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

Table 5 Clustering measures

	Cluster points		Intraclass distance		Interclass distance
	n1	n2	c1	c2	d12	d21	Clustering cost	Sensitivity	Specificity	Accuracy
DWT high ultradian entropy (WEn5-6)	15	7	8.73	6.73	3.85	3.86	1.673	82.35%	80%	81.81%
CWT entropy neuro meta	5	17	6.15	6.79	4.25	4.24	1.749	60%	88.23%	81.81%
CWT entropies	8	14	10.61	4.11	3.61	2.79	1.777	100%	82.35%	86.36%
CWT entro ultradian	14	8	3.95	10.76	2.68	3.57	1.788	82.35%	100%	86.36%
SampEn and sumEn	17	5	8.43	6.34	3.28	3.83	1.905	76.47%	20%	63.63%
CWT entro all and neurogenic	5	17	6.78	8.73	4.21	4.09	1.990	60%	88.23%	81.81%
DWT low ultradian entropy (WEn7-8)	15	7	13.49	9.46	3.17	3.44	2.402	76.47%	60%	72.72%
DWT Neurogenic and metabolic entropy (WEn1-2-3)	6	16	9.63	11.71	5.83	6.36	2.420	60%	82.35%	77.27%
T Mean & Std	9	13	11.63	12.78	2.35	2.13	2.499	40%	58.82%	54.54%
CWT energy	4	18	14.48	11.92	8.17	6.35	4.353	0	76.47%	59.09%

The table shows, for sign_mdetr signal, clusters formed for different feature sets, number of data per cluster (n1 and n2), along with the intraclass euclidean distance for each cluster (c1 and c2), the mean interclass distance to center of the class (d12 and d21), the cost, calculated as the sum of intraclass and inverse interclass distances, and the correspondence of clustering with the a posteriori knowledge of groups (as accuracy, sensitivity, and specificity, with sensitivity referring to SIRS). The schemes are sorted in terms of cost. Schemes resulting in clustering with fewer than three members are omitted. DWT, discrete wavelet transformation; CWT, continuous wavelet transformation; SampEn, sample entropy; sumEn, multiscale entropy; SampEn, sample entropy; T, temperature; Std, standard deviation.

ISSN: 1364-8535