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Development of an Intelligent System for Detection of Chronic Stress from Biological signal processing 9th

Development of an Intelligent System for Detection of Chroni...

9th Latin American Congress on Biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical Engineering, CBEB 2022

作者： Junqueira, Luis Pina, Marta Biomedical Engineering Laboratory of Signal Processing and Medical Image SP São Paulo Brazil Federal Institute of Education Science and Technology IFSP SP São Paulo Brazil

ISBN: (纸本)9783031494031

Our objective in this work was to develop an intelligent system able to perform an automated detection of chronic stress, based on biological signals processing and features extraction, confronted with Hans Selye clinical model of stress phase’s assessment. We recorded biological signals of blood pressure, skin surface temperature, galvanic skin resistance and heart rate of 120 health adult volunteers. Also the heart rate variability parameters were extracted for the time-domain and frequency-domain analysis. A Multi-Layer Perceptron Artificial Neural Network with a supervised learning approach was applied to generate the mathematical model. The system classification produced a precision index of 89% for identifying the stressed class, when applying the skin temperature and the heart rate variability parameters as input of the neural network, providing a satisfactory initial performance in the discrimination of stressed individuals. A worth training set with more examples mighty potentially increases the precision for identifying the stressed individuals. Despite study limitations, we consider that the use of intelligent systems to classify biological signals and identify the long term stress presence in human organism could contribute to a more objective analysis of such physiologic characteristics associated to the chronic stress and its implications in human health and performance. Future works should include different settle of physiological parameters and different machine learning techniques for analysis and classification of signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent systems

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A Novel Construction of Two-Dimensional Z-Complementary Array Code Sets Based on Extended Boolean Functions

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7821-7834页

作者： Tian, Liying Song, Xiaoshi Li, Yubo Zhang, Haijun Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Two-dimensional (2-D) array sets with good 2-D correlation properties have received considerable attention in wireless communication systems. This paper focuses on 2-D Z-complementary array code sets (ZCACSs), