A Graph-Temporal Fused Dual-Input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals

Cai, Qing; Gao, Zhong-Ke; An, Jianpeng; Gao, Shuang; Grebogi, Celso

doi:10.1109/tcsii.2020.3014514

Cited by 44 publications

(22 citation statements)

References 33 publications

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“…For example, some methods adopted multiple parallel convolutional neural networks (CNNs) branches to extract better features from EEG signals [4], [6], [7]. Also, some methods included residual CNN layers [15], [16], while others used graph-based CNN networks [17]. On the other hand, Phan et al [18] proposed Long Short Term Memory (LSTM) networks to extract features from EEG spectrograms.…”

Section: A Sleep Stage Classificationmentioning

confidence: 99%

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Eldele¹,

Ragab²,

Chen³

et al. 2022

Preprint

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The past few years have witnessed a remarkable advance in deep learning for EEG-based sleep stage classification (SSC). However, the success of these models is attributed to possessing a massive amount of labeled data for training, limiting their applicability in real-world scenarios. In such scenarios, sleep labs can generate a massive amount of data, but labeling these data can be expensive and time-consuming. Recently, the selfsupervised learning (SSL) paradigm has shined as one of the most successful techniques to overcome the scarcity of labeled data. In this paper, we evaluate the efficacy of SSL to boost the performance of existing SSC models in the few-labels regime. We conduct a thorough study on three SSC datasets, and we find that fine-tuning the pretrained SSC models with only 5% of labeled data can achieve competitive performance to the supervised training with full labels. Moreover, self-supervised pretraining helps SSC models to be more robust to data imbalance and domain shift problems.

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Section: A Sleep Stage Classificationmentioning

confidence: 99%

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Eldele¹,

Ragab²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…This allows us to identify outliers, which likely pose greater environmental risk: Looking at a facility in the right tail, we find a facility which applied 15 times in Winter 2018, 42 times in Winter 2019, and 16 times in Winter 2020, when the average number of events across all facilities is 5. 4 As a policy matter, the results suggest that the permit terms for such a facility may not support to intensity of livestock production, requiring larger manure storage systems, reduction in animal count, or larger land application area. Identifying such outliers using this approach can help to allocate scarce compliance resources where needed most.…”

Section: Trends and Outliersmentioning

confidence: 97%

“…Separating application from other features (e.g., trees, dark sheds, roads) is easier if one can see whether purported application is a consistent feature across time. The dual CNN approach is inspired by several successful applications of this methodology to a wide range of tasks, such as fracture detection [7], optometry [50], and analyzing EEG [4].…”

Section: Image Classification Modelsmentioning

confidence: 99%

Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act

Chugg,

Rothbacher,

Feng

et al. 2022

Preprint

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This paper introduces a new, highly consequential setting for the use of computer vision for environmental sustainability. Concentrated Animal Feeding Operations (CAFOs) (aka intensive livestock farms or "factory farms") produce significant manure and pollution. Dumping manure in the winter months poses significant environmental risks and violates environmental law in many states. Yet the federal Environmental Protection Agency (EPA) and state agencies have relied primarily on self-reporting to monitor such instances of "land application." Our paper makes four contributions. First, we introduce the environmental, policy, and agricultural setting of CAFOs and land application. Second, we provide a new dataset of high-cadence (daily to weekly) 3m/pixel satellite imagery from 2018-20 for 330 CAFOs in Wisconsin with hand labeled instances of land application (n=57,697). Third, we develop an object detection model to predict land application and a system to perform inference in near real-time. We show that this system effectively appears to detect land application (PR AUC = 0.93) and we uncover several outlier facilities which appear to apply regularly and excessively. Last, we estimate the population prevalence of land application events in Winter 2021/22. We show that the prevalence of land application is much higher than what is self-reported by facilities. The system can be used by environmental regulators and interest groups, one of which piloted field visits based on this system this past winter. Overall, our application demonstrates the potential for AI-based computer vision systems to solve major problems in environmental compliance with near-daily imagery.

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“…According to expert standards, polysomnography (PSG) is divided into stages every 30 s. Professional sleep data evaluation can only be carried out in the hospital, and then experts judge each paragraph according to the classification criteria through visual observation. It is very cumbersome, time-consuming, and prone to subjective errors ( 10 ). An 8-h PSG data requires professional doctors to spend 2–4 h on sleep staging, which is very inefficient.…”

Section: Related Workmentioning

confidence: 99%

Intelligent automatic sleep staging model based on CNN and LSTM

Liu

Dai

Zhou

et al. 2022

Front. Public Health

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Since electroencephalogram (EEG) is a significant basis to treat and diagnose somnipathy, sleep electroencephalogram automatic staging methods play important role in the treatment and diagnosis of sleep disorders. Due to the characteristics of weak signals, EEG needs accurate and efficient algorithms to extract feature information before applying it in the sleep stages. Conventional feature extraction methods have low efficiency and are difficult to meet the time validity of fast staging. In addition, it can easily lead to the omission of key features owing to insufficient a priori knowledge. Deep learning networks, such as convolutional neural networks (CNNs), have powerful processing capabilities in data analysis and data mining. In this study, a deep learning network is introduced into the study of the sleep stage. In this study, the feature fusion method is presented, and long-term and short-term memory (LSTM) is selected as the classification network to improve the accuracy of sleep stage recognition. First, based on EEG and deep learning network, an automatic sleep phase method based on a multi-channel EGG is proposed. Second, CNN-LSTM is used to monitor EEG and EOG samples during sleep. In addition, without any signal preprocessing or feature extraction, data expansion (DA) can be realized for unbalanced data, and special data and non-general data can be deleted. Finally, the MIT-BIH dataset is used to train and evaluate the proposed model. The experimental results show that the EEG-based sleep phase method proposed in this paper provides an effective method for the diagnosis and treatment of sleep disorders, and hence has a practical application value.

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A Graph-Temporal Fused Dual-Input Convolutional Neural Network for Detecting Sleep Stages from EEG Signals

Cited by 44 publications

References 33 publications

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation

Detecting Environmental Violations with Satellite Imagery in Near Real Time: Land Application under the Clean Water Act

Intelligent automatic sleep staging model based on CNN and LSTM

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