The proposed method achieved 100% accuracy in detecting seizures in binary classification tasks on two datasets.
A hybrid deep learning approach combining feature fusion was developed for efficient seizure detection.
The method utilized Discrete Wavelet Transform to extract time-frequency and nonlinear features from signals.
Support Vector Machine-Recursive Feature Elimination was employed to select the most distinctive features for fusion.
In a three-class classification task on the Bonn dataset, the model achieved 96.19% accuracy.
Validation on the CHB-MIT dataset resulted in average metrics of 98.43% accuracy and 97.84% sensitivity.
Simplified
BACKGROUND: The diagnosis and treatment of epilepsy continue to face numerous challenges, highlighting the urgent need for the development of rapid, accurate, and non-invasive methods for seizure detection. In recent years, advancements in the analysis of electroencephalogram () signals have garnered widespread attention, particularly in the area of seizure recognition.
METHODS: A novel hybrid deep learning approach that combines feature fusion for efficient seizure detection is proposed in this study. First, the Discrete Wavelet Transform (DWT) is applied to perform a five-level decomposition of the raw EEG signals, from which time-frequency and nonlinear features are extracted from the decomposed sub-bands. To eliminate redundant features, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is employed to select the most distinctive features for fusion. Finally, seizure states are classified using - (CNN-Bi-LSTM).
RESULTS: The method was rigorously validated on the Bonn and New Delhi datasets. In the binary classification tasks, both the D-E group (Bonn dataset) and the Interictal-Ictal group (New Delhi dataset) achieved 100% accuracy, 100% sensitivity, 100% specificity, 100% precision, and 100% F1-score. In the three-class classification task A-D-E on the Bonn dataset, the model performed excellently, achieving 96.19% accuracy, 95.08% sensitivity, 97.34% specificity, 97.49% precision, and 96.18% F1-score. In addition, the proposed method was further validated on the larger and more clinically relevant CHB-MIT dataset, achieving average metrics of 98.43% accuracy, 97.84% sensitivity, 99.21% specificity, 99.14% precision, and an F1 score of 98.39%. Compared to existing literature, our method outperformed several recent studies in similar classification tasks, underscoring the effectiveness and advancement of the approach presented in this research.
CONCLUSION: The findings indicate that the proposed method demonstrates a high level of effectiveness in detecting seizures, which is a crucial aspect of managing epilepsy. By improving the accuracy of seizure detection, this method has the potential to significantly enhance the process of diagnosing and treating individuals affected by epilepsy. This advancement could lead to more tailored treatment plans, timely interventions, and ultimately, better quality of life for patients.
Key numbers
100%
Accuracy in Binary Classification
Achieved on both Bonn and New Delhi datasets.
96.19%
Accuracy in Three-Class Classification
Achieved on the Bonn dataset.
98.43%
Average Accuracy on CHB-MIT Dataset
Evaluated on a larger, clinically relevant dataset.
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Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.
