Epileptic Seizure Prediction from Spectral, Temporal, and Spatial Features of EEG Signals Using Deep Learning Algorithms

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Mohammadkhani Ghiasvand Nazanin; GHADERI FOAD

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Journal Paper

Paper Information

Journal: Neuroscience Journal of Shefaye Khatam Year:2020 | Volume:9 | Issue:1 Page(s): 110-119

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Information Journal Paper

Title

Epileptic Seizure Prediction from Spectral, Temporal, and Spatial Features of EEG Signals Using Deep Learning Algorithms

Author(s)

Mohammadkhani Ghiasvand Nazanin | GHADERI FOAD | Issue Writer Certificate

Keywords

PatientsQ1

Deep LearningQ1

ElectroencephalographyQ1

Abstract

Introduction: Epilepsy is one of the mos t common brain disorders that greatly affect Patients’ life. However, early detection of seizure attacks can significantly improve their quality of life. In this s tudy, we evaluated a deep neural network to learn robus t features from Electroencephalography (EEG) signals to automatically detect and predict seizure attacks. Materials and Methods: The architecture consis ts of convolutional neural networks and long short-term memory networks. It is designed to simultaneously capture spectral, temporal, and spatial information. Moreover, the architecture does not rely on explicit channel selection algorithms. The method is applied to the Children’ s Hospital of Bos ton-Massachusetts Ins titute of Technology dataset (CHB-MIT). To evaluate the method, the proposed model is trained in the patient-specific approach. Results: The proposed architecture achieves a sensitivity of 90. 7 ± 7. 9 percent, a false prediction rate of 0. 12/h, and a mean prediction time of 36. 8 minutes. Moreover, in the cases of focal seizures, the proposed model es timates the seizure focus. Conclusion: The proposed model achieved a high capability in seizure prediction. Moreover, by using the automated feature selection of the Deep Learning algorithm, the patterns of the pre-ictal period in EEG signals were determined. Furthermore, by specifying the seizure focus, the model can help neurologists to take further curative actions.

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APA: Copy

Mohammadkhani Ghiasvand, Nazanin, & GHADERI, FOAD. (2021). Epileptic Seizure Prediction from Spectral, Temporal, and Spatial Features of EEG Signals Using Deep Learning Algorithms. NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM, 9(1 ), 110-119. SID. https://sid.ir/paper/416340/en

Vancouver: Copy

Mohammadkhani Ghiasvand Nazanin, GHADERI FOAD. Epileptic Seizure Prediction from Spectral, Temporal, and Spatial Features of EEG Signals Using Deep Learning Algorithms. NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM[Internet]. 2021;9(1 ):110-119. Available from: https://sid.ir/paper/416340/en

IEEE: Copy

Nazanin Mohammadkhani Ghiasvand, and FOAD GHADERI, “Epileptic Seizure Prediction from Spectral, Temporal, and Spatial Features of EEG Signals Using Deep Learning Algorithms,” NEUROSCIENCE JOURNAL OF SHEFAYE KHATAM, vol. 9, no. 1 , pp. 110–119, 2021, [Online]. Available: https://sid.ir/paper/416340/en

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