Automatic Sleep Stage Scoring on Raw Single-Channel EEG : A comparative analysis of CNN Architectures

Parekh, Nirali; Dave, Bhavi; Shah, Raj; Srivastava, Kriti

doi:10.1109/icecct52121.2021.9616895

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Convolutional neural networks were originally proposed to solve the task of image classification, such as LeNet, AlexNet, VGG and other classical convolutional neural networks [14]. However, a major drawback of classical convolutional neural networks when dealing with classification problems with multi-stage nature (such as the ''sleep'' case) is that they fail to take into account the time dependence [18]. This is because in the design of traditional convolutional neural networks, all convolution layers and pooling layers are translationally invariant, and such structures cannot directly deal with the time-dependent properties of sequential signals.…”

Section: Deep Representation Mechanism Of Cnnsmentioning

confidence: 99%

“…Eldele et al [17] proposed an attentionbased architecture to classify sleep stages using singlechannel EEG signals, which start with a feature extraction module based on multi-resolution convolutional neural networks and adaptive feature recalibination. In addition, many CNN-based methods have been used to classify one-dimensional biological signals [18], such as Phan et al [19] proposed a joint classification-and-prediction framework based on convolutional neural networks for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework.…”

Section: Introductionmentioning

confidence: 99%

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Sle-CNN: a novel convolutional neural network for sleep stage classification

Zhang

Xue

Słowik

et al. 2023

Neural Comput & Applic

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Many classical methods have been used in automatic sleep stage classification but few methods explore deep learning. Meanwhile, most deep learning methods require extensive expertise and suffer from a mass of handcrafted steps which are time-consuming. In this paper, we propose an efficient convolutional neural network, Sle-CNN, for five-sleep-stage classification. We attach each kernel in the first layers with a trainable coefficient to enhance the learning ability and flexibility of the kernel. Then, we make full use of the genetic algorithm’s heuristic search and the advantage of no need for the gradient to search for the sleep stage classification architecture. We verify the convergence of Sle-CNN and compare the performance of traditional convolutional neural networks before and after using the trainable coefficient. Meanwhile, we compare the performance between the Sle-CNN generated through genetic algorithm and the traditional convolutional neural networks. The experiments demonstrate that the convergence of Sle-CNN is faster than the normal convolutional neural networks and the Sle-CNN generated by genetic algorithm outperforms the traditional handcrafted counterparts too. Our research suggests that deep learning has a great potential on electroencephalogram signal processing, especially with the intensification of neural architecture search. Meanwhile, neural architecture search can exert greater power in practical engineering applications. We conduct the Sle-CNN with the Python library, Pytorch, and the code and models will be publicly available.

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Section: Deep Representation Mechanism Of Cnnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sle-CNN: a novel convolutional neural network for sleep stage classification

Zhang

Xue

Słowik

et al. 2023

Neural Comput & Applic

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“…In the field of biomedical engineering, CNN is also widely used in medical imaging and classification of one-dimensional biological signals, such as EEG and ECG. In recent years, many sleep EEG classification methods based on CNN are also developing rapidly [18]. In [19], the authors used successive convolution and pooling layers with fully-connected layers and the overall accuracy was 74%.…”

mentioning

confidence: 99%

EEG-Based Sleep Stage Classification via Neural Architecture Search

Kong

Peng

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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With the improvement of quality of life, people are more and more concerned about the quality of sleep. The electroencephalogram (EEG)-based sleep stage classification is a good guide for sleep quality and sleep disorders. At this stage, most automatic staging neural networks are designed by human experts, and this process is time-consuming and laborious. In this paper, we propose a novel neural architecture search (NAS) framework based on bilevel optimization approximation for EEG-based sleep stage classification. The proposed NAS architecture mainly performs the architectural search through a bilevel optimization approximation, and the model is optimized by search space approximation and search space regularization with parameters shared among cells. Finally, we evaluated the performance of the model searched by NAS on the Sleep-EDF-20, Sleep-EDF-78 and SHHS datasets with an average accuracy of 82.7%, 80.0% and 81.9%, respectively. The experimental results show that the proposed NAS algorithm provides some reference for the subsequent automatic design of networks for sleep classification.

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