Sleep apnea (SA) is a common sleep-related breathing disorder that tends to induce a series of complications, such as pediatric intracranial hypertension, psoriasis, and even sudden death. Therefore, early diagnosis and treatment can effectively prevent malignant complications SA incurs. Portable monitoring (PM) is a widely used tool for people to monitor their sleep conditions outside of hospitals. In this study, we focus on SA detection based on single-lead electrocardiogram (ECG) signals which are easily collected by PM. We propose a bottleneck attention based fusion network named BAFNet, which mainly includes five parts of RRI (R-R intervals) stream network, RPA (R-peak amplitudes) stream network, global query generation, feature fusion, and classifier. To learn the feature representation of RRI/RPA segments, fully convolutional networks (FCN) with cross-learning are proposed. Meanwhile, to control the information flow between RRI and RPA networks, a global query generation with bottleneck attention is proposed. To further improve the SA detection performance, a hard sample scheme with k-means clustering is employed. Experiment results show that BAFNet can achieve competitive results, which are superior to the state-of-the-art SA detection methods. It means that BAFNet has great potential to be applied in the home sleep apnea test (HSAT) for sleep condition monitoring. The source code is released at https://github.com/Bettycxh/Bottleneck-Attention-Based-Fusion-Network-for-Sleep-Apnea-Detection.

Sleep quality is heavily influenced by sleep posture, with research indicating that a supine posture can worsen obstructive sleep apnea (OSA) while lateral postures promote better sleep. For patients confined to beds, regular changes in posture are crucial to prevent the development of ulcers and bedsores. This study presents a novel sparse sensor-based spatiotemporal convolutional neural network (S³CNN) for detecting sleep posture. This S³CNN holistically incorporates a pair of spatial convolution neural networks to capture cardiorespiratory activity maps and a pair of temporal convolution neural networks to capture the heart rate and respiratory rate. Sleep data were collected in actual sleep conditions from 22 subjects using a sparse sensor array. The S³CNN was then trained to capture the spatial pressure distribution from the cardiorespiratory activity and temporal cardiopulmonary variability from the heart and respiratory data. Its performance was evaluated using three rounds of 10 fold cross-validation on the 8583 data samples collected from the subjects. The results yielded 91.96% recall, 92.65% precision, and 93.02% accuracy, which are comparable to the state-of-the-art methods that use significantly more sensors for marginally enhanced accuracy. Hence, the proposed S³CNN shows promise for sleep posture monitoring using sparse sensors, demonstrating potential for a more cost-effective approach.

Fast pace of life has made the incidence rate and mortality rate caused by cardiovascular diseases increase. It is of great significance to detect and treat cardiovascular problems as early as possible. Due to inconvenience and uncomfortable reasons, wearable ECG monitoring devices are unsuitable to be applied in daily healthcare, especially during sleep at night. It is necessary to provide non-contact heart health monitoring method for those at risk of heart disease. In this paper, we propose a multi-instance learning based algorithm to extract cardiac characteristics from Ballistocardiogramm (BCG) signals collected by piezoelectric ceramic sensors. Time and frequency domain heart rate variability characteristics are obtained and compared with that extracted from ECG (Electrocardiogram) signals. Results show that the proposed method has the advantages of high detection accuracy comparing with ECG method. Therefore, non-contact characteristic make BCG monitoring convenient to be used in daily healthcare for real-time alarm of heart disease, so as to achieve the aim of in-time risk detection and early treatment.

Amidst the escalating need for stable power supplies and high-quality communication services in remote regions globally, due to challenges associated with deploying a conventional power communication infrastructure and its susceptibility to natural disasters, LEO satellite networks present a promising solution for broad geographical coverage and the provision of stable and high-speed communication services in remote regions. Given the necessity for frequent handovers to maintain service continuity, due to the high mobility of LEO satellites, a primary technical challenge confronting LEO satellite networks lies in efficiently managing the handover process between satellites, to guarantee the continuity and quality of communication services, particularly for power services. Thus, there is a critical need to explore satellite handover optimization algorithms. This paper presents a handover optimization scheme that integrates deep reinforcement learning (DRL) and graph neural networks (GNN) to dynamically optimize the satellite handover process and adapt to the time-varying satellite network environment. DRL models can effectively detect changes in the topology of satellite handover graphs across different time periods by leveraging the powerful representational capabilities of GNNs to make optimal handover decisions. Simulation experiments confirm that the handover strategy based on the fusion of message-passing neural network and deep Q-network algorithm (MPNN-DQN) outperforms traditional handover mechanisms and DRL-based strategies in reducing handover frequency, lowering communication latency, and achieving network load balancing. Integrating DRL and GNN into the satellite handover mechanism enhances the communication continuity and reliability of power systems in remote areas, while also offering a new direction for the design and optimization of future power system communication networks. This research contributes to the advancement of sophisticated satellite communication architectures that facilitate high-speed and reliable internet access in remote regions worldwide.

Quality of experience (QoE) is a vital metric that indicates how well the wireless network provides transmission services to users, while quality of service (QoS) help better configure the network parameters for higher performance. The evaluation time of QoE is usually several orders of magnitude larger than that of QoS, because QoE is the perception of users over a period of time, but QoS can be collected every millisecond. Therefore, the implementation of QoE/QoS mapping model can help us obtain QoE by collecting the QoS measurements, and perform QoE-based network configurations with smaller time granularity. Many studies are made to obtain the QoS to QoE mapping, including the use of machine learning (ML) methods. However, traditional ML-based regression methods for QoE/QoS mapping face the challenge of high regression error and catastrophic forgetting in dealing with continuously arriving data. In this paper, we propose a novel QoE model based on continual deep learning in wireless network. This model is formed with two deep neural networks (DNNs) concatenated. The first DNN classifies data into different subsets, which are then fed into the second DNN for regression. The second DNN dynamically form the corresponding subnets, each with nodes and connections adaptively selected in each new time period with new arriving data. We solve the catastrophic forgetting problem with the use of node splitting and hidden state augmentation. Our proposed learning framework greatly reduces the regression error to as low as 0.9314%. The experimental results demonstrate that our proposed model reduces the root mean square error (RMSE) by $21 \sim 86$ times compared with several existing approaches, specially, the testing error of our proposed model is more than 80 times lower than that of traditional DNN. Compared with other DNN-based cascade models, our proposed method provides good performance in both training time and RMSE.

With the development of artificial intelligence, the application of intelligent medical devices is gradually becoming popular. Intelligent medical devices can access many types of medical data through sensors and analyze these data to monitor, diagnose and treat patients. Intelligent medical device communication is an important research area that is of great significance to the development of the medical field. Intelligent medical device communication technology allows medical devices to communicate and share information with each other, thereby improving the efficiency and accuracy of medical devices. However, due to the heterogeneous nature of medical data and the diversity of data sources, it has become a challenge to integrate and analyze these data effectively. In order to achieve data interaction and sharing between different devices, it is necessary to fuse heterogeneous data from multiple sources to improve data integrity and usability. In this paper, we propose a self-supervised multi-source heterogeneous data fusion method using encode and decode attention for intelligent medical device communication analysis. The method improves data fusion by automatically learning the correlation between data through self-supervised learning and using encoding and decoding attention to strengthen the correlation between data. The experimental results show that the proposed method can effectively improve the accuracy and efficiency of medical data fusion and provide a strong support for communication between intelligent medical devices. This research integrates different types of medical information to provide better decision support for physicians and to promote medical technology advancement and health management.