Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset
2.1.1. Bonn Dataset
2.1.2. Freiburg Dataset
2.2. Preprocessing
Tunable-Q Wavelet Transform
2.3. Feature Extraction
2.3.1. Statistical Features
2.3.2. Frequency Features
- (1)
- Intensity Weighted Mean Frequency (IWMF)
- (2)
- Intensity Weighted Bandwidth (IWBW)
2.3.3. Fractal Features
- (1)
- Higuchi Fractal
- (2)
- Katz Fractal
- (3)
- Petrosian Fractal
- (4)
- Detrended Fluctuation Analysis
2.3.4. Entropy Features
- (1)
- Shannon Feature
- (2)
- Log-Energy Entropy
- (3)
- Average Shannon Wavelet Entropy
- (4)
- Average Rényi Wavelet Entropy
- (5)
- Average Tsallis Wavelet Entropy
- (6)
- Permutation Rényi Entropy
- (7)
- Graph Entropy
- (8)
- Fuzzy Entropy
- (9)
- Refined Composite Multiscale Fuzzy Entropy (RCMFE)
- (10)
- Inherent Fuzzy Entropy
- 1.
- Calculating the extremes to cover and [102].
- 2.
- Calculating the average [102]:
- 3.
- Candidates of inherent functions are derived intrinsic mode functions (IMFs) [102]:
- 4.
- 5.
- Given t = t + 1, consider d(t + 1) as the input EEG data; while iterating on the residual m(t), which continues until the final residue r that becomes a monotonic function from which no more IMF can be extracted [102].
- 6.
- The total accumulated residual IMFs are used to reconstruct the signal [102]:
- (11)
- Averaged Fuzzy Entropy
- A translation of n samples, corresponds to .
- A reflection at the position n, corresponds to .
- An inversion at the position n, corresponds to .
- A glide reflection of n samples, corresponds to .
- (12)
- Fractional Fuzzy Entropy
- (13)
- Spectral Entropy
- (14)
- Sample Entropy
- (15)
- Permutation Entropy
2.4. Classification
2.4.1. SVM
2.4.2. KNN
2.4.3. CNN–RNN
3. Statistical Metrics
4. Results
5. Limitations of Study
6. Discussion, Conclusions, and Future Works
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sets | Subjects | ||||
---|---|---|---|---|---|
Patient Stage | Electrode Type | Num. of Cases | Num. of Data | Length of Segments | |
Set A | Eye Open | Surface | 5 | 100 | 4097 |
Set B | Eye Close | Surface | 5 | 100 | 4097 |
Set C | Seizure Free | Intracranial | 5 | 100 | 4097 |
Set D | Seizure Free | Intracranial | 5 | 100 | 4097 |
Set E | Seizure Activity | Intracranial | 5 | 100 | 4097 |
Subjects | Problem Classifications | Description |
---|---|---|
Subject 1 | A–E | Healthy—Ictal |
Subject 2 | B–E | Healthy—Ictal |
Subject 3 | C–E | Interictal—Ictal |
Subject 4 | D and E | Interictal—Ictal |
Subject 5 | ABCD and E | Normal—Seizure |
Subject 6 | AB and CD and E | Healthy—Interictal—Seizure |
Patient | Age | Gender | Seizure Origin | Seizure Type | Number of Seizures |
---|---|---|---|---|---|
1 | 15 | Female | Temporal | SP, CP | 4 |
2 | 38 | Male | Frontal | SP, CP, GTC | 3 |
3 | 14 | Male | Temporal | SP, CP | 5 |
4 | 26 | Female | Temporal | SP, CP, GTC | 5 |
