayesian Inference of Multi-sensors Impedance Cardiography for Detection of Aortic Dissection. Compel, in press.","DOI":"10.1108\/COMPEL-03-2021-0072"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ranftl, S., Melito, G.M., Badeli, V., Reinbacher-K\u00f6stinger, A., Ellermann, K., and Linden, W.v.d. (2019). On the Diagnosis of Aortic Dissection with Impedance Cardiography: A Bayesian Feasibility Study Framework with Multi-Fidelity Simulation Data. Proceedings, 33.","DOI":"10.3390\/proceedings2019033024"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ranftl, S., Melito, G.M., Badeli, V., Reinbacher-K\u00f6stinger, A., Ellermann, K., and von der Linden, W. (2020). Bayesian Uncertainty Quantification with Multi-Fidelity Data and Gaussian Processes for Impedance Cardiography of Aortic Dissection. Entropy, 22.","DOI":"10.3390\/e22010058"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101758","DOI":"10.1016\/j.bspc.2019.101758","article-title":"Automatic diagnosis of valvular heart diseases by impedance cardiography signal processing","volume":"57","author":"Salah","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/BF02344724","article-title":"Stroke volume equation for impedance cardiography","volume":"43","author":"Bernstein","year":"2005","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1111\/j.1553-2712.2003.tb00054.x","article-title":"Bench to Bedside: Electrophysiologic and Clinical Principles of Noninvasive Hemodynamic Monitoring Using Impedance Cardiography","volume":"10","author":"Summers","year":"2003","journal-title":"Acad. Emerg. Med."},{"key":"ref_23","first-page":"3542","article-title":"Impedance cardiography: Recent applications and developments","volume":"29","author":"Mansouri","year":"2018","journal-title":"Biomed. Res.-Tokyo"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1016\/j.ress.2008.10.008","article-title":"Polynomial chaos expansion for sensitivity analysis","volume":"94","author":"Crestaux","year":"2009","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"5397814","DOI":"10.1155\/2019\/5397814","article-title":"A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices","volume":"2019","author":"Chakraborty","year":"2019","journal-title":"J. Healthc. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, R., Shi, H., Sun, J., Zhao, L., Seah, H., Quah, C., and Tandianus, B. (2019). Multi-Channel Convolutional Neural Network Based 3D Object Detection for Indoor Robot Environmental Perception. Sensors, 19.","DOI":"10.3390\/s19040893"},{"key":"ref_28","unstructured":"Mughal, Y.M., Krivoshei, A., and Annus, P. (2013). Separation of cardiac and respiratory components from the electrical bio-impedance signal using PCA and fast ICA. arXiv."},{"key":"ref_29","unstructured":"Hu, W., Sun, H., and Wang, X. (November, January 30). A study on methods for impedance cardiography. Proceedings of the 1997 Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1109\/10.464380","article-title":"Reduction of motion artifacts using a two-frequency impedance plethysmograph and adaptive filtering","volume":"42","author":"Rosell","year":"1995","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_31","unstructured":"Krivoshei, A., Min, M., Parve, T., and Ronk, A. (2006, January 20\u201321). An adaptive filtering system for separation of cardiac and respiratory components of bioimpedance signal. Proceedings of the 2006 IEEE International Workshop on Medical Measurements and Applications, Benevento, Italy."},{"key":"ref_32","unstructured":"Min, M., Parve, T., Kukk, V., and Kuhlberg, A. (2001, January 21\u201323). An implantable analyzer of bio-impedance dynamics: Mixed signal approach. Proceedings of the 2001 Instrumentation and Measurement Technology Conference, Budapest, Hungary."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"57","DOI":"10.5755\/j01.eie.24.5.21844","article-title":"Sparse reconstruction method for separating cardiac and respiratory components from electrical bioimpedance measurements","volume":"24","author":"Butsenko","year":"2018","journal-title":"Elektron. Elektrotech."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102541","DOI":"10.1016\/j.bspc.2021.102541","article-title":"Bayesian source separation of electrical bioimpedance signals","volume":"67","author":"Pichler","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_35","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_36","unstructured":"Bishop, C.M. (2016). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_37","unstructured":"Thakur, A. (2020). Approaching (Almost) Any Machine Learning Problem, Abhishek Thakur."},{"key":"ref_38","unstructured":"(2021, May 20). Keras: The Python Deep Learning API. Available online: https:\/\/keras.io\/."},{"key":"ref_39","unstructured":"(2021, May 20). TensorFlow. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_40","first-page":"2121","article-title":"Adaptive Subgradient Methods for Online Learning and Stochastic Optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_42","unstructured":"Sutskever, I., Martens, J., Dahl, G., and Hinton, G. (2013, January 16\u201321). On the importance of initialization and momentum in deep learning. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_43","unstructured":"Ruder, S. (2017). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_44","unstructured":"Besse, P., Castets-Renard, C., Garivier, A., and Loubes, J.M. (2018). Can everyday AI be ethical. Fairness of Machine Learning Algorithms. arXiv."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/12\/1661\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:44:38Z","timestamp":1760168678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/23\/12\/1661"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,10]]},"references-count":44,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["e23121661"],"URL":"https:\/\/doi.org\/10.3390\/e23121661","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,10]]}}}