Recognition of Imbalanced Epileptic EEG Signals by a Graph-Based Extreme Learning Machine

Table 5

Results of all methods on the precision index.

Datasets	ELM	W1-ELM	W2-ELM	R1-ELM	R2-ELM	G-ELM
D1	0.9078(0.0058)	0.8247(0.0304)	0.8342(0.0301)	0.9619(0.0087)	0.8648(0.0097)	0.9126(0.0029)
D2	0.8801(0.0077)	0.7878(0.0313)	0.7901(0.0298)	0.9497(0.0077)	0.8914(0.0063)	0.9569(0.0091)
D3	0.8279(0.0125)	0.8009(0.0261)	0.7911(0.0212)	0.8208(0.0062)	0.8040(0.0053)	0.9053(0.0026)
D4	0.8054(0.0124)	0.8725(0.0375)	0.7729(0.0290)	0.8761(0.0079)	0.8267(0.0060)	0.9131(0.0085)
D5	0.5031(0.0107)	0.8438(0.0360)	0.8609(0.0434)	0.7845(0.0147)	0.5178(0.0078)	0.9007(0.0102)
D6	0.6825(0.0243)	0.8833(0.0388)	0.8502(0.0399)	0.8541(0.0112)	0.6244(0.0216)	0.8952(0.0125)
D7	0.7714(0.0131)	0.7905(0.0400)	0.8817(0.0349)	0.8568(0.0088)	0.7985(0.0067)	0.9266(0.0092)
D8	0.8431(0.0149)	0.8466(0.0359)	0.7732(0.0233)	0.8194(0.0120)	0.7860(0.0119)	0.8932(0.0083)
D9	0.8374(0.0176)	0.8414(0.0204)	0.8229(0.0367)	0.7701(0.0139)	0.6733(0.0087)	0.8650(0.0174)
Ave	0.7843	0.8324	0.8197	0.8548	0.7541	0.9076
Ave. std	0.0132	0.0329	0.0320	0.0101	0.0093	0.0090