. Author manuscript; available in PMC: 2016 May 25.

Published in final edited form as: Clin Neurophysiol. 2016 Feb 9;127(4):2038–2046. doi: 10.1016/j.clinph.2016.02.001

Table 4.

Performance of the automatic burst suppression detection method.

Performance measures					Reviewers and EEG data
SE (%) (CI_95%)	SP (%) (CI_95%)	PPV (%) (CI_95%)	NPV (%) (CI_95%)	κ (%) (CI_95%)	Rev. (n)	Rev. IDs	Dataset
89 (86–91)	84 (83–85)	51 (48–54)	97 (97–98)	56 (53–59)	1	JH	VIEN
88 (85–91)	81 (80–83)	42 (39–45)	98 (97–98)	47 (44–51)	1	JK	VIEN
92 (89–95)	85 (84–87)	46 (43–49)	99 (98–99)	54 (50–58)	2	JH + JK	VIEN
88 (85–91)	68 (61–75)	90 (88–93)	63 (55–70)	62 (55–68)	2	BW + MS	MGH
90 (88–92)	84 (83–86)	64 (61–66)	96 (96–97)	65 (63–68)	2	JH + JK, BW + MS	VIEN + MGH

Detection performance and agreement between the detection algorithm and the EEG reviewers is shown. Sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) are calculated based on annotations defined by one or two reviewers. The Cohen’s κ value measures the level of agreement between the reviewer and the result of the detection algorithm. The number of reviewers (Rev.) of each EEG sample, their IDs and the annotated datasets are shown.