TY - GEN
T1 - Double Attention-Based Deep Convolutional Neural Network for Seizure Detection Using EEG Signals
AU - Shi, Lin
AU - Wang, Zexin
AU - Ma, Yuanwei
AU - Chen, Jianjun
AU - Xu, Jingzhou
AU - Qi, Jun
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Epilepsy is a common neurological disorder caused by sudden and transient excessive excitation of neurons in the brain due to abnormal electrical dis-charge. Existing diagnosis methods using brain magnetic resonance imaging (MRI) and computed tomography (CT) scans have limitations that they rely on episodic symptoms and the subjectivity of doctors. Although electroencephalography (EEG) signals only record the electrical activity on the surface of the cerebral cortex and require appropriate timing for examination to capture abnormal brain electrical activity, EEG examinations are non-invasive and safe for patients. They can be used for real-time monitoring and evaluating treatment effects, playing an important role in the diagnosis and treatment of epilepsy. In this study, we proposed a Double Attention-based Convolutional Neural Network (CNN) for Seizure detection. It computed the Pearson Correlation Coefficient (PCC) of each channel, and mapping them to a correlation coefficient matrix for positional encoding, with attention-based CNN model feature extraction to obtain the final classification results. From AUBMC dataset and the CHB-MIT dataset, our proposed model achieved classification accuracies of 90.88% and 93.69% respectively.
AB - Epilepsy is a common neurological disorder caused by sudden and transient excessive excitation of neurons in the brain due to abnormal electrical dis-charge. Existing diagnosis methods using brain magnetic resonance imaging (MRI) and computed tomography (CT) scans have limitations that they rely on episodic symptoms and the subjectivity of doctors. Although electroencephalography (EEG) signals only record the electrical activity on the surface of the cerebral cortex and require appropriate timing for examination to capture abnormal brain electrical activity, EEG examinations are non-invasive and safe for patients. They can be used for real-time monitoring and evaluating treatment effects, playing an important role in the diagnosis and treatment of epilepsy. In this study, we proposed a Double Attention-based Convolutional Neural Network (CNN) for Seizure detection. It computed the Pearson Correlation Coefficient (PCC) of each channel, and mapping them to a correlation coefficient matrix for positional encoding, with attention-based CNN model feature extraction to obtain the final classification results. From AUBMC dataset and the CHB-MIT dataset, our proposed model achieved classification accuracies of 90.88% and 93.69% respectively.
KW - Classification
KW - Convolutional Neural Network (CNN)
KW - Double Attention
KW - Electroencephalography (EEG)
KW - Epilepsy
KW - Pearson Correlation Coefficient (PCC)
UR - http://www.scopus.com/inward/record.url?scp=85201183451&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-5692-6_35
DO - 10.1007/978-981-97-5692-6_35
M3 - Conference Proceeding
AN - SCOPUS:85201183451
SN - 9789819756919
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 392
EP - 404
BT - Advanced Intelligent Computing in Bioinformatics - 20th International Conference, ICIC 2024, Proceedings
A2 - Huang, De-Shuang
A2 - Pan, Yijie
A2 - Zhang, Qinhu
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Intelligent Computing, ICIC 2024
Y2 - 5 August 2024 through 8 August 2024
ER -