J Med Syst - Singular value decomposition based feature extraction technique for physiological signal analysis.

Tópicos

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Resumo

Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.

Resumo Limpo

multiscal entropi mse one popular techniqu calcul describ complex physiolog signal mani studi use approach detect chang physiolog condit human bodi howev mse result easili affect nois trend lead incorrect estim mse valu paper singular valu decomposit svd adopt replac mse extract featur physiolog signal adopt support vector machin svm classifi differ physiolog state test data set base physionet websit use classif result show use svd extract featur physiolog signal attain classif accuraci rate higher use mse valu result show propos analysi procedur effect appropri distinguish differ physiolog state promis result use refer doctor diagnosi congest heart failur chf diseas

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