ECG based prediction of Atrial Fibrillation using Support Vector Classifier

Autor: Siniša Sovilj, Gordana Rajsman, Ratko Magjarević
Jazyk: angličtina
Rok vydání: 2011
Předmět:
Zdroj: Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije
Volume 52
Issue 1
ISSN: 1848-3380
0005-1144
Popis: In patients undergoing Coronary Artery Bypass G rafting (CABG) surgery postoperative atrial fibrillation (AF) occurs with prevalence of up to 40%. The highest incidence is between the second and third day after the operation. Following cardiac surgery AF causes various complications, hemodynamic instability, and can cause heart attack, cerebral and other thromboemolisms. AF increases morbidity, duration and expense of medical treatment. This study aims to identify patients at high risk of postoperative AF. An early prediction of AF would provide a timely prophylactic treatment and would reduce incidence of arrhythmia. Patients at low risk of postoperative AF could be excluded from the side effects of anti-arrhythmic drugs. The investigation included 50 patients in whom lead II electrocardiograms were continuously recorded for 48 hours following CABG. Univariate statistical analysis was used in the search of signal features that might predict AF. The most promising identified features were: P wave duration, RR interval duration and PQ segment level. On the basis of these a nonlinear multivariate prediction model was made deploying a Support Vector Machine (SVM) classifier. The prediction accuracy was found uprising over the time. At 48 hours following CABG; the measured best average sensitivity was 95 . 9% and specificity 93. 4% . The positive and negative predictive accuracy were 88. 9% and 98. 8% , respectively and the overall accuracy was 94. 6% . In regard to the prediction accuracy, the risk assessment and prediction of postoperative A F are optimal to be done in the period between 24 and 48 hours following CABG.
Postoperacijska fibrilacija atrija (AF) pojavljuje se u oko 40% pacijenata podvrgnutih operaciji aortokoronarnog premoštenja (CABG), s najvećom učestalosti pojavljivanja oko trećeg dana nakon operacije. Postoperacijska AF može stvoriti brojne komplikacije poput hemodinamske nestabilnosti, srčanog udara, cerebralnih i drugih tromboembolija; povećava morbiditet, trajanje i troškove liječenja. S tudija ima za cilj rano otkrivanje pacijenta sa visokim rizikom razvoja postoperacijske AF, što bi osiguralo pravovremenu profilaktičku terapiju i smanjilo učestalost aritmije, dok bi pacijenti sa niskim rizikom razvoja postoperacijske AF bili pošteđeni nuspojava antiaritmičkih lijekova. Podatkovni skup uključuje 50 pacijenata, snimanih II standardnim odvodom elektrokardiografa, kontinuirano u razdoblju od 48 sati nakon operacije. Univarijatna statistička analiza korištena je za određivanje parametara signala koji bi mogli predvidjeti AF, te su kao najznačajniji određeni: trajanje P vala, trajanje RR intervala i razina PQ spojnice; na temelju kojih je izveden nelinearni multivarijatni predikcijski model zasnovan na SVM klasifikatoru. Ukupna predikcijska točnost modela povećava se s vremenom. U 48 . satu nakon operacije najbolje prosječne značajke iznosile su: osjetljivost 95 , 9%, specifičnost 93, 4% , pozitivna prediktivnost 88, 9% , negativna prediktivnost 98 , 8% te ukupna točnost 94, 6% . Prema rezultatima predikcijske točnosti, procjenu rizika i predikciju postoperacijske AF optimalno bilo bi načiniti u periodu između 24-tog i 48-og sata nakon operacije ugradnje aortokoronarnih premosnica.
Databáze: OpenAIRE
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