title: Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients.
abstract: BACKGROUND
  Patients surviving myocardial infarction (MI) can be divided into high and low arrhythmic risk groups. Distinguishing between these two groups is of crucial importance since the high-risk group has been shown to benefit from implantable cardioverter defibrillator insertion; a costly surgical procedure with potential complications and no proven advantages for the low-risk group. Currently, markers such as left ventricular ejection fraction and myocardial scar size are used to evaluate arrhythmic risk.
  
  
  METHODS
  In this paper, we propose quantitative discriminative features extracted from late gadolinium enhanced cardiac magnetic resonance images of post-MI patients, to distinguish between 20 high-risk and 34 low-risk patients. These features include size, location, and textural information concerning the scarred myocardium. To evaluate the discriminative power of the proposed features, we used several built-in classification schemes from matrix laboratory (MATLAB) and Waikato environment for knowledge analysis (WEKA) software, including k-nearest neighbor (k-NN), support vector machine (SVM), decision tree, and random forest.
  
  
  RESULTS
  In Experiment 1, the leave-one-out cross-validation scheme is implemented in MATLAB to classify high- and low-risk groups with a classification accuracy of 94.44%, and an AUC of 0.965 for a feature combination that captures size, location and heterogeneity of the scar. In Experiment 2 with the help of WEKA, nested cross-validation is performed with k-NN, SVM, adjusting decision tree and random forest classifiers to differentiate high-risk and low-risk patients. SVM classifier provided average accuracy of 92.6%, and AUC of 0.921 for a feature combination capturing location and heterogeneity of the scar. Experiment 1 and Experiment 2 show that textural features from the scar are important for classification and that localization features provide an additional benefit.
  
  
  CONCLUSIONS
  These promising results suggest that the discriminative features introduced in this paper can be used by medical professionals, or in automatic decision support systems, along with the recognized risk markers, to improve arrhythmic risk stratification in post-MI patients.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, Kjell Arholms Gate 41, Stavanger 4036, Norway. Electronic address: lasya.kotu@gmail.com.
authors.forename: Lasya Priya
authors.initials: LP
authors.lastname: Kotu
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, Kjell Arholms Gate 41, Stavanger 4036, Norway. Electronic address: kjersti.engan@uis.no.
authors.forename: Kjersti
authors.initials: K
authors.lastname: Engan
authors.affiliation: Department of Electrical Engineering and Computer Science, Northwestern University, 633 Clark St, Evanston, IL 60208, USA.
authors.forename: Reza
authors.initials: R
authors.lastname: Borhani
authors.affiliation: Department of Electrical Engineering and Computer Science, Northwestern University, 633 Clark St, Evanston, IL 60208, USA.
authors.forename: Aggelos K
authors.initials: AK
authors.lastname: Katsaggelos
authors.affiliation: Department of Cardiology, Stavanger University Hospital, Gerd Ragna Bloch Thorsens Gate 8, Stavanger 4011, Norway.
authors.forename: Stein
authors.initials: S
authors.lastname: Ørn
authors.affiliation: Department of Cardiology, Stavanger University Hospital, Gerd Ragna Bloch Thorsens Gate 8, Stavanger 4011, Norway.
authors.forename: Leik
authors.initials: L
authors.lastname: Woie
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Stavanger, Kjell Arholms Gate 41, Stavanger 4036, Norway.
authors.forename: Trygve
authors.initials: T
authors.lastname: Eftestøl
mesh_terms: D001145:Arrhythmias, Cardiac / Q000209:etiology*; D016009:Chi-Square Distribution; D002921:Cicatrix / Q000150:complications / Q000473:pathology* / Q000503:physiopathology; D003287:Contrast Media; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D019028:Magnetic Resonance Imaging, Cine; D009203:Myocardial Infarction / Q000150:complications / Q000473:pathology* / Q000503:physiopathology; D009206:Myocardium / Q000473:pathology*; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012984:Software; D013318:Stroke Volume; D060388:Support Vector Machine; D016277:Ventricular Function, Left
chemical_list: D003287:Contrast Media
keywords: Cardiac magnetic resonance image; High and low arrhythmic risk; Local binary pattern; Sobel filter; Support vector machine classifier; k-Nearest neighbor classifier