An Arrhythmia Classification Method in Utilizing the Weighted KNN and the Fitness Rule

Abstract

BackgroundArrhythmia is the disturbance in the heart's regular rhythmic activity. Although arrhythmia signals are generally reflected on electrocardiogram records, some arrhythmias do not continuously appear. In such cases, heart-rate variability signals may have to be obtained over long periods of time and should be inspected by experts. However, analyses by experts require a long time and may omit important information. Therefore, computer aided diagnosis systems are required to automatically detect diverse types of arrhythmias. In addition, although many efforts have been made to develop arrhythmia detection techniques, studies on algorithms that are reliable, robust, and have an excellent adaptability to diverse situations remain necessary. ObjectiveIn this paper, we integrate coefficients extracted through principal component analysis (PCA) and linear discriminant analysis (LDA) for reinforcing the original signal's representative features and use the weighted k-nearest neighbor (WKNN) applying the weighted value control for reducing the sensitivity depending on the K size of k-nearest neighbor (KNN), and reclassify the data that were not accurately classified using the fitness rule for improving the arrhythmia classification accuracy. MethodsIn the preprocessing phase, the amplitude is adjusted through normalization and baseline fluctuations, and diverse noises existing in signals are removed using wavelets. In the feature extraction and selection phase, using daubechies 2, the pre-processed electrocardiogram signals are decomposed, and d4 and a4 feature coefficients are extracted. Thereafter, the dimensions of the d4 and a4 feature coefficients are reduced using PCA and LDA, respectively, and the resultant coefficients from the reduction are combined and composed as features. In the classification phase, the test data are classified into four classes using the KNN. The goodness of fit of the classified data is tested using the fitness rules, and any beats that are not classified correctly are reclassified by applying them to the WKNN algorithm. ResultsThe results show that sensitivity = 97.57%, specificity = 99.42%, and positive predictive value = 94.41% are exhibited, as well as slightly higher sensitivity, specificity, and positive predictive values than other results. ConclusionThe classification combining the features of PCA and LDA shows better results than classification using the features of PCA and LDA each. Additionally, the classification using WKNN and fitness rules exhibits higher sensitivity, specificity, and positive predictive values than classification without using WKNN and fitness rules thereby classifying arrhythmia more efficiently.