January 6, 2025BMC Medical Informatics and Decision Making60 citationsOpen Access

A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection

XCXiaoshuai CaoFirst Affiliated Hospital of Henan University of Science and Technology SZShaojie ZhengNanjing Forestry University JZJincan ZhangHenan University of Science and Technology

Structured PICO

Population

EEG datasets (Bonn, New Delhi, and CHB-MIT datasets) for epilepsy seizure detection

Intervention

Hybrid CNN-Bi-LSTM model with feature fusion (Discrete Wavelet Transform and Support Vector Machine-Recursive Feature Elimination)

Comparator

Existing literature/methods

Outcome

Seizure detection accuracy, sensitivity, specificity, precision, and F1-score

A novel hybrid CNN-Bi-LSTM deep learning model demonstrates high accuracy in detecting epileptic seizures from EEG signals across multiple datasets.

Abstract

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 (EEG) 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 Convolutional Neural Network-Bidirectional Long Short-Term Memory (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.

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