Identifying interesting relationships between pairs of variables in large data sets is increasingly important. Here, we present a measure of dependence for two-variable relationships: the maximal information coefficient (MIC). MIC captures a wide range of associations both functional and not, and for functional relationships provides a score that roughly equals the coefficient of determination (R2) of the data relative to the regression function. MIC belongs to a larger class of maximal information-based nonparametric exploration (MINE) statistics for identifying and classifying relationships. We apply MIC and MINE to data sets in global health, gene expression, major-league baseball, and the human gut microbiota and identify known and novel relationships.

Detecting Novel Associations in Large Data Sets

Sleep stage classification is essential for sleep assessment and disease diagnosis. Although previous attempts to classify sleep stages have achieved high classification performance, several challenges remain open: 1) How to effectively utilize time-varying spatial and temporal features from multi-channel brain signals remains challenging. Prior works have not been able to fully utilize the spatial topological information among brain regions. 2) Due to the many differences found in individual biological signals, how to overcome the differences of subjects and improve the generalization of deep neural networks is important. 3) Most deep learning methods ignore the interpretability of the model to the brain. To address the above challenges, we propose a multi-view spatial-temporal graph convolutional networks (MSTGCN) with domain generalization for sleep stage classification. Specifically, we construct two brain view graphs for MSTGCN based on the functional connectivity and physical distance proximity of the brain regions. The MSTGCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. In addition, attention mechanism is employed for capturing the most relevant spatial-temporal information for sleep stage classification. Finally, domain generalization and MSTGCN are integrated into a unified framework to extract subject-invariant sleep features. Experiments on two public datasets demonstrate that the proposed model outperforms the state-of-the-art baselines.

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Multi-View Spatial-Temporal Graph Convolutional Networks With Domain Generalization for Sleep Stage Classification

Multimedia stimulation of brain activities has not only become an emerging field for intensive research, but also achieves important progress in the electroencephalogram (EEG) emotion classification based on brain activities. However, how to make full use of different EEG features and the discriminative local patterns among the features for different emotions is challenging. Existing models ignore the complementarity among the spatial-spectral-temporal features and discriminative local patterns in all features, which limits the classification ability of the models to a certain extent. In this paper, we propose a novel spatial-spectral-temporal based attention 3D dense network, named SST-EmotionNet, for EEG emotion recognition. The main advantage of the SST-EmotionNet is the simultaneous integration of spatial-spectral-temporal features in a unified network framework. Meanwhile, a 3D attention mechanism is designed to adaptively explore discriminative local patterns. Extensive experiments on two real-world datasets demonstrate that the SST-EmotionNet outperforms the state-of-the-art baselines.

SST-EmotionNet: Spatial-Spectral-Temporal based Attention 3D Dense Network for EEG Emotion Recognition

Sleep disorder diagnosis relies on the analysis of polysomnography (PSG) records. As a preliminary step of this examination, sleep stages are systematically determined. In practice, sleep stage classification relies on the visual inspection of 30-second epochs of polysomnography signals. Numerous automatic approaches have been developed to replace this tedious and expensive task. Although these methods demonstrated better performance than human sleep experts on specific datasets, they remain largely unused in sleep clinics. The main reason is that each sleep clinic uses a specific PSG montage that most automatic approaches cannot handle out-of-the-box. Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics. To address these issues, we introduce RobustSleepNet, a deep learning model for automatic sleep stage classification able to handle arbitrary PSG montages. We trained and evaluated this model in a leave-one-out-dataset fashion on a large corpus of 8 heterogeneous sleep staging datasets to make it robust to demographic changes. When evaluated on an unseen dataset, RobustSleepNet reaches 97% of the F1 of a model explicitly trained on this dataset. Hence, RobustSleepNet unlocks the possibility to perform high-quality out-of-the-box automatic sleep staging with any clinical setup. We further show that finetuning RobustSleepNet, using a part of the unseen dataset, increases the F1 by 2% when compared to a model trained specifically for this dataset. Therefore, finetuning might be used to reach a state-of-the-art level of performance on a specific population.