5 | 16 | Female | Frontal | SP, CP, GTC | 5 |
6 | 31 | Female | Temporal | CP, GTC | 3 |
7 | 42 | Female | Temporal | SP, CP, GTC | 3 |
8 | 32 | Female | Temporal | SP, CP | 2 |
9 | 44 | Male | Frontal | CP, GTC | 5 |
10 | 47 | Male | Frontal | SP, CP, GTC | 5 |
11 | 10 | Female | Frontal | SP, CP, GTC | 4 |
12 | 42 | Female | Frontal | SP, CP, GTC | 4 |
13 | 22 | Female | Temporal | SP, CP, GTC | 2 |
14 | 41 | Female | Temporal | CP, GTC | 4 |
15 | 31 | Male | Frontal | SP, CP, GTC | 4 |
16 | 50 | Female | Temporal | SP, CP, GTC | 5 |
17 | 28 | Male | Temporal | SP, CP, GTC | 5 |
18 | 25 | Female | Temporal | SP, CP | 5 |
19 | 28 | Female | Frontal | SP, CP, GTC | 4 |
20 | 33 | Male | Temporal | SP, CP, GTC | 5 |
21 | 13 | Male | Temporal | SP, CP | 5 |
Formula | Feature Name | Equations |
---|---|---|
Mean | (3) | |
Variance | (4) | |
Kurtosis | (5) | |
Skewness | (6) | |
Standard Deviation | (7) | |
Max | (8) |
Parameters | Layer |
---|---|
Kernel size = 3, activation = ‘relu’, filters = 32 | Conv1d |
Kernel size = 3, activation = ‘relu’, filters = 32 | Conv1d_1 |
Pool_size = 2 | Maxpooling1d |
Kernel size = 3, activation = ‘relu’, filters = 32 | Conv1d_2 |
--- | Flatten |
Number of neurons = 64 | LSTM |
Number of neurons = 128, activation = ‘relu’ | Dense |
Number of neurons = 128, activation = ‘relu’ | Dense_1 |
Number of neurons = 2 or 3, activation = ‘softmax’ | Dense_2 |
Methods | Sets | Accuracy | Precision | Spec | Sens | F1-Score |
---|---|---|---|---|---|---|
Standard SVM | A–E | 97.50 | 97.31 | 97.29 | 97.36 | 97.66 |
B–E | 98.11 | 98.06 | 98.04 | 98.82 | 98.03 | |
C–E | 98.05 | 98.54 | 98.56 | 98.47 | 97.95 | |
D and E | 98.67 | 99.11 | 98.43 | 98.62 | 98.48 | |
ABCD and E | 98.17 | 99.03 | 98.18 | 97.26 | 98.26 | |
AB and CD and E | 98.03 | 98.71 | 98.72 | 98.17 | 98.01 | |
SVM-RBF | A–E | 98.38 | 98.61 | 98.94 | 98.99 | 98.53 |
B–E | 98.24 | 99.09 | 98.71 | 99.02 | 98.96 | |
C–E | 98.33 | 98.98 | 98.76 | 99.13 | 98.83 | |
D and E | 98.24 | 99.86 | 98.83 | 99.22 | 99.03 | |
ABCD and E | 98.14 | 99.17 | 98.31 | 98.72 | 98.97 | |
AB and CD and E | 98.17 | 99.03 | 99.03 | 98.66 | 98.69 | |
KNN (K = 3) | A–E | 96.62 | 96.32 | 96.50 | 94.75 | 94.51 |
B–E | 96.37 | 96.24 | 96.23 | 96.49 | 96.37 | |
C–E | 96.62 | 95.37 | 95.28 | 98.08 | 96.67 | |
D and E | 97.87 | 98.12 | 98.41 | 98.46 | 98.57 | |
ABCD and E | 96.90 | 94.62 | 96.87 | 95.19 | 94.34 | |
AB and CD and E | 96.31 | 95.18 | 97.30 | 97.44 | 96.11 | |
KNN (K = 5) | A–E | 95.12 | 95.75 | 92.34 | 92.25 | 94.92 |
B–E | 96.37 | 96.25 | 98.25 | 98.49 | 97.37 | |
C–E | 96.49 | 94.92 | 94.62 | 97.21 | 96.56 | |
D and E | 96.71 | 97.77 | 97.72 | 96.73 | 97.75 | |
ABCD and E | 95.90 | 93.21 | 96.38 | 93.50 | 92.34 | |
AB and CD and E | 94.42 | 94.38 | 96.15 | 95.33 | 96.97 | |
CNN–RNN | A–E | 99.61 | 99.78 | 99.81 | 99.43 | 99.69 |
