A Novel Machine Learning Model for the Detection of Epilepsy and Epileptic Seizures Using Electroencephalographic Signals Based on Chaos and Fractal Theories

Table 4

Selected features and proposed feature fusion for classification.

Experiments	U-Sub	Input feat	FF
1	ZO-N	HFD1-HFD4
	ZO-F	HFD2-HFD4
	ZO-NF	HFD1-HFD2-HFD4
2	ZO-S	HFD4	—
3	N-S	HFD2-HFD5
	F-S	HFD3-HFD5
	NF-S	HFD2-HFD3-HFD5
4	ZN-S, ON-S, ZON-S	-
	ZF-S, OF-S, ZOF-S	-
	ZNF-S, ONF-S, ZONF-S	-
5	Z-NS Z-FS, Z-NFS,	-
	O-NS, O-FE, O-NFS
	ZO-NS, ZO-F, ZO-NFS
	ZO-NFS
6	Z-N-S,O-N-S,ZO-N-S	-
	Z-F-S,O-F-S, ZO-F-S	-
	Z-NF-S,O-NF-S	-
	ZO-NF-S