Table 3.

Overview of existing work on seizure detection using—machine learning classifiers, features, performance score, performance metrics, datasets, and Authors

Classifier(s)	Feature(s)	Performance (%)	Performance metrics	Dataset	Authors
SVM	Vector	96	Sensitivity (Sen)	CHB-MIT	Shoeb and Guttag [41]
Random forest	Time and frequency	93.8	Senstivity	EPILEPSIAE	Donos et al. [44]
ANN	Line length	99.6	Classification accuracy (Class Acc)	BONN	Guo et al. [69]
Burst detection algo	Line length	84.27, 84,85.7	Acc, Sen, Specificity (Spec)	NICU, Belgium	Koolen et al. [70]
Normalization	Line length	52	ROC	CHB-MIT	Logesparan et al. [71]
ELM and BPNN	SE	95.6	Class Accuracy	BONN	Song and Lio [72]
SVM and ELM	AE and SE	95.58	Class Accuracy	BCI Lab, Colarodo	Zhang et al. [73]
SVM	DWT	94.8	Avg Accuracy	CHB-MIT	Ahmad et al. [74]
GMM	Spectral, hybrid, temporal	87.58	Avg Accuracy	CHB-MIT	Gill et al. [75]
Random forest	PCA, STF, Moving Max	97.12, 99.29, 0.77/h	Sen, Spec, FPR	CHB-MIT	Orellana and Cerqueira [76]
Random forest and k-NN	Spectral power	80.87, 47.45, 2.5/h, 56.23	Sen, Prec, FPR, F-meas	CHB-MIT	Birjandtalab et al. [77]
Boosting	Stockwell	94.26, 96.34	Sen, Spec	Freiburg	Yan et al. [78]
SVM, MLP, KNN, Naïve bayes	Energy	98.75	Class Acc	EPILEPSIAE	Amin et al. [79]
Random forest	Entropy and DWT	98.45	Class Acc	BONN	Mursalin et al. [80]
SVM	Time–Frequency	90.62, 99.32	Sen, Spec	CHB-MIT	Zabihi et al. [81]
Random forest	Time-domain	96.94	ROC curve	Kaggle	Truong et al. [82]
SVM, LDA, QDA, LC,PC, DT, KNN, UDC, PARZEN	Time–frequency	84, 85	Sen, Spec	CHB-MIT	Fergus et al. [83]
SVM	DWT	86.83	Confusion Matrix	CHB-MIT	Chen et al. [84]
SVM and neural network	DWT and CWT	99.1	Overall Acc	BONN	Satapathy et al. [85]
ELM	Time–frequency	97.73, 0.37/h	Sen, false alarm rate	Freiburg	Yuan et al. [86]
SVM	DWT	99.38	Class Acc	BONN	Subasi et al. [87]
LS-SVM	FFT and DWT	100	Class Acc	BONN	Al Ghayab et al. [88]
SVM and Naïve bayes	Entropy, RMS, variance, energy	96.55, 95.63, 95.7	Sen, Spec, Acc	CHB-MIT	Selvakumari et al. [89]
LS-SVM	8 types of Entropies	100, 99.4, 99.5	Sen, Spec, Acc	BONN	Chen S et al. [90]
ANN	Spectral power	86	F-meas	CHB-MIT	Birjandtalab et al. [91]
KNN and GHE	-	100	Class Acc	BONN	Lahmiri and shumel [92]
Random forest	DWT	99.74, 0.21/h	Sen, FPR	BONN and Freiburg	Tzimourta et al. [93]
Random forest	STFT, mean, energy and std dev	96.7	Class Acc	BONN	Wang et al. [94]
Random forest, SVM, KNN, and Adaboost	28 statistical and time–frequency features	97.6, 94.4, 96.1, 92.9, 98.8, 0.96	Sen, Spec, Acc, PPR, NPR, ROC	Bern-Barcelona	Raghu and Sriraam [95]
ANN,KNN,SVM, and Random forest	Mean, std dev, power, skewness, kurtosis, absolute mean	100	Overall Accuracy	Freiburg and CHB-MIT	Alickovic et al. [96]
SVM	Energy	99.5	Class Acc	BONN and Barcelona	Fasil and Rajesh [97]
SVM and Random forest	10-time and frequency	0.98	ROC(AUC)	EPILEPSIAE	Manzouri et al. [98]
LS-SVM	DCT, SVD, IMF, DCT-DWT,	91.36	Acc, Sen, Spec	Freiburg	Parvez and Paul [99]
SysFor and Forest CERN	9 statistical features	100	Class Acc	Epilepsy Centre UCSF	Siddiqui et al. [63]
Random forest	L1-penalized robust regression (L1PRR)	100	Class Acc	BONN	Hussein et al. [100]
SVM, NB, KNN, random forest, logistic model Trees (LMT)	15-features	97.40, 97.40,97.50	Acc, Sen, Spec	BONN	Mursalin et al. [101]
Random forest	IMF	98.4,98.6,96.4	Sen, Spec, Acc	BONN	Sharma et al. [102]
ANN	Time–frequency	100	Overall Acc	BONN	Tzallas et al. [103]
Decision forest–Random forest, Boosting	9 statistical features	96.67,74.36, 84.06	Pre, Rec, F-measure	CHB-MIT	Siddiqui et al. [104]