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

Table 6 The pairwise classification results

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%

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.

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