This paper presents automatic ECG arrhythmia classification method using symbolic dynamics through hybrid classifier. The proposed method consists of four steps: pre-processing, data extraction, symbolic time series construction and classification. In the proposed method, initially ECG signals are pre-processed to remove noise. Further, QRS complex is extracted followed by R peak detection. From R peak value, symbolic time series representation is formed. Finally, the symbolic time series is classified using Fuzzy clustering Neural Network (FCNN). To evaluate the proposed method we conducted the experiments on MIT-BIH dataset and compared the results with Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN) classifiers. The experimental results reveal that the FCNN classifier outperforms other two classifiers.
In this paper, a simple and robust algorithm is proposed for iris segmentation. The proposed method consists of two steps. In first step, iris and pupil is segmented using Robust Spatial Kernel FCM (RSKFCM) algorithm. RSKFCM is based on traditional Fuzzy-c-Means (FCM) algorithm, which incorporates spatial information and uses kernel metric as distance measure. In second step, small eigenvalue transformation is applied to localize iris boundary. The transformation is based on statistical and geometrical properties of the small eigenvalue of the covariance matrix of a set of edge pixels. Extensive experimentations are carried out on standard benchmark iris dataset (viz. CASIA-IrisV4 and UBIRIS.v2). We compared our proposed method with existing iris segmentation methods. Our proposed method has the least time complexity of O(n(i+p)) . The result of the experiments emphasizes that the proposed algorithm outperforms the existing iris segmentation methods.
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