EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network

EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network

Objective: Classification of sleep-wake states using multichannel electroencephalography (EEG) data that reliably work for neonates. Methods: A deep multilayer perceptron (MLP) neural network is developed to classify sleep-wake states using multichannel bipolar EEG signals, which takes an input vect...

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Bibliographic Details
Main Authors: Saadullah Farooq Abbasi, Jawad Ahmad, Ahsen Tahir, Muhammad Awais, Chen Chen, Muhammad Irfan, Hafiza Ayesha Siddiqa, Abu Bakar Waqas, Xi Long, Bin Yin, Saeed Akbarzadeh, Chunmei Lu, Laishuan Wang, Wei Chen
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Neonatal sleep staging
electroencephalogram
classification
multilayer perceptron
neural network
Online Access:https://ieeexplore.ieee.org/document/9210487/

Internet

https://ieeexplore.ieee.org/document/9210487/

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