B–E | 99.46 | 99.51 | 99.17 | 99.22 | 99.46 | |
C–E | 99.51 | 99.42 | 99.31 | 99.43 | 99.28 | |
D and E | 99.82 | 99.59 | 99.68 | 99.82 | 99.61 | |
ABCD and E | 99.78 | 98.71 | 98.91 | 98.83 | 98.81 | |
AB and CD and E | 99.71 | 99.68 | 99.79 | 99.61 | 99.73 |
Methods | Accuracy | Sensitivity | Specificity | Precision | F1-Score |
---|---|---|---|---|---|
SVM | 97.13 | 97.24 | 97.31 | 97.39 | 97.28 |
SVM–RBF | 97.41 | 97.86 | 97.73 | 97.43 | 97.59 |
3NN | 96.66 | 96.19 | 95.93 | 96.39 | 97.11 |
5NN | 96.71 | 96.02 | 96.93 | 96.03 | 96.97 |
CNN–RNN | 99.13 | 98.96 | 98.96 | 99.01 | 99.11 |
Work | Preprocessing | Feature Extraction | Feature Selection | Classifiers | Accuracy |
---|---|---|---|---|---|
[21] | TQWT | CCEnt | PCA | LS-SVM | 97.02% |
[22] | TQWT | Hybrid Features | Firefly | RF | 97% |
[23] | TQWT | AVP, STD | No | K-NN | 98.80% |
[24] | TQWT | Statistic Features | No | K-NN | 100% |
[25] | TQWT | KNN Entropy | Wrapper | SVM | 100% |
[26] | TQWT | CTM, 2D-RPS plots | N/A | NA | N/A |
[27] | TQWT | MvFE | No | LS-SVM | 84.67% |
[28] | EMD–TQWT | IP | Different Methods | LS-SVM | 99% |
[29] | TQWT | SC, SS, SF, SSl | No | bootstrap | 100% |
[30] | TQWT | Correntropies | N/A | RF | 92.78% |
[31] | TQWT | KnnEnt, CCorrEnt, FzEnt | No | LS-SVM | 95% |
[32] | TQWT | Centered correntropy | No | RF | 98.30% |
[33] | TQWTRF | FDs, AppEnt | No | SVMRF | 100% |
[34] | TQWT | Mixture Correntropy | Various Methods | LS-SVM | 90.10% |
[35] | IEVDHM–HT | Various Features | Student’s t-test | LS-SVM | 100% |
[36] | FAWT | CVDistEnt, logarithmic energy | N/A | FKNN | 100% |
[37] Multi-Classes = 99.46% | VMD, HT | BLIMFs | No | EMRVFLN | Two-Classes = 100% Multi-Classes = 99.46% |
[38] Multi-Classes = 96.50% | Filtering | LSP | NCA | SVM | Two-Classes = 99.10% |
[39] Multi-Classes = 99.70% | Filtering, DWT | Different Features | N/A | SVM | Two-Classes = 99.50% Multi-Classes = 99.70% |
[40] | DWT | Linear and Non-Linear Features | No | SVM | 99.50% |
[41] | DWT | Statistic Features, Entropy, RWE | WOA | SVM | 99.80% |
[42] | SSA | 1D-LBP | No | SVM | N/A |
[43] | DWT | Entropy Features | ANOVA-FSFS | SVM | 99.50% |
[44] Multi-Classes = 99.07% | WPT | FDE | Kruskal Wallis | KNN | Two-Classes = 99.69% Multi-Classes = 99.07% |
[45] | MODWPT | Statistic Parameters | Different Methods | LS-SVM | 99.60% |
[46] | FSST | GLCM | N/A | KNN | 99.59% |
[47] | ECT | Graph Theory, FD | No | RF | 98.50% |
[48] | MRBF–MPSO | PSD | PCA | SVM | 98.73% |
[49] | Z-Score Normalization | 1D-CNN | No | Softmax | 86.67% |
[50] | DWT | PSR | SVCM | LS-SVM | 98.55% |
[51] | EMD | Spectral and Temporal Features | No | SVM | N/A |
[52] | ATFFWT | FD | Different Methods | LS-SVM | Two-Classes = 100% |
[53] Multi-Classes = 100% | TWD | Statistical Features | No | KNN | Multi-Classes = 100% 99.33% |
