A Two-Stage Neural Network for Sleep Stage Classification Based on Feature Learning, Sequence Learning, and Data Augmentation

Sun, Chenglu; Fan, Jiahao; Chen, Chen; Li, Wei; Chen, Wei

doi:10.1109/access.2019.2933814

Cited by 55 publications

(44 citation statements)

References 33 publications

(40 reference statements)

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“…Sun et al (2019) scored the sleep stage automatically. This study presents a stage-classification method based on a two-stage neural network [97]. The first, feature learning stage can fuse network-trained features with traditional hand-crafted features.…”

Section: Ruffini Et Al (2019)mentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

256

140

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Highlights • Data augmentation (DA) is increasingly used with deep learning (DL) on EEG • It enhances decoding accuracy left unexplained by 29% on average on the datasets we review • We analyze which specific DA techniques appear to work best for which EEG tasks • We tested various DA techniques on an open motor-imagery task and compared the accuracy gains to demonstrate the usefulness of DA for DL-based EEG analysis • We propose guidelines for reporting parameters for different DA techniques Abstract-Background Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.-New method We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?-Results DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively.-Comparing with existing methods Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload-29% on average.-Conclusions DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.

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Section: Ruffini Et Al (2019)mentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

256

140

View full text Add to dashboard Cite

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“…On the other hand, monitoring the sleep stage is also an important way to evaluate sleep state. In the period of 90-100 minutes, there are two different stages: non-rapid eye movement sleep (NREMs), and rapid eye movement sleep (REMs) [3]. Insomnia is the most prominent and frequent manifestation of sleep disorders.…”

Section: Introductionmentioning

confidence: 99%

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and overfitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: ''https://github.com/zhaoaite/SSRM ''.

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“…Computer-aided methods like machine learning algorithms are used for sleep stage classification to solve described problems. Recently many studies have developed machine learning methods for sleep staging to achieve reliable and practical results [6]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…It is well established that RNN, e.g. long short-term memory (LSTM) [20] is functional in sequential modeling tasks like sleep stage classification [6], [21]. Convolutional neural network (CNN) is developed for the processing of multidimensional data such as 2D images.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

et al. 2020

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The most important part of sleep quality assessment is the classification of sleep stages, which helps to diagnose sleep-related disease. In the traditional sleep staging method, subjects have to spend a night in the sleep clinic for recording polysomnogram. Sleep expert classifies the sleep stages by monitoring the signals, which is time consuming and frustrating task and can be affected by human error. New studies propose fully automated techniques for classifying sleep stages that makes sleep scoring possible at home. Despite comprehensive studies have been presented in this field the classification results have not yet reached the gold standard due to the concentration on the use of a limited source of information such as single channel EEG. Therefore, this article introduces a new method for fusing two sources of information, including electroencephalogram (EEG) and electrooculogram (EOG), to achieve promising results in the classification of sleep stages. In the proposed method, extracted features from the EEG and EOG signals, are divided into two feature sets consisting of the EEG features and fused features of EEG and EOG. Then, each feature set transformed into a horizontal visibility graph (HVG). The images of the HVG are produced in a novel framework and classified by proposed transfer learning convolutional neural network for data fusion (TLCNN-DF). Employing transfer learning at the training stage of the model has accelerated the training process of the CNN and improved the performance of the model. The proposed algorithm is used to classify the Sleep-EDF and Sleep-EDFx benchmark datasets. The algorithm can classify the Sleep-EDF dataset with an accuracy of 93.58% and Cohen's kappa coefficient of 0.899. The results show proposed method can achieve superior performance compared to state-of-the-art studies on classification of sleep stages. Furthermore, it can attain reliable results as an alternative to conventional sleep staging. INDEX TERMS Convolutional neural network, data fusion, horizontal visibility graph, Sleep stage classification, transfer learning.

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A Two-Stage Neural Network for Sleep Stage Classification Based on Feature Learning, Sequence Learning, and Data Augmentation

Cited by 55 publications

References 33 publications

Data augmentation for deep-learning-based electroencephalography

Data augmentation for deep-learning-based electroencephalography

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Transfer Learning Convolutional Neural Network for Sleep Stage Classification Using Two-Stage Data Fusion Framework

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