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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

Neural Computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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A Novel Breast Cancer Detection and Classification Using Enhanced ANN Classification 5

A Novel Breast Cancer Detection and Classification Using Enh...

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Mathiyalagan, P. Kiruthiga, G. Rasmi, A. Vadivel, M. Mahalakshmi, K.V. Poornappriya, T.S. Sri Ramakrishna Engineering College Department of CSE Coimbatore India Karpagam College of Engineering Dept of Artificial Intelligence and Data Science Coimbatore India RR Institute of Technology Information Science and Engineering Bangalore India Excel Engineering College Department of Information Technology TamilNadu Namakkal India B v Raju Institute of Technology CSE Telangana 502313 India Aspire Systems Pvt Ltd Data and Analytics Karnataka Bengaluru560037 India

ISBN: (纸本)9798331518097

The second most prevalent and leading cause of cancer death is breast cancer, which is the most common malignancy among women. Because the causes of breast cancer are not fully known, there is currently no reliable technique to prevent it. In recent years, breast cancer has become a widespread condition among women. Over the past several years, the issue of breast tumour categorization has become a significant issue. This article offers logical reasoning for the order in which to identify malignant lesions, a significant development in the mammography diagnosis of breast cancer. As a result, we concentrate on studying and analysing cancer imaging, with a particular emphasis on breast cancer. Mammography produces and processes pictures that are used in breast cancer detection. Sensitivity, specificity, and classification accuracy are the metrics used to assess the experimental findings. Results of an improved ANN classifier for differentiating between normal and abnormal cases in pictures from applied advanced mammography. © 2024 IEEE.

关键词： Lung cancer

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Integral Reinforcement Reciprocal Zhang Neural Net for Time-Varying Linear Equations

Integral Reinforcement Reciprocal Zhang Neural Net for Time-...

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2024 International Conference on New Trends in Computational Intelligence, NTCI 2024

作者： Wang, Changyuan Chen, Long Zhang, Yunong Li, Shuai University of Macau Faculty of Science and Technology China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland Technical Research Center of Finland Oulu Finland

ISBN: (纸本)9798331517021

Solving time-varying linear equations is a com-mon challenge encountered in engineering and science. The development of neural dynamic systems has led to the creation of various feedback neural nets that effectively solve continuous time-varying linear equations. In this context, reciprocal Zhang neural net (RZNN) was developed, which is focused by this work, providing an explicit inverse-free continuous model for solving time-varying linear equations. However, the continuous model often struggle with noise interference, e.g., Gaussian noise, during the solving process. This paper enhances RZNN model with an integral reinforcement term to improve robustness of the explicit continuous models with noisy conditions. Furthermore, we optimize the RZNN solving process, making it easier for implementation in circuit systems and numerical simulations. © 2024 IEEE.

关键词： Continuous time systems

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CloudyPages: A Secure and Scalable Serverless Web-Based Blogging Application 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Prakash, Vijay Jaiswal, Shrestha Purohit, Saumya Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Information Systems Faculty of Information and Communication Technology University of Malta MsidaMSD 2080 Malta

ISBN: (数字)9789819995318

ISBN: (纸本)9789819995301

The CloudyPages app is a precise and trustworthy platform for bloggers to create and effectively manage blogs. The CloudyPages application provides bloggers a platform for creating and managing their blogs. The application can achieve remarkable accuracy, precision, and review rates by utilizing AWS services, assuring minimal errors and enhancing the discoverability and accessibility of bloggers’ content. Collectively, these metrics contribute to an optimized user experience. AWS WAF (Amazon Web Services Web Application Firewall) provides robust protection against common web application vulnerabilities such as SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF) attacks. By implementing this security layer, the application effectively protects the data of bloggers and readers from potential attacks. The AWS Web Application Firewall (WAF) protects against known attack patterns and continuously monitors and detects harmful activities to prevent unauthorized access. The application's serverless architecture lets bloggers focus on content creation and administration instead of server maintenance. The application's affordable price makes it an appealing, adaptable, and inclusive solution. The application's serverless design and AWS WAF and AWS Open make it flexible, affordable, and accessible for bloggers of all sizes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blogs