[54] | DWT | Statistical Features | N/A | SVM | Two-Classes = 97.97% |
[55] Multi-Classes = 98% | IMFs | AmE | DESA | RF | Multi-Classes = 98% Two-Classes = 99.41% |
[56] | DoG | LBP and Histogram Features | No | SVM | Multi-Classes = 98.80% 99.12% |
[57] | GST | SVD Feature | No | RF | 97.78% |
[58] | DCT | HE and ARMA Model | No | LSTM | 96% |
[59] | DWT | Feature Extraction | No | N/A | 99.26% |
[60] | -- | ApEn and RQA | No | N/A | 95% |
[61] | WT | Approximate Entropy, LLE, Correlation Dimension | FRBS | N/A | 99% |
[62] | Clustering, Covariance Matrix | Statistical Features | Non-Parametric Tests | AB-LS-SVM | Two-Classes = 99.64% |
Proposed Method | TQWT | Statistical + Frequency + Fractal and Entropy Features | Proposed Convolutional RNN (CNN–RNN) | Multi-Classes = 99.71% |
Works | Preprocessing | Feature Extraction | Feature Selection | Classification | Accuracy |
---|---|---|---|---|---|
[63] | Filtering | ApEn, SampEn, PE, PFuzzy | -- | SVM | 95.3% |
[64] | DWT | Energy, Entropy, STD, Mean | -- | SVM | 99.26% |
[65] | FFT | -- | -- | CNN | 92% |
[66] | NA | DWT, DESA, Temporal and Spatial Averaging | Feature Aggregation | RF, Logistic, SVM | 95% |
[67] | WPT | Relative Amplitude, PSD, PMRS | -- | weighted ELM | -- |
[68] | Time and Frequency Domain | -- | -- | CNN | -- |
[69] | Filtering, CSA | Linear and Non-Linear Features | -- | SVM | 96.8% |
[70] | WT | Maximum, Minimum, Mean, STD | Bag-of-Words | SVM | -- |
[71] | Filtering | -- | -- | LSTM | 97.75% |
[72] | FFT, Filtering | -- | -- | Integer-Net | 93.2% |
[73] | Filtering | Different Features | -- | SVM | 97.5% |
[74] | Filtering, HADTFD | TF-Flux, TF-Entropy, TF-Flatness | Spatial Averaging | Linear | 98.56% |
[75] | DWT | Uniform 1 D-LBP | -- | Different Methods | 95.33% |
[76] | -- | Linear and Non-Linear Features | Krill Herd Algorithm | Proposed Method | 98.9% |
Proposed Method | TQWT | Statistical + Frequency + Fractal and Entropy Features | Proposed Convolutional RNN (CNN–RNN) | 99.13 |
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Malekzadeh, A.; Zare, A.; Yaghoobi, M.; Kobravi, H.-R.; Alizadehsani, R. Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. Sensors 2021, 21, 7710. https://doi.org/10.3390/s21227710
Malekzadeh A, Zare A, Yaghoobi M, Kobravi H-R, Alizadehsani R. Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. Sensors. 2021; 21(22):7710. https://doi.org/10.3390/s21227710
Chicago/Turabian StyleMalekzadeh, Anis, Assef Zare, Mahdi Yaghoobi, Hamid-Reza Kobravi, and Roohallah Alizadehsani. 2021. "Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features" Sensors 21, no. 22: 7710. https://doi.org/10.3390/s21227710
APA StyleMalekzadeh, A., Zare, A., Yaghoobi, M., Kobravi, H. -R., & Alizadehsani, R. (2021). Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features. Sensors, 21(22), 7710. https://doi.org/10.3390/s21227710