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Table 6 The pairwise classification results

From: Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock

Groups   Feature set Accuracy Sensitivity Specificity
SIRS vs Sepsis sign_m WE(s2) 40% 80% 20%
  sign_m WE(s2), WEn(s6) 80% 80% 80%
  sign_mdetr CWTentro4 80% 80% 80%
  sign_mdetr CWTentro4, WE(s5), CWTene 93.33% 100% 90%
SIRS vs S. Shock sign_m WEn(s5) 83.33% 80% 85.71%
  sign_m WEn(s5), WEn(s6), WEn(s8), WE(s8) 91.67% 80% 100%
  sign_mdetr WEn(s6) 83.33% 80% 85.71%
  sign_mdetr WEn(s6), CWTentro4, WE(s8) 100% 100% 100%
  1. The table demonstrates the randset feature sets and the classification performance achieved with a linear classifier and leave-one-out cross-validation, in terms of accuracy, sensitivity, and specificity. Here, sensitivity refers to SIRS, and specificity refers to sepsis or septic shock, respectively. The results are presented for sign_m and sign_mdetr, separately. Both univariate models for the best feature selected, and multivariate models, are depicted. WE, wavelet energy; Wen, wavelet entropy; CWT, continuous wavelet transformation; s, scale; entro4, entropy per scale 4.