RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals
Abstract
:1. Introduction
- A hybrid of recurrent neural networks (RNNs) and bi-directional long short-term memories (BiLSTM) is proposed for the purpose of automatically identifying epileptic seizures through the processing of EEG signals.
- The efficacy of the newly developed model is validated by conducting a complete comparison to the existing state-of-the-art learning models.
- The recommended method provides a number of advantages, including shorter periods of time needed for detection, a reduced proportion of false positive results, increased sensitivity, and increased specificity.
2. Literature Review
3. Materials and Methods
3.1. Data Collection and Preprocessing
3.2. The Proposed RNN-BiLSTM Learning Model
3.3. Model Training and Testing
4. Results and Discussion
5. Comparing the Proposed Model to Traditional ML Models
6. Comparing the Performance of the Proposed Model to State-of-the-Art
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Class Description | No. of Samples | Class Labels | Binary Classification Samples |
---|---|---|---|
seizure | 2300 | 1 | 2300 |
First normal (Before seizure the signal of the patient)- | 2300 | 0 | 9200 |
Second normal (Healthy brain EEG recorded data)- | 2300 | - | - |
Third normal—Eyes closed have no seizure | 2300 | - | - |
Fourth normal—Eyes opened have no-seizure | 2300 | - | - |
Layers | Other Parameters | Value |
---|---|---|
RNN layer | Batch size = 100 Epochs = 100 Learning rate = 0.01 ADAM | Hidden 100 Units |
BiLSTM | Hidden 200 Units | |
FC layer | 2 FC layers- | |
SoftMax | Cross entropy | |
Classification Layer |
Classifier | Learning Parameters |
---|---|
SVM | Kernel function = Sigmoid Kernel Function |
KNN | K = 5 |
RF | n-estimators = 10 |
Publication | Method | EEG Class | Dataset | Acc (%) | Sens (%) | Spec (%) |
---|---|---|---|---|---|---|
Yao et al. [34] | Independent RNN | binary | CHB-MIT | 87% | 87.3% | 86.7% |
Raghu et al. [32] | Transfer learning and CNN | 8 classes | Temple University Hospital EEG signals | 88.3% | - | - |
Choi et al. [21] | hybrid model (1D CNN and GRU) | binary | Asan Medical Center Children’s Hospital | 82.86 % | 80% | - |
Najafi et al. [2]. | hybrid RNN and LSTM | binary | HCTM hospital’s EEG data | 96.1% | 96.8% | 97.4% |
Hilal et al. [6] | Deep Clinical Sparse Autoencoder | binary | UCI-Epileptic | 98.67%. | 99.19% | 99.2% |
Mursalin et al. [33] | Hybrid metaheuristic Feature selection, and traditional ML-based classifiers | binary | UCI-Epileptic | 98.7% | - | - |
Proposed model | Hybrid RNN-BiLSTM model | binary | UCI-Epileptic | 98.4% | 98.30% | 98.10% |
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Samee, N.A.; Mahmoud, N.F.; Aldhahri, E.A.; Rafiq, A.; Muthanna, M.S.A.; Ahmad, I. RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals. Life 2022, 12, 1946. https://doi.org/10.3390/life12121946
Samee NA, Mahmoud NF, Aldhahri EA, Rafiq A, Muthanna MSA, Ahmad I. RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals. Life. 2022; 12(12):1946. https://doi.org/10.3390/life12121946
Chicago/Turabian StyleSamee, Nagwan Abdel, Noha F. Mahmoud, Eman A. Aldhahri, Ahsan Rafiq, Mohammed Saleh Ali Muthanna, and Ijaz Ahmad. 2022. "RNN and BiLSTM Fusion for Accurate Automatic Epileptic Seizure Diagnosis Using EEG Signals" Life 12, no. 12: 1946. https://doi.org/10.3390/life12121946