Table 3. Classifier performance.

	Model		Sensitivity [95% CI]	Specificity [95% CI]	Best CVA [95% CI]	AUC	Features Used	Noise/Not Noise
Noise	M1	All Noise	0.910.88 0.93	0.820.78 0.86	0.870.84 0.89	0.93	147	475/880
	M2	Eyeballs	0.460.25 0.61	1.01.0 1.0	0.980.97 0.99	0.93	40	30/880
	M3	HeadMotion	0.390.21 0.57	0.990.99 1.0	0.970.97 0.98	0.99	16	28/880
	M4	Ventricles	0.430.29 0.62	0.990.99 1.0	0.970.96 0.98	0.93	5	37/880

Performance metrics (sensitivity, specificity, best cross validation accuracy (CVA), area under the curve (AUC)), number of features selected, and proportion of noise components in data for four successful models, including comprehensive noise (All Noise, M1) and three noise subtypes (M2)(M3)(M4), built with and tested with ten -fold cross validation on Data A (healthy control).