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Comparative Analysis of Ensemble Learning Techniques for Brain Tumor Classification

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Informatica (Slovenia) 2024年第20期48卷 41-50页

作者： Sharif, Rehman Azam, Muhammad Ali, Arshad Hashmi, Muhammad Usman Uzair, Muhammad Department of Mathematics & Physics The Superior University Lahore54000 Pakistan Department of Computer Science & Information Technology The Superior University Lahore54000 Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Al Madinah Al Munawarah Saudi Arabia Faculty of Engineering Islamic University of Madinah Al Madinah Al Munawarah Saudi Arabia

For the evaluation of our work, we used a brain tumor MRI dataset obtained from Kaggle for the experimental analysis. Our study investigates the performance of ensemble learning techniques for brain tumor classification. This study focuses on comparing the efficacy of homogenous ensemble classifiers, exemplified by the Random Forest (RF) algorithm, against heterogeneous ensemble classifiers like Voting and Stacking, this study embarks on a thorough evaluation journey. Our evaluation is not limited to accuracy measures only;instead, it surrounds recall, ROC AUC, precision, and F1-score, for better assessment of classifiers’ performance. Our findings demonstrate that the Random Forest classifier achieved an accuracy of 99%, an F1 score of 0.99%, and AUC of 0.99%, outperforming other ensemble techniques like Voting and Stacking classifiers. Building upon an observed assessment performed on an appropriately selected brain tumor dataset, we provide solid empirical support demonstrating that RF not only performs better than base classifiers but also outperforms the heterogeneous ensemble methods in terms of many different performance measures. Furthermore, we discuss the specific reason that makes RF outperform other algorithms in this dataset and discuss the robustness and flexibility of this method. By unscrambling these insights, this paper addresses to fill gaps in the existing knowledge regarding the ensemble learning techniques in medical imaging, particularly for brain tumor classification diagnostics. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Random forests

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Non-thermal obliteration of critically ranked carbapenem-resistant Acinetobacter baumannii and its resistance gene in a batch atmospheric plasma reactor

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Environmental science and Pollution Research 2024年第37期31卷 49811-49822页

作者： Mosaka, Thabang B. M. Unuofin, John O. Daramola, Michael O. Tizaoui, Chedly Iwarere, Samuel A. Sustainable Energy and Environment Research Group Department of Chemical Engineering Faculty of Engineering Built Environment and Information Technology University of Pretoria Pretoria Hatfield0002 South Africa Water and Resources Recovery Research Lab Department of Chemical Engineering Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom

Wastewater treatment plants (WWTPs) have been implicated as direct key reservoir of both antibiotic-resistant bacteria (ARB) and antibiotic-resistant genes (ARGs) associated with human infection, as high concentrations of ARBs and ARGs have been detected in recycled hospital wastewater. Among the ARBs, the carbapenem-resistant Acinetobacter baumannii has been ranked as priority 1 (critical) pathogen by the World Health Organization (WHO), due to its overwhelming burden on public health. Therefore, this study is aimed at investigating non-thermal plasma (NTP) technology as an alternative disinfection step to inactivate this bacterium and its ARGs. Culture-based method and PCR were employed in confirming the carbapenem resistance gene blaNDM-1 in A. baumannii (BAA 1605). Suspension of carbapenem-resistant A. baumannii (24 h culture) was prepared from the confirmed isolate and subjected to plasma treatment at varying time intervals (3 min, 6 min, 9 min, 12 min, and 15 min) in triplicates. The plasma-treated samples were evaluated for re-growth and the presence of the resistance gene. The treatment resulted in a 1.13 log reduction after 3 min and the highest log reduction of ≥ 8 after 15 min, and the results also showed that NTP was able to inactivate the blaNDM-1 gene. The log reduction and gel image results suggest that plasma disinfection has a great potential to be an efficient tertiary treatment step for WWTPs. © The Author(s) 2024.

