Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection

Unsupervised Feature Representation Based on Deep Boltzmann Machine for Seizure Detection

The Electroencephalogram (EEG) pattern of seizure activities is highly individual-dependent and requires experienced specialists to annotate seizure events. It is clinically time-consuming and error-prone to identify seizure activities by visually scanning EEG signals. Since EEG data are heavily und...

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Bibliographic Details
Main Authors: Tengzi Liu, Muhammad Zohaib Hassan Shah, Xucun Yan, Dongping Yang
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Seizure detection
electroencephalogram (EEG)
Deep Boltzmann Machine (DBM)
discrete wavelet transform (DWT)
Fisher’s discriminant function
Online Access:https://ieeexplore.ieee.org/document/10064189/

Internet

https://ieeexplore.ieee.org/document/10064189/

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