Explainable electroencephalography-based attention-deficit/hyperactivity disorder detection model with a combination of ternary pattern and twin wavelet transform

Table 5 Comparison of results, %

Ref.	Method(s)	Classifier	Subjects	Samples	Channels	Split ratio	Results
Nouri et al[50], 2023	Layer-wise relevance propagation, CNN	Softmax	31 ADHD; 30 controls	656	19	5-fold CV	Acc: 92.45; Sen: 93.06; Spe: 98.10
Chen et al[24], 2019	CNN	Softmax	50 ADHD; 51 controls	4545	32	90:10	Acc: 94.67
Sharma et al[51], 2023	MEMD, GA, MEWT, multivariate empirical-basis decomposition approaches, NCA	ANN	61 ADHD; 60 controls	7983	19	5-fold CV	Acc: 96.16; F1: 96.32; MCC: 0.92
Cura et al[52], 2023	Intrinsic time-scale decomposition	Bagged tree	15 ADHD; 18 controls	198	12	10-fold CV	Acc: 99.46; Sen: 99.47; Spe: 99.47
Barua et al[53], 2022	Ternary motif pattern, TQWT, NCA	kNN	61 ADHD 60 Controls	4173	14	10-fold CV	Acc: 95.57; GM:95.18
Tor et al[54], 2021	EMD, DWT	kNN	45 ADHD; 62 ADHD + CD; CD 16	5000	12	10-fold CV	Acc: 97.88; Sen: 96.68; Spe: 100
Tosun[55], 2021	LSTM, PSD	SVM	8 ADHD; 8 controls	4352	16	80:20	Acc:92.15; Sen: 90.95; Spe: 93.43
Moghaddari et al[56], 2020	Deep CNN	Softmax	31 ADHD; 30 controls	328	19	5-fold CV	Acc: 98.48; Rec:98.48; F1:98.49; Pre: 98.51
Kaur et al[57], 2020	Phase space reconstruction	SVM	47 ADHD; 50 controls	Not specified	19	69:31	Acc: 93.30; Sen: 100; Spe: 86.70
Tanko et al[58], 2022	8-pointed star pattern learning network	kNN	61 ADHD; 60 controls	Not specified	19	10-fold CV	Acc: 97.19; Rec: 97.12; Pre: 97.18; F1: 97.15
García-Ponsoda et al[59], 2024	Independent component analysis process	XGBoost	61 ADHD; 60 controls	128 samples per second	19	5-fold CV	Acc: 86.10
Mercado-Aguirre et al[60], 2025	Ridge Regression, Independent Component Analysis	SVM	22 ADHD; 25 controls	800 ms (100 samples)	6	5-fold CV	Acc: 86.36; Sen: 95.45
Colonnese et al[61], 2025	Hyperdimensional Computing with a spatio-temporal encoder	ADHDC (HDC)	37 ADHD; 42 controls	7168	14	75:25	Acc: 88.90; F1: 87.50; Rec: 90.40
Cai et al[62], 2025	Phase space reconstruction	kNN	61 ADHD; 60 controls	Not specified	19	80:20	Acc: 78.27; Sen: 80.62; Spe: 75.63
Alhussen et al[63], 2025	Discrete Cosine Transform- Independent Component Analysis, rhinofish optimization, AttentionNet	Softmax	(1) 17 ADHD; 17 controls; (2) 51 ADHD; 52 controls	Not specified	25	5-fold CV	(1) Acc: 97.89; (2) Acc: 98.52
Our study	Twin wavelet transformation, combination ternary pattern, NCA, iterative majority voting	kNN	137 ADHD; 150 controls	7399	20	10-fold CV	Acc: 99.97; Sen: 99.96; Spe: 99.98

CNN: Convolutional neural network; ADHD: Attention-deficit/hyperactivity disorder; CV: Cross-validation; MEMD: Multivariate empirical mode decomposition; GA: Genetic algorithm; MEWT: Multivariate empirical wavelet transform; NCA: Neighborhood component analysis; MCC: Matthews correlation coefficient; TQWT: Tunable Q-factor wavelet transform; EMD: Empirical mode decomposition; DWT: Discrete wavelet transform; CD: Conduct disorder; LSTM: Long short-term memory; PSD: Power spectral density; SVM: Support vector machine; HDC: Hyperdimensional computing; Acc: Accuracy; Sen: Sensitivity; Spe: Specificity.