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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | 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 |
| Abstract | 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 vector of size 108 containing the joint features of 9 channels. The network avoids any post-processing step in order to work as a full-fledged real-time application. For training and testing the model, EEG recordings of 3525 30-second segments from 19 neonates (postmenstrual age of 37 ± 05 weeks) are used. Results: For sleep-wake classification, mean Cohen's kappa between the network estimate and the ground truth annotation by human experts is 0.62. The maximum mean accuracy can reach up to 83% which, to date, is the highest accuracy for sleep-wake classification. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2020.3028182 |
| Journal | IEEE Access |
| Volume Number | 8 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2020-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Neonatal Sleep Staging Electroencephalogram Classification Multilayer Perceptron Neural Network |
| Content Type | Text |
| Resource Type | Article |
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