title: Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks.
abstract: This paper presents a decision support system that aims to estimate a patient׳s general condition and detect situations which pose an immediate danger to the patient׳s health or life. The use of this system might be especially important in places such as accident and emergency departments or admission wards, where a small medical team has to take care of many patients in various general conditions. Particular stress is laid on cardiovascular and pulmonary conditions, including those leading to sudden cardiac arrest. The proposed system is a stand-alone microprocessor-based device that works in conjunction with a standard vital signs monitor, which provides input signals such as temperature, blood pressure, pulseoxymetry, ECG, and ICG. The signals are preprocessed and analysed by a set of artificial intelligence algorithms, the core of which is based on Bayesian networks. The paper focuses on the construction and evaluation of the Bayesian network, both its structure and numerical specification.
authors.affiliation: Łódź University of Technology, Department of Microelectronics and Computer Science, ul. Wólczańska 221/223, 90-924 Łódź, Poland. Electronic address: wojciech.tylman@p.lodz.pl.
authors.forename: Wojciech
authors.initials: W
authors.lastname: Tylman
authors.affiliation: Norbert Barlicki University Clinical Hospital No. 1 in Łódź, ul. Kopcińskiego 22, 90-153 Łódź, Poland; NHS Orkney, Balfour Hospital, New Scapa Road, Kirkwall, Orkney KW15 1BH, United Kingdom.
authors.forename: Tomasz
authors.initials: T
authors.lastname: Waszyrowski
authors.affiliation: Łódź University of Technology, Department of Microelectronics and Computer Science, ul. Wólczańska 221/223, 90-924 Łódź, Poland.
authors.forename: Andrzej
authors.initials: A
authors.lastname: Napieralski
authors.affiliation: Łódź University of Technology, Department of Microelectronics and Computer Science, ul. Wólczańska 221/223, 90-924 Łódź, Poland.
authors.forename: Marek
authors.initials: M
authors.lastname: Kamiński
authors.affiliation: Norbert Barlicki University Clinical Hospital No. 1 in Łódź, ul. Kopcińskiego 22, 90-153 Łódź, Poland.
authors.forename: Tamara
authors.initials: T
authors.lastname: Trafidło
authors.affiliation: Łódź University of Technology, Department of Microelectronics and Computer Science, ul. Wólczańska 221/223, 90-924 Łódź, Poland.
authors.forename: Zbigniew
authors.initials: Z
authors.lastname: Kulesza
authors.affiliation: Łódź University of Technology, Department of Microelectronics and Computer Science, ul. Wólczańska 221/223, 90-924 Łódź, Poland.
authors.forename: Rafał
authors.initials: R
authors.lastname: Kotas
authors.affiliation: Łódź University of Technology, Department of Microelectronics and Computer Science, ul. Wólczańska 221/223, 90-924 Łódź, Poland.
authors.forename: Paweł
authors.initials: P
authors.lastname: Marciniak
authors.affiliation: Łódź University of Technology, Department of Microelectronics and Computer Science, ul. Wólczańska 221/223, 90-924 Łódź, Poland.
authors.forename: Radosław
authors.initials: R
authors.lastname: Tomala
authors.affiliation: Łódź University of Technology, Department of Microelectronics and Computer Science, ul. Wólczańska 221/223, 90-924 Łódź, Poland.
authors.forename: Maciej
authors.initials: M
authors.lastname: Wenerski
mesh_terms: D000208:Acute Disease; D001499:Bayes Theorem; D002318:Cardiovascular Diseases / Q000175:diagnosis / Q000503:physiopathology; D003661:Decision Support Techniques; D004562:Electrocardiography / Q000295:instrumentation / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008955:Models, Cardiovascular; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*
keywords: Bayesian networks; Cardiology; Decision support; Embedded systems; Sudden cardiac arrest