Table 5 Applications of machine learning methods
From: The applied principles of EEG analysis methods in neuroscience and clinical neurology
References | Data type | Subjects | Method | Disease/state | Application | Effect evaluation |
|---|---|---|---|---|---|---|
Qaraqe et al. [162] | EEG | CHB-MIT dataset | CSP | Epilepsy | Utilized the CSP approach for seizure detection | A sensitivity of 100%, a detection latency of 7.28Â s, and a false alarm rate of 1.2 per hour were successfully attained in this article |
Dissanayake et al. [163] | EEG | CHB-MIT dataset | CSP | Epilepsy | Adopted the CSP algorithm for patient-independent seizure prediction | Accuracy achievements of 88.81% and 91.54% were reported in this article |
Liu et al. [164] | EEG | BCI competition III-4a BCI competition IV-2a strokes (n = 5) | CSP | Stroke | Investigated the rehabilitation of stroke patients using the CSP algorithm | High accuracies were achieved in comparison with seven state-of-the-art approaches, as highlighted in this article |
Alturki et al. [165] | EEG | Normal (males = 10) ASD (males = 6, females = 3) CHB-MIT dataset | CSP | Epilepsy and ASD | Applied the CSP algorithm for the diagnosis of epilepsy and autism | Accuracy rates of approximately 98.46% for diagnosing ASD and 98.62% for epilepsy were achieved in this article |
Jamal et al. [166] | EEG | ASD (n = 12) Normal (n = 12) | LDA | ASD | Carried out LDA to classify ASD | Leave-one-out cross-validation of the classification algorithm resulted in a best performance of 94.7% accuracy, with corresponding sensitivity and specificity values of 85.7% and 100%, as reported in this article |
Jeong et al. [167] | EEG | PDD (n = 26) AD (n = 26) Normal (n = 26) | LDA | PDD and AD | Applied LDA to distinguish between PD-related dementia and AD | A maximum performance of 80.19% accuracy was achieved using LDA with WC in this article |
Boostani et al. [168] | EEG | SZ (males = 13) Normal (males = 18) | LDA | SZ | Adopted LDA in the diagnosis of SZ | Accuracies of 87.51%, 85.36%, and 85.41% were achieved for BDLDA, LDA, and Adaboost, respectively, in this article |
Rajaguru et al. [169] | EEG | Epilepsy (n = 20) | LDA | Epilepsy | Used the LDA approach for the classification of epilepsy | When the dB2 and dB4 wavelets were classified with LDA, average classification accuracies of 95.83% and 95.03% were obtained, as claimed in this article |
Kang et al. [170] | EEG | ASD (boys = 39, girls = 10) TD (boys = 36, girls = 12) | SVM | ASD | Employed the SVM method to identify children with ASD | Combining two types of data resulted in a maximum accuracy of 85.44%, with AUC = 0.93 when 32 features were selected in this article |
Fu et al. [171] | EEG | Bonn dataset | SVM | Epilepsy | Adopted the SVM approach for the classification of epilepsy | A 99.125% accuracy of the algorithm with the theta rhythm of EEG signals was achieved in this article |
Shen et al. [172] | EEG | Normal (n = 10) | SVM | Mental fatigue measurement | Utilized the SVM method for mental fatigue measurement | An accuracy of 87.2% for the probabilistic multi-class SVM compared to 85.4% using the standard multi-class SVM was reported. With confidence estimates aggregation, the accuracy increased to 91.2% |
Liu et al. [173] | iEEG | Epilepsy (n = 21) | SVM | Epilepsy | Performed seizure detection in long-term EEG using the SVM method | A sensitivity of 94.46%, specificity of 95.26%, and a false detection rate of 0.58/h for seizure detection in long-term iEEG were achieved in this article |
Zhou et al. [174] | EEG | CHB-MIT dataset | CNN | Epilepsy | Detected seizures through CNN models | The article achieved a convincing performance with an accuracy of 94.67% on the test data |
Hassan et al. [175] | EEG | Bonn dataset | CNN | Epilepsy | Detected epilepsy through the 1D-CNN approach | Using frequency domain signals, average accuracies of 96.7%, 95.4%, and 92.3% for the three experiments were achieved in the Freiburg database, while average accuracies for detection in the CHB-MIT database were 95.6%, 97.5%, and 93% for the three experiments in this article |
Hassan et al. [176] | EEG | SZ (n = 14) Normal (n = 14) | CNN | SZ | Detected SZ through the 1D-CNN approach | The article effectively predicted two, three, four, and five classes with accuracies of 100%, 99%, 94.6%, and 94%, respectively, for the Bonn dataset and 98% for the CHB-MIT dataset |
Dong et al. [177] | EEG | ASD (children = 86) Normal (children = 89) | CNN | ASD | Applied the CNN method for the assessment of ASD in children | Accuracies of 90% and 98% were achieved for subject-based and non-subject-based testing, respectively, in this article |
Aliyu et al. [178] | EEG | Bonn dataset | CNN | Epilepsy | Detected epileptic EEG signals using CNN | The method was claimed to outperform its counterparts, achieving individual/sample accuracy of 92.63%/83.23%, as reported in this article |
Lee et al. [179] | EEG | PD (n = 20) Normal (n = 20) | RNN | PD | Combined CNN with RNN for the identification of PD | An accuracy of 99.2%, precision of 98.9%, and recall of 99.4% in differentiating PD from healthy controls were achieved in this article |
Sarkar et al. [180] | EEG | Normal (male = 1, female = 1) | RNN | Mental depression | Detected mental depression through RNN | The article achieved the highest accuracies of 97.50% in the training set and 96.50% in the test set |
Mishra et al. [181] | EEG | Sleep-EDF dataset | RNN | Sleep stages | Employed CNN and RNN for sleep stage classification | Efficient classification performance in sleep stage N1, as well as improvement in subsequent stages of sleep, was reported in this article |
Michielli et al. [182] | EEG | Normal (n = 10) | LSTM | Sleep stages | Used LSTM for the classification of different sleep stages | The overall percentage of correct classifications for five sleep stages was found to be 86.7% in this article |
Hu et al. [183] | EEG | CHB-MIT dataset | LSTM | Epilepsy | Established LSTM models to achieve the automatic detection of epilepsy | A mean sensitivity of 93.61% and a mean specificity of 91.85% were achieved on a long-term scalp EEG database in this article |
Koya et al. [184] | EEG | Normal (n = 10) | LSTM | Emotion | Adopted LSTM to recognize and classify different emotions | In this article, the LSTM + CNN model outperformed traditional or deep learning models, achieving an accuracy of 64% |
Lee et al. [185] | EEG | Normal (n = 10) | LSTM | Sleep stages | Detected drowsiness indicators using the LSTM method | The LSTM-CNN model in this article demonstrated an average accuracy of 85.6% and a kappa index of 0.77 for the three-class classification problem |