关键词： Bacteria

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Differentiable optimization of multiple freeform lenses for high-performance tilted illumination

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Optics Letters 2025年第11期50卷 3505-3508页

作者： Tang, Haisong Feng, Zexin Luo, Yi Mao, Xianglong Beijing Engineering Research Center of Mixed Reality and Advanced Display School of Optics and Photonics Beijing Institute of Technology Beijing100081 China MOE Key Laboratory of Optoelectronic Imaging Technology and Systems Beijing Institute of Technology Beijing100081 China Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China The New Technology Laboratory of Space Photon Information Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Shaanxi Xi’an710119 China

We present an optimization framework for designing multiple freeform lenses to achieve high-performance oblique illumination. The optimization process involves two steps: optimizing the irradiance distribution on an intermediate plane and fine-tuning the surface parameters to achieve the desired tilted target irradiance distribution. The framework could be used to design freeform illumination systems with an arbitrary number of lenses without significantly increasing the computational resource requirements. Leveraging differentiable ray tracing (DRT), we design a compact system with double freeform lenses, realizing a high tilt angle (TA) of 78.86◦ and an average spacing-to-height ratio (SHR) of 3.77. It holds significant potential for applications in the design of vehicle headlights, industrial vision inspection, and decoration lighting. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Illumination design Lens design Optical systems Ray tracing systems design Total internal reflection

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Mode-division Multiplexing Signals through Both Wired and Wireless Optical Channels

Mode-division Multiplexing Signals through Both Wired and Wi...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Zong, Shiyu Wen, Feng Zhao, Tianfeng Qiu, Kun University of Electronic Science and Technology of China Key Lab of Optical Fiber Sensing and Communications Ministry of Education School of Information and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350375909

Radio-over-fiber (RoF) technology allows RF signals to be transmitted through optical fiber, greatly improving the anti-interference and long-distance transmission capabilities of RF signals. The combination of mode-division multiplexing (MDM) and analog radio over fiber (A-RoF) technologies could provide the higher capacity for the forward transmission in the scenarios of 6G and beyond, as well as the physical layer for the massive multi-input multi-output (M-MIMO), beamforming and other technologies. However, the crosstalk nature happened between optical modes also leads to the extra distortion on the RF signals when they are propagating through the MDM channels. In this paper, we propose an optical mode-related single-sideband (M-SSB) modulation scheme, which could reduce the impact of crosstalk between modes and avoiding the problem of periodic power fading in the dispersive fiber channel. Moreover, we also perform the joint compensation of the optical mode-related crosstalk in the few-mode fiber (FMF) channel and the Doppler shift effect in the wireless channel. According to the simulation investigation, the signal-to-noise ratio (SNR) of compensated signals could be up to 26.21dB, confirming the availability of the proposed MDM scheme through both wired and wireless channels. © 2024 IEEE.

关键词： Signal to noise ratio

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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Low-disturbance Nonlinear Control of TBM Cutterhead Speed in Coal Mine Tunneling 50

Low-disturbance Nonlinear Control of TBM Cutterhead Speed in...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Liu, Nannan Wang, Tong Wang, Jianmin Fan, Yingda School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Department of Computer Science University of Pittsburgh Pittsburgh United States

ISBN: (纸本)9781665464543

To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking performance with minimal overshoot, we propose a direct adaptive robust control method for the cutterhead rotation speed hydraulic system based on inversion design. This method considers the strong disturbances such as loads and motion affecting the cutterhead hydraulic drive system, as well as the uncertainties in the cutterhead model. We establish the nonlinear model of the cutterhead hydraulic drive system and employ virtual control to reduce the model order. Using Lyapunov functions, we ensure the stability of the entire system and derive the control law for the cutterhead rotation speed controller, along with parameter-adaptive laws acting as parameter estimators. We validate the effectiveness of the proposed control strategy through joint simulation using AMESim and Simulink. The results show that the designed cutterhead rotation speed controller achieves high tracking accuracy and good adaptability. © 2024 IEEE.

关键词： Hydraulic drives

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