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Cluster Computing

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26.06.2023

Hybrid cuckoo finch optimisation based machine learning classifier for seizure prediction using EEG signals in IoT network

verfasst von: Bhaskar Kapoor, Bharti Nagpal

Erschienen in: Cluster Computing | Ausgabe 2/2024

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Abstract

The Internet of Things (IoT) is an indispensable part of the healthcare system since it creates a link between the doctor and the patient for remote medical consultations. IoT-based seizure prediction detects seizures and monitors the health of patients remotely. The disease seizure is classified as the sudden and repeated malfunction of the neurons of the brain. To protect patients’ lives, it’s critical to recognise the risk of an epileptic seizure. In this research, a hybrid cuckoo finch optimisation is proposed using a tuned Deep-CNN (Deep-Convolutional Neural Network) classifier to recognise and predict the occurrence of epileptic seizures using electroencephalogram (EEG) signal data obtained through IoT. Initially, the gathered data is pre-processed and subjected to frequency band generation. Then there are the notable characteristics, such as statistical features, wavelet features, entropy-based features, spectral features, CPR (common spatial patterns), and logarithmic band power, which are extracted and concatenated. The optimal electrode selection is done by using the proposed hybrid cuckoo finch optimisation that inherits the characteristics of intrusive and attentive search agents. The data is finally normalised and fed to the proposed hybrid cuckoo finch optimisation tuned to Deep-CNN to classify the seizure disease. The specificity, accuracy, and sensitivity of the proposed model are 92.5212%, 97.7648%, and 95.6324%, which demonstrate the efficient performance of the proposed seizure prediction model.

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Titel: Hybrid cuckoo finch optimisation based machine learning classifier for seizure prediction using EEG signals in IoT network
verfasst von: Bhaskar Kapoor
Bharti Nagpal
Publikationsdatum: 26.06.2023
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 2/2024
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-023-04059-x

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