Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

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Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2016-0156 ◽

2018 ◽

Vol 63 (2) ◽

pp. 177-190 ◽

Cited By ~ 18

Author(s):

Junming Zhang ◽

Yan Wu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Single Channel ◽

Sleep Stage ◽

Experimental Results ◽

Sleep Stages ◽

Stage Classification ◽

Sleep Stage Classification ◽

Complex Valued ◽

Learned Features

AbstractMany systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

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Automatic Sleep Stage Classification Using 1D Convolutional Neural Network

Frontiers in Biomedical Technologies ◽

10.18502/fbt.v7i3.4616 ◽

2020 ◽

Author(s):

Asma Salamatian ◽

Ali Khadem

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Deep Learning ◽

Convolutional Neural Network ◽

Single Channel ◽

Sleep Stage ◽

Sleep Stages ◽

Eeg Signal ◽

Stage Classification ◽

Sleep Stage Classification

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-learn the discriminative features from the EEG signal. Results: Applying the proposed method to sleep-EDF dataset resulted in overall accuracy, sensitivity, specificity, and Precision of 94.09%, 74.73%, 96.43%, and 71.02%, respectively, for classifying five sleep stages. Using single-channel EEG and providing a network with fewer trainable parameters than most of the available deep learning-based methods are the main advantages of the proposed method. Conclusion: In this study, a 13-layer 1D CNN model was proposed for sleep stage classification. This model has an end-to-end complete architecture and does not require any separate feature extraction/selection and classification stages. Having a low number of network parameters and layers while still having high classification accuracy, is the main advantage of the proposed method over most of the previous deep learning-based approaches.

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Improving Sleep Stage Classification Performance by Single-Channel EEG Data Augmentation via Spectral Band Blending

2021 9th International Winter Conference on Brain-Computer Interface (BCI) ◽

10.1109/bci51272.2021.9385297 ◽

2021 ◽

Author(s):

Choel-Hui Lee ◽

Hyun-Ji Kim ◽

Jae-Wook Heo ◽

Hakseung Kim ◽

Dong-Joo Kim

Keyword(s):

Data Augmentation ◽

Single Channel ◽

Sleep Stage ◽

Spectral Band ◽

Classification Performance ◽

Eeg Data ◽

Stage Classification ◽

Sleep Stage Classification

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Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

Applied Sciences ◽

10.3390/app10051797 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1797 ◽

Cited By ~ 2

Author(s):

Mera Kartika Delimayanti ◽

Bedy Purnama ◽

Ngoc Giang Nguyen ◽

Mohammad Reza Faisal ◽

Kunti Robiatul Mahmudah ◽

...

Keyword(s):

Machine Learning ◽

Sleep Stage ◽

Machine Learning Algorithms ◽

High Dimensional ◽

Sleep Stages ◽

Eeg Signals ◽

Stage Classification ◽

Sleep Stage Classification ◽

Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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CCRRSleepNet: A Hybrid Relational Inductive Biases Network for Automatic Sleep Stage Classification on Raw Single-Channel EEG

Brain Sciences ◽

10.3390/brainsci11040456 ◽

2021 ◽

Vol 11 (4) ◽

pp. 456

Author(s):

Wenpeng Neng ◽

Jun Lu ◽

Lei Xu

Keyword(s):

Deep Learning ◽

Single Channel ◽

Sleep Stage ◽

Learning Model ◽

Actual Performance ◽

Feature Extraction Method ◽

Inductive Bias ◽

Stage Classification ◽

Sleep Stage Classification ◽

Deep Learning Model

In the inference process of existing deep learning models, it is usually necessary to process the input data level-wise, and impose a corresponding relational inductive bias on each level. This kind of relational inductive bias determines the theoretical performance upper limit of the deep learning method. In the field of sleep stage classification, only a single relational inductive bias is adopted at the same level in the mainstream methods based on deep learning. This will make the feature extraction method of deep learning incomplete and limit the performance of the method. In view of the above problems, a novel deep learning model based on hybrid relational inductive biases is proposed in this paper. It is called CCRRSleepNet. The model divides the single channel Electroencephalogram (EEG) data into three levels: frame, epoch, and sequence. It applies hybrid relational inductive biases from many aspects based on three levels. Meanwhile, multiscale atrous convolution block (MSACB) is adopted in CCRRSleepNet to learn the features of different attributes. However, in practice, the actual performance of the deep learning model depends on the nonrelational inductive biases, so a variety of matching nonrelational inductive biases are adopted in this paper to optimize CCRRSleepNet. The CCRRSleepNet is tested on the Fpz-Cz and Pz-Oz channel data of the Sleep-EDF dataset. The experimental results show that the method proposed in this paper is superior to many existing methods.

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