RobustSleepNet: Transfer Learning for Automated Sleep Staging at Scale

Barnett et al. in 2009 proved that Granger causality and transfer en-tropy causality measure are equivalent for time series which have aGaussian distribution. Granger causality test is linear, while trans-fer entropy a non-linear test. Many biological and physical mechanismsshow to have non-Gaussian distributions. In this paper we investigateunder which conditions on probability density distributions of the datacan the equivalence of the two causality measures be extended. In thecomplexity sense ”cheaper” linear Granger test can be applied for de-tection of causality in time series satisfying these conditions. Theseresults have an impact on causality detection in common biological andphysical time series.

Equivalence of Granger Causalityand Transfer Entropy: A Generalization.

Sleep stage classification is essential for sleep assessment and disease diagnosis. However, how to effectively utilize brain spatial features and transition information among sleep stages continues to be challenging. In particular, owing to the limited knowledge of the human brain, predefining a suitable spatial brain connection structure for sleep stage classification remains an open question. In this paper, we propose a novel deep graph neural network, named GraphSleepNet, for automatic sleep stage classification. The main advantage of the GraphSleepNet is to adaptively learn the intrinsic connection among different electroencephalogram (EEG) channels, represented by an adjacency matrix, thereby best serving the spatial-temporal graph convolution network (ST-GCN) for sleep stage classification. Meanwhile, the ST-GCN consists of graph convolutions for extracting spatial features and temporal convolutions for capturing the transition rules among sleep stages. Experiments on the Montreal Archive of Sleep Studies (MASS) dataset demonstrate that the GraphSleepNet outperforms the state-of-the-art baselines.

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GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification

Sleep staging is the basis of sleep medicine for diagnosing psychiatric and neurodegenerative diseases. However, the existing sleep staging methods ignore the fact that multi-modal physiological signals are heterogeneous, and different modalities contribute to sleep staging with distinct impacts on specific stages. Therefore, how to model the heterogeneity of multi-modal signals and adaptively utilize the multi-modal signals for sleep staging remains challenging. Moreover, existing methods suffer from the individual variance of physiological signals. How to generalize the sleep staging model for the variance among subjects is also challenging. To address the above challenges, we propose the multi-modal physiological signals based Squeeze-and-Excitation Network with Domain Adversarial Learning (SEN-DAL) to capture the features of electroencephalogram (EEG) and electrooculogram (EOG) for sleep staging. The SEN-DAL is made up of two independent feature extraction networks for modeling the heterogeneity, a Multi-modal Squeeze-and-Excitation feature fusion module for adaptively utilizing the multi-modal signals, and a Domain Adversarial Learning module to extract subject-invariant sleep feature. Experiments demonstrate that the SEN-DAL is superior to the baseline models on a public sleep staging dataset, reaching an F1-Score of 82.1%, which is similar to human experts. Through the ablation experiments, we found that the proposed mechanisms, including modal-independent feature extraction, adaptive utilization of multi-modal signals, and domain adversarial learning, are effective for sleep staging tasks. The code of SEN-DAL is available at https://github.com/xiyangcai/SEN-DAL.

Multi-Modal Physiological Signals Based Squeeze-and-Excitation Network With Domain Adversarial Learning for Sleep Staging

State-space reconstruction is essential to analyze the dynamics and internal interactions of complex systems. However, it is difficult to estimate high-dimensional conditional mutual information and select the optimal time delays in most existing nonuniform state-space reconstruction methods. Therefore, we propose a nonuniform embedding method framed in information theory for state-space reconstruction. We use a low-dimensional approximation of conditional mutual information criterion for time delay selection, which is effectively solved by the particle swarm optimization algorithm. The obtained embedded vector has relatively strong independence and low redundancy, which better characterizes multivariable complex systems and detects coupling within complex systems. In addition, the proposed nonuniform embedding method exhibits good performance in coupling detection of linear stochastic, nonlinear stochastic, chaotic systems. In the actual application, the importance of small airports that cause delay propagation has been demonstrated by constructing the delay propagation network.

Ziyu Jia

Papers

GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification

Multi-View Spatial-Temporal Graph Convolutional Networks With Domain Generalization for Sleep Stage Classification

SST-EmotionNet: Spatial-Spectral-Temporal based Attention 3D Dense Network for EEG Emotion Recognition

Multi-Modal Physiological Signals Based Squeeze-and-Excitation Network With Domain Adversarial Learning for Sleep Staging

Refined nonuniform embedding for coupling detection in multivariate time series.