Table 6 Comparison of the proposed method with state-of-the-art techniques on two benchmark datasets.

Dataset	Model	Acc (%)	Sen (%)	Spe (%)	AUC	F1
CHB-MIT	CNN-Self-Attention¹⁹	71.44	72.92	73.29	0.83	0.73
	DCN³⁶	79.24	73.65	85.75	0.88	0.77
	DeepCNN³⁷	77.14	71.25	82.55	0.85	0.74
	Resnet-LSTM¹⁸	83.74	75.32	92.16	0.92	0.81
	PANN¹⁷	71.63	78.39	64.83	0.83	0.72
	StackedCNN¹⁵	79.20	74.36	84.01	0.87	0.76
	EEGwaveNet¹⁶	78.73	78.25	79.14	0.87	0.77
	Transformer¹⁴	81.22	76.24	86.22	0.89	0.78
	HybirdCNN⁴⁸	73.32	73.14	82.56	0.84	0.73
	QFF-MLNet⁴⁹	81.63	78.56	87.32	0.90	0.80
	PSD-LW-DCN	85.84\(\uparrow\)	79.57\(\uparrow\)	92.12	0.94\(\uparrow\)	0.84\(\uparrow\)
TUSZ	CNN-Self-Attention¹⁹	76.42	75.68	83.24	0.86	0.76
	DCN³⁶	80.12	77.09	84.69	0.89	0.78
	DeepCNN³⁷	79.14	75.25	82.13	0.83	0.72
	Resnet-LSTM¹⁸	81.32	76.41	81.86	0.83	0.73
	PANN¹⁷	74.29	75.89	82.73	0.82	0.71
	StackedCNN¹⁵	75.17	73.17	77.18	0.83	0.73
	EEGwaveNet¹⁶	70.22	74.89	65.57	0.79	0.70
	Transformer¹⁴	79.29	78.28	80.31	0.88	0.77
	HybirdCNN⁴⁸	73.41	73.35	75.67	0.81	0.71
	QFF-MLNet⁴⁹	80.28	77.31	85.46	0.89	0.78
	PSD-LW-DCN	83.21\(\uparrow\)	78.02	88.39\(\uparrow\)	0.91\(\uparrow\)	0.81\(\uparrow\)

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