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Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram

Neuroscience Informatics 2025年第2期5卷 100197-100197页

作者： Ramesh, Janjhyam Venkata Naga Quraishi, Aadam Aoudni, Yassine Mudhafar, Mustafa Nimma, Divya Bansal, Monika Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh 522302 India M.D. Research Intervention Treatment Institute Houston TX United States Computer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS) University of Sfax BP 1173 Sfax 3038 Tunisia Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala 56001 Iraq Department of Anesthesia Techniques and Intensive Care Al-Taff university college Kerbala 56001 Iraq Data Analyst in UMMC United States Department of Computer Science SSD Women's Institute of Technology Bathinda India

Non-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integrating single-channel electrocardiogram (ECG) signals, heart rate variability (HRV) features, and tNIR stimulation. The maximal overlap discrete wavelet transform (MODWT) is applied for multi-resolution analysis of ECG signals, followed by peak information extraction. Based on the first-order deviation of peak positions, multi-dimensional HRV features are extracted. To identify HRV features strongly associated with different sleep stages, we introduce a feature selection method combining the ReliefF algorithm and Gini index. The selected features are then processed using the INFO-ABC Logit Boost method to establish correlations between HRV dynamics and sleep stages. Experimental results on publicly available datasets demonstrate that the proposed model achieves an overall accuracy of 83.67%, a precision of 82.59%, a Kappa coefficient of 77.94%, and an F1-score of 82.97%. Compared with conventional sleep staging methods, our approach enhances sleep quality assessment and facilitates real-time, non-invasive monitoring in home and mobile healthcare settings, leveraging the potential of tNIR-based NIBS for sleep modulation. © 2025 The Author(s)

关键词： ECG Healthcare HRV Non-invasive brain stimulation tNIR

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DOMAIN-INVARIANT REPRESENTATION LEARNING FROM EEG WITH PRIVATE ENCODERS

arXiv

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arXiv 2022年

作者： Bethge, David Hallgarten, Philipp Grosse-Puppendahl, Tobias Kari, Mohamed Mikut, Ralf Schmidt, Albrecht Özdenizci, Ozan Dr. Ing. h.c. F. Porsche AG Stuttgart Germany Ludwig-Maximilians University Munich Germany Karlsruhe Institute of Technology Germany Institute of Theoretical Computer Science Graz University of Technology Austria TU Graz - SAL Dependable Embedded Systems Lab Silicon Austria Labs Austria

Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This becomes a more challenging problem when privacy-preserving representation learning is of interest such as in clinical settings. To that end, we propose a multi-source learning architecture where we extract domain-invariant representations from dataset-specific private encoders. Our model utilizes a maximum-mean-discrepancy (MMD) based domain alignment approach to impose domain-invariance for encoded representations, which outperforms state-of-the-art approaches in EEG-based emotion classification. Furthermore, representations learned in our pipeline preserve domain privacy as dataset-specific private encoding alleviates the need for conventional, centralized EEG-based deep neural network training approaches with shared parameters. Copyright © 2022, The Authors. All rights reserved.

关键词： Electroencephalography

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Design and demonstration of an operating system for executing applications on quantum network nodes

arXiv

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arXiv 2024年

作者： Donne, Carlo Delle Iuliano, Mariagrazia van der Vecht, Bart Ferreira, Guilherme M. Jirovská, Hana van der Steenhoven, Thom J.W. Dahlberg, Axel Skrzypczyk, Matt Fioretto, Dario Teller, Markus Filippov, Pavel Montblanch, Alejandro R.-P. Fischer, Julius van Ommen, H. Benjamin Demetriou, Nicolas Leichtle, Dominik Music, Luka Ollivier, Harold te Raa, Ingmar Kozlowski, Wojciech Taminiau, Tim H. Pawelczak, Przemyslaw Northup, Tracy E. Hanson, Ronald Wehner, Stephanie QuTech Kavli Institute of Nanoscience Delft University of Technology Delft2628 CJ Netherlands Quantum Computer Science Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands Embedded Systems Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands Institut für Experimentalphysik Universität Innsbruck Technikerstraße 25 Innsbruck6020 Austria QAT DIENS Ecole Normale Supérieure PSL University CNRS INRIA. 45 rue d’Ulm Paris75005 France LIP6 CNRS Sorbonne Université 4 Place Jussieu Paris75005 France

The goal of future quantum networks is to enable new internet applications that are impossible to achieve using solely classical communication[1, 2, 3]. Up to now, demonstrations of quantum network applications[4, 5, 6] and functionalities[7, 8, 9, 10, 11, 12] on quantum processors have been performed in ad-hoc software that was specific to the experimental setup, programmed to perform one single task (the application experiment) directly into low-level control devices using expertise in experimental physics. Here, we report on the design and implementation of the first architecture capable of executing quantum network applications on quantum processors in platform-independent high-level software. We demonstrate the architecture’s capability to execute applications in high-level software, by implementing it as a quantum network operating system – QNodeOS – and executing test programs including a delegated computation from a client to a server[13] on two quantum network nodes based on nitrogen-vacancy (NV) centers in diamond[14, 15]. We show how our architecture allows us to maximize the use of quantum network hardware, by multitasking different applications on a quantum network for the first time. Our architecture can be used to execute programs on any quantum processor platform corresponding to our system model, which we illustrate by demonstrating an additional driver for QNodeOS for a trapped-ion quantum network node based on a single 40Ca+atom[16]. Our architecture lays the groundwork for computer science research in the domain of quantum network programming, and paves the way for the development of software that can bring quantum network technology to society. Copyright © 2024, The Authors. All rights reserved.

关键词： Trapped ions

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Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

arXiv

引用

arXiv 2021年

作者： Díaz Ferreyra, Nicolás E. Hecking, Tobias Aïmeur, Esma Heisel, Maritta Hoppe, H. Ulrich University of Duisburg-Essen Department of Computer Science and Applied Cognitive Science Duisburg47057 Germany Institute for Software Technology Intelligent and Distributed Systems German Aerospace Center Köln51147 Germany Department of Computer Science and Operations Research University of Montréal Canada

Access-Control Lists (ACLs) (a.k.a. "friend lists") are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as "untrusted") to the emerging communities inside the user’s ego-network. However, unlike users’ access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination. Copyright © 2021, The Authors. All rights reserved.

关键词： Access control

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Polyjuice: High-performance transactions via learned concurrency control

arXiv

引用

arXiv 2021年

作者： Wang, Jiachen Ding, Ding Wang, Huan Christensen, Conrad Wang, Zhaoguo Chen, Haibo Li, Jinyang Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai AI Laboratory China Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Department of Computer Science New York University

Concurrency control algorithms are key determinants of the performance of in-memory databases. Existing algorithms are designed to work well for certain workloads. For example, optimistic concurrency control (OCC) is better than twophase- locking (2PL) under low contention, while the converse is true under high contention. To adapt to different workloads, prior works mix or switch between a few known algorithms using manual insights or simple heuristics. We propose a learning-based framework that instead explicitly optimizes concurrency control via offline training to maximize performance. Instead of choosing among a small number of known algorithms, our approach searches in a "policy space" of fine-grained actions, resulting in novel algorithms that can outperform existing algorithms by specializing to a given workload. We build Polyjuice based on our learning framework and evaluate it against several existing algorithms. Under different configurations of TPC-C and TPC-E, Polyjuice can achieve throughput numbers higher than the best of existing algorithms by 15% to 56%. Copyright © 2021, The Authors. All rights reserved.

关键词： Concurrency control

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Cognitive Twins for Predictive Maintenance and Security in IoT Software systems

Cognitive Twins for Predictive Maintenance and Security in I...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： B. Venkata Swamy Pareshwar Prasad Barmola Senthil Thangavel S. Kaliappan Hardik Patel Girish Abhyankar Department of Chemistry B V Raju Institute of Technology Medak India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Distributed Systems Cloud Solutions & Machine Learning Expert Paypal Inc California USA Division of Research and Development Lovely Professional University Phagwara India Department of Electronics & Communication Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The increasing complexity and interconnectedness of Internet of Things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. This paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. The framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. The framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%.

关键词： Biological system modeling Decision making Reinforcement learning Software systems Real-time systems Natural language processing Maintenance Internet of Things Security Predictive maintenance

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Label Distribution Learning on Imbalanced Data 5

Label Distribution Learning on Imbalanced Data

引用

5th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020

作者： Zhou, Guangyue Li, Kewen Xie, Peng Zhai, Jiannan Zhu, Jianbing China University of Petroleum College of Computer Science and Technology Qingdao China Institute for Sensing and Embedded Network Systems Engineering Florida Atlantic University Boca Raton United States Geophysical Research Institute Shengli Oilfield Company SINOPEC Dongying China

ISBN: (纸本)9781728160245

Although multi-label learning can handle problems involving label ambiguity, it is not suitable for applications where the overall distribution of the importance of the labels matters. Imbalanced data also poses challenges to classification applications. In this paper, we propose a novel learning paradigm named Label Distribution Learning on Imbalanced Data (LDLID) for such applications, which is based on the improved Kullback-Leibler Divergence and uses the adaptive step size to update the gradient. The experimental results show that LDLID achieves better performance in comparison with four widely-used algorithms on four imbalanced datasets. © 2020 IEEE.

关键词： Classification (of information)

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Gemeinsame Digitalisierung durch die Entwicklung von IT-Governance-Strukturen auf Landesebene

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Wirtschaftsinformatik & Management 2022年第2期14卷 127-139页

作者： von der Heyde, Markus Gerl, Armin Groß, Rainer Klapper, Frank Knaden, Andreas Meister, Vera G. Rehm, Simone dH-IT Weimar Deutschland Department of Distributed Information Systems University of Passau Passau Deutschland Computer Science Department Nuremberg Institute of Technology Nürnberg Deutschland Universität Bielefeld Bielefeld Deutschland Universität Osnabrück Osnabrück Deutschland Technische Hochschule Brandenburg Brandenburg a. d. H. Brandenburg Deutschland Universität Stuttgart Stuttgart Deutschland

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An embedded CNN Implementation for On-Device ECG Analysis

An Embedded CNN Implementation for On-Device ECG Analysis

引用

IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Alwyn Burger Chao Qian Gregor Schiele Domenik Helms Embedded Systems University of Duisburg-Essen Duisburg Germany OFFIS Institute for Computer Science Oldenburg Germany

ISBN: (数字)9781728147161

ISBN: (纸本)9781728147178

IoT systems that employ AI and neural networks for processing sensor data are usually dependent on an active connection to the cloud, or restricted to simplified models and techniques. By employing a highly optimised Convolutional Neural Network that can be executed directly on our heterogeneous IoT device, we can address both these problems without sacrificing accuracy. Additionally, leveraging the cloud when unknown or uncertain samples are seen allows us to continuously improve the model. Capable of running a “normal” model trained using frameworks such as TensorFlow, we show that our system can achieve accuracy of up to 97% in medical applications such as ECGs. This is done using our device that uses less than 200 mW but can locally process more than 300 heart beats per second.

关键词： Field programmable gate arrays Cloud computing Heart Monitoring Medical services Integrated circuit modeling Training

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