Inter-intra feature for the complementary convolutional neural network in the effective classification of epileptic seizures

The electrical activity of the brain can be monitored using the electroencephalogram (EEG), which can be used in the detection of seizures. This paper proposes an epileptic seizure detection algorithm that uses inter-intra Head-body-tail (HBT) features. Initially, the EEG signals are subdivided into...

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Vydáno v:Multimedia tools and applications Ročník 83; číslo 35; s. 82699 - 82718
Hlavní autoři: Bell, T. Beula, Latha, D., Sheela, C. Jaspin Jeba
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.10.2024
Springer Nature B.V
Témata:
Accuracy
Algorithms
Artificial neural networks
Computer Communication Networks
Computer Science
Convulsions & seizures
Data Structures and Information Theory
Datasets
Electroencephalography
Epilepsy
Feature extraction
Multimedia Information Systems
Neural networks
Seizures
Sensitivity analysis
Special Purpose and Application-Based Systems
Epileptic seizures
EEG signal
Convolutional kernel
Convolutional neural network
Accumulation layer
ISSN:1573-7721, 1380-7501, 1573-7721
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Shrnutí:The electrical activity of the brain can be monitored using the electroencephalogram (EEG), which can be used in the detection of seizures. This paper proposes an epileptic seizure detection algorithm that uses inter-intra Head-body-tail (HBT) features. Initially, the EEG signals are subdivided into non-overlapping frames. From each frame, three regions namely head (H), body (B), and tail (T) are constructed. Intra features named intra-correlative features are extracted within a frame while the inter-HBT features namely magnitude change and zero crossing feature are extracted between adjacent HBT frames. This paper also proposes a complementary convolutional neural network (CP-CNN) which uses two parallel sections of a traditional 1 D -CNN network. The complementary CNN is proposed to preserve the components that are eliminated by the traditional 1 D -CNN network in the feature extraction process. The evaluation of the proposed seizure detection algorithm was done with the evaluation metrics namely accuracy, sensitivity, F1-score, and specificity with the datasets namely CHB-MIT EEG dataset where the recordings were done from 22 subjects, and the Siena EEG dataset where the recordings were performed from 14 subjects. The proposed seizure detection provides an accuracy, sensitivity, F1-score, and Specificity of 98.17 % , 94.21 % , 95.79 % , and 98.25 % respectively for the CHB-MIT dataset. For the Siena-EEG dataset the accuracy, sensitivity, F1-score, and specificity were estimated as 98.76 % , 94.15 , 95.14%, and 98.77 % respectively.
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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-024-18742-7