A Novel Sleep Staging Algorithm Based on Hybrid Neural Network

Hao, Jingwei; Luo, Senlin; Pan, Limin

doi:10.1109/iceiec.2019.8784612

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Afterwards, two-layer Bi-LSTMs are employed to learn how features extracted from adjacent epochs are related to each other, and to then classify the different sleep stages. A very similar model was studied in [27], which achieved an average accuracy of 92.21% on single-channel EEG data. Another mixed architecture formed in conjugation with multilayer perceptron and an LSTM was proposed in [28] to take advantage of both sparse patterns and sequential patterns in the temporal domain of an EEG signal.…”

Section: Related Workmentioning

confidence: 99%

Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals

Hasan

Koo

2023

Diagnostics

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Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification. In this paper, a hybrid deep learning model is proposed for classifying sleep and wake states based on a single-channel electroencephalogram (EEG) signal. The model combines an artificial neural network (ANN) and a convolutional neural network (CNN) trained using mixed-input features. The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch. The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded. The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings.

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Section: Related Workmentioning

confidence: 99%

Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals

Hasan

Koo

2023

Diagnostics

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“…A fusion of CNN and the Bi-LSTM layer performs its convolutional and classification operation in the proposed HNM network. There are many more studies that experienced hybrid models [18]. The hybrid model comprises more than one classical network [52].…”

Section: A Neoteric Topology -Hybrid Network Modelmentioning

confidence: 99%

A deep learning hybrid ensemble fusion for chest radiograph classification

Sultana¹,

Hussain²,

Hashmani³

et al. 2021

NNW

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Biomedical imaging, archiving, and classification is the recent challenge of computer-aided medical imaging. The popular and influential Deep Learning methods predict and congregate distinct markable features of ambiguity in radiographs precisely and accurately. This study submits a new topology of a deep learning network for chest radiograph classification. In this approach, a hybrid ensemble fusion of neural network topology can better diagnose ambiguities with high precision. The proposed topology also compares statistical findings with three optimizers and the most possible varying essential attributes of dropout probabilities and learning rates. The performance as a function of the AUCROC of this model is measured on the Chest Xpert dataset.

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Convolutional Transformer with Domain Adversarial Learning for Multi-Channel Sleep Stage Classification

Wang,

Mei,

et al. 2023

2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

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A Novel Sleep Staging Algorithm Based on Hybrid Neural Network

Cited by 6 publications

References 11 publications

Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals

Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals

A deep learning hybrid ensemble fusion for chest radiograph classification

Convolutional Transformer with Domain Adversarial Learning for Multi-Channel Sleep Stage Classification

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