Nerve detection with optical spectroscopy for regional anesthesia procedures

Table 1 Classification results according to SVM, PLS-DA and CART for discrimination of fascicular tissue of the nerve from the surrounding tissues

Classification method	Feature selection	MCC	ACC	SENS (%)	SPEC (%)	PPV	NPV	TP	FN	FP	TN
SVM	Fit	0.711	0.854	82.6	88.8	0.901	0.806	580	122	64	508
SVM	PCA	0.793	0.897	89.9	89.5	0.913	0.878	631	71	60	512
SVM	Segments	0.779	0.890	88.6	89.5	0.912	0.865	622	80	60	512
SVM	Combined	0.826	0.914	91.3	91.4	0.929	0.896	641	61	49	523
PLSDA	10PC’s	0.814	0.907	92.5	89.5	0.864	0.943	494	40	78	662
CART	Fit	0.615	0.808	81.2	80.4	0.836	0.777	570	132	112	460

For SVM different feature selection methods are used: fit parameters, PCA, segments and a combination of the last three. For PLSDA, 10 principal components have been used (10PC’s). For the CART analysis, the fit parameters have been used as features
Matthews correlation coefficient (MCC see Eq. 2 text), accuracy (ACC = [TP + TN]/[TP + FN + FP + TN]), sensitivity (SENS = TP/[TP + FN]), specificity (SPEC = TN/[FP + TN]), positive predictive value (PPV = TP/[TP + FP]), negative predictive value (NPV = TN/[TN + FN]), true positive (TP), false negative (FN), false positive (FP), true negative (TN)

ISSN: 1479-5876