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Table 3 Overview of existing work on seizure detection using—machine learning classifiers, features, performance score, performance metrics, datasets, and Authors

From: A review of epileptic seizure detection using machine learning classifiers

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]