|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|382337||660757||2016||14 صفحه PDF||سفارش دهید||دانلود رایگان|
• This paper shows a system for feature optimization using modified Taguchi method.
• This method can reduce the number of features, and classification hazards.
• This study enhances the rules of Taguchi method for the system has no exact output.
• This study classifies multiple clusters with less number of parameters.
• This method possesses minimal pre-processing with anchor point feature selection.
Development of an expert system for clinical application includes automation in diagnosis of abnormality and patient monitoring based on features derived from continuous data set. This paper presents a novel method for feature optimization and classification of electrocardiogram (ECG) for arrhythmia analysis. A feature set optimization technique can reduce the classification hazard by selecting few comprehensive features to cater all kind of abnormalities under consideration. Proposed work deals with ranking and selection of an optimized pair of features using Taguchi method from eleven possible features normally used for characterizing arrhythmic beats like left bundle branch (LBBB), right bundle branch (RBBB) and premature ventricular contraction (PVC) are compared to normal beats. An imposed target based modification of Taguchi method is also suggested for the systems where the output is not pre-defined as in the case of biomedical applications. The proposed method is advantageous for the expert systems in which individual identity of the features are to be stored while reducing the dimensionality of the feature set. Multiclass Navis Bayes classifier is used to classify the beats in a single run and good performance parameters are obtained as reported in the result section.
Journal: Expert Systems with Applications - Volume 56, 1 September 2016, Pages 268–281