Independent component analysis algorithm and its application in biomedical engineering / 国际生物医学工程杂志

Independent component algorithm (ICA) is a method of higher-order statistics(HOS) with the study objects of multivariate random signals that are mutual independent. It aim is to transform multivariate random signal into the signal having components that are mutually independent in complete statistical sense. This article briefly introduce series of the ICA algorisms including second order blind identification, multiple unknown source extraction algorithm based on second-order statistics, as well as Informax, modified Informax, fast fixedpoint ICA and joint approximative diagonalization of eigenmatrix (JADE) algorithm that are based on HOS. At the end of the article, the performance of each algorithm is compared and its application prospect is forecasted.

Hierarchical Classification of ECG Beat Using Higher Order Statistics and Hermite Model / 대한의료정보학회지

OBJECTIVE: The heartbeat classification of the electrocardiogram is important in cardiac disease diagnosis. For detecting QRS complex, conventional detection algorithmhave been designed to detect P, QRS, Twave, first. However, the detection of the P and T wave is difficult because their amplitudes are relatively low, and occasionally they are included in noise. Furthermore the conventionalmulticlass classificationmethodmay have skewed results to themajority class, because of unbalanced data distribution. METHODS: The Hermite model of the higher order statistics is good characterization methods for recognizing morphological QRS complex. We applied three morphological feature extraction methods for detecting QRS complex: higher-order statistics, Hermite basis functions andHermitemodel of the higher order statistics.Hierarchical scheme tackle the unbalanced data distribution problem. We also employed a hierarchical classification method using support vector machines. RESULTS:We compared classification methods with feature extraction methods. As a result, our mean values of sensitivity for hierarchical classification method (75.47%, 76.16% and 81.21%) give better performance than the conventionalmulticlass classificationmethod (46.16%). In addition, theHermitemodel of the higher order statistics gave the best results compared to the higher order statistics and the Hermite basis functions in the hierarchical classification method. CONCLUSION: This research suggests that the Hermite model of the higher order statistics is feasible for heartbeat feature extraction. The hierarchical classification is also feasible for heartbeat classification tasks that have the unbalanced data distribution.