Comput. Biol. Med. - Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability.

Tópicos

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Resumo

This paper proposes a congestive heart failure (CHF) recognition method that includes features calculated from the bispectrum of heart rate variability (HRV) diagrams and a genetic algorithm (GA) for feature selection. The roles of the bispectrum-related features and the GA feature selector are investigated. Features calculated from the subband regions of the HRV bispectrum are added into a feature set containing only regular time-domain and frequency-domain features. A support vector machine (SVM) is employed as the classifier. A feature selector based on genetic algorithm proceeds to select the most effective features for the classifier. The results confirm the effectiveness of including bispectrum-related features for promoting the discrimination power of the classifier. When compared with the other two methods in the literature, the proposed method (without GA) outperforms both of them with a high accuracy of 96.38%. More than 3.14% surpluses in accuracies are observed. The application of GA as a feature selector further elevates the recognition accuracy from 96.38% to 98.79%. When compared to the Isler and Kuntalp's impressive results recently published in the literature that also uses GA for feature selection, the proposed method (with GA) outperforms them with more than 2.4% surpass in the recognition accuracy. These results confirm the significance of recruiting bispectrum-related features in a CHF classification system. Moreover, the application of GA as feature selector can further improve the performance of the classifier.

Resumo Limpo

paper propos congest heart failur chf recognit method includ featur calcul bispectrum heart rate variabl hrv diagram genet algorithm ga featur select role bispectrumrel featur ga featur selector investig featur calcul subband region hrv bispectrum ad featur set contain regular timedomain frequencydomain featur support vector machin svm employ classifi featur selector base genet algorithm proceed select effect featur classifi result confirm effect includ bispectrumrel featur promot discrimin power classifi compar two method literatur propos method without ga outperform high accuraci surplus accuraci observ applic ga featur selector elev recognit accuraci compar isler kuntalp impress result recent publish literatur also use ga featur select propos method ga outperform surpass recognit accuraci result confirm signific recruit bispectrumrel featur chf classif system moreov applic ga featur selector can improv perform classifi

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