Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

Refining the rule base of fuzzy classifier to support the evaluation of fetal condition

The paper proposes a method to simplify a rule base of zero order Takagi–Sugeno–Kang fuzzy classifier, involving the determination of the ɛ -similar rules based on fuzzy clustering with ɛ -hyperballs. The rule simplification process is based on the concept of ɛ -insensitivity areas underlying the partitioning process of rule centers (centers of membership functions in the rule premises), which directly corresponds to the idea of rule ɛ -similarity. Clustering parameters leading to the best performance of the modified rule base, including the degree of rule ɛ -similarity, are determined by means of the evolution strategy. Since our main objective was to maintain the high performance of the resulting classifier, two rule-based simplification procedures, both called rule base refinement, are proposed. The work focuses mainly on the practical application to support the diagnosis of fetal condition based on the analysis of CardioTocoGraphic (CTG) signals. The publicly available collection of CTG recordings (CTU-UHB) was used in order to verify the quality of the introduced solutions. The classification performance was assessed with respect to the reference evaluation of fetal state determined on the basis of a retrospective analysis using the newborn outcome defined with different thresholds of the blood pH from the umbilical artery. The experiments confirmed the high generalization ability of the refined fuzzy classifier, in particular its high efficiency in supporting the qualitative assessment of fetal condition based on the analysis of parameters quantitatively describing fetal signals.Web of Science147art. no. 11079