Joint ensemble empirical mode decomposition and tunable Q factor wavelet transform based sleep stage classifications

An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment

Autonomous sleep tracking at home has become inevitable in today's fast-paced world. A crucial aspect of addressing sleep-related issues involves accurately classifying sleep stages. This paper introduces a novel approach PSO-XGBoost, combining particle swarm optimisation (PSO) with extreme gradient boosting (XGBoost) to enhance the XGBoost model's performance. Our model achieves improved overall accuracy and faster convergence by leveraging PSO to fine-tune hyperparameters. Our proposed model utilises features extracted from EEG signals, spanning time, frequency, and time-frequency domains. We employed the Pz-oz signal dataset from the sleep-EDF expanded repository for experimentation. Our model achieves impressive metrics through stratified-K-fold validation on ten selected subjects: 95.4% accuracy, 95.4% F1-score, 95.4% precision, and 94.3% recall. The experiment results demonstrate the effectiveness of our technique, showcasing an average accuracy of 95%, outperforming traditional machine learning classifications. The findings revealed that the feature-shifting approach supplements the classification outcome by 3 to 4 per cent. Moreover, our findings suggest that prefrontal EEG derivations are ideal options and could open up exciting possibilities for using wearable EEG devices in sleep monitoring. The ease of obtaining EEG signals with dry electrodes on the forehead enhances the feasibility of this application. Furthermore, the proposed method demonstrates computational efficiency and holds significant value for real-time sleep classification applications.

Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction

BACKGROUND AND OBJECTIVE

Graph neural networks (GNNs) are widely used for automatic sleep staging. However, the majority of GNNs are based on spectral approaches, as far as we know, which heavily depend on the Laplacian eigenbasis determined by the graph structure with a large computing cost.

METHODS

We introduced a non-spectral approach named graph attention networks v2 (GATv2) as the core of our network to extract spatial information (S-GATv2 in our work), which is more flexible and intuitive than the routined spectral method. Meanwhile, to resolve the issue of weak generalization of using traditional feature extraction, the multi-convolutional layers are implemented to automatically extract features. In this work, the proposed spatiotemporal convolution sleep network (ST-GATv2) consists of multi-convolution layers and a GATv2 block. Of note, the graph attention technique to the time domain was applied to construct temporal GATv2 (T-GATv2), which intends to capture the connection between two channels in the adjacent sleep stages. Besides, the modified function is further proposed to capture the hidden changing trend information by the difference in the feature's value of the two adjacent stages.

RESULTS

In our experiment, we used the SS3 datasets in the MASS as our test datasets to compare with other advanced models. Our result reveals our model achieves the highest accuracy at 89.0 %. Besides, the proposed T-GATv2 block and modified function bring an approximate 0.5 % improvement in Kappa and F1-score.

CONCLUSIONS

Our results support the potential of graph attention mechanisms and creative blocks (T-GATv2 and modified function) in sleep classification. We suggest the proposed ST-GATv2 model as an effective tool in sleep staging in either healthy or diseased states.