Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

A novel deep learning model based on transformer and cross modality attention for classification of sleep stages

The classification of sleep stages is crucial for gaining insights into an individual's sleep patterns and identifying potential health issues. Employing several important physiological channels in different views, each providing a distinct perspective on sleep patterns, can have a great impact on the efficiency of the classification models. In the context of neural networks and deep learning models, transformers are very effective, especially when dealing with time series data, and have shown remarkable compatibility with sequential data analysis as physiological channels. On the other hand, cross-modality attention by integrating information from multiple views of the data enables to capture relationships among different modalities, allowing models to selectively focus on relevant information from each modality. In this paper, we introduce a novel deep-learning model based on transformer encoder-decoder and cross-modal attention for sleep stage classification. The proposed model processes information from various physiological channels with different modalities using the Sleep Heart Health Study Dataset (SHHS) data and leverages transformer encoders for feature extraction and cross-modal attention for effective integration to feed into the transformer decoder. The combination of these elements increased the accuracy of the model up to 91.33% in classifying five classes of sleep stages. Empirical evaluations demonstrated the model's superior performance compared to standalone approaches and other state-of-the-art techniques, showcasing the potential of combining transformer and cross-modal attention for improved sleep stage classification.

Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice

Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.

An artificial intelligence based news feature mining system based on the Internet of Things and multi-sensor fusion

The application of Internet of Things (IoT) technology in news media communication has significantly enhanced the effectiveness and coverage of news data releases. However, as the scale of news data continues to grow, traditional IoT approaches face challenges such as slow data processing speed and low mining efficiency. To address these issues, a novel news feature mining system combining IoT and Artificial Intelligence (AI) has been developed. The hardware components of the system include a data collector, a data analyzer, a central controller, and sensors. The GJ-HD data collector is utilized to gather news data. Multiple network interfaces are designed at the device terminal to ensure data extraction from the internal disk in case of device failure. The central controller integrates the MP/MC and DCNF interfaces for seamless information interconnection. In the software aspect of the system, the network transmission protocol of the AI algorithm is embedded, and a communication feature model is constructed. This enables fast and accurate mining of news data communication features. Experimental results demonstrate that the system achieves a mining accuracy of over 98%, enabling efficient processing of news data. Overall, the proposed IoT and AI-based news feature mining system overcomes the limitations of traditional approaches, allowing for efficient and accurate processing of news data in a rapidly expanding digital landscape.