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. Author manuscript; available in PMC: 2019 Jun 1.
Published in final edited form as: J Neural Eng. 2019 Feb 21;16(3):036004. doi: 10.1088/1741-2552/ab0933

Figure 9. Sleep spindle detection performance with respect to different normalization factors.

Figure 9.

a, False event rate and true event rate for pre- and post-computed normalization on the MASS dataset (n=19). b, Sensitivity, specificity, FDR, F1-score, accuracy and AUROC for pre- and post-computed normalization on the MASS dataset (n=19). Pre-computed normalization is the average spindle standard deviation from the training set, whereas post-computed normalization is the average standard deviation from the testing set. c, Testing on the MASS subject #18 with various normalization factors in sleep EEG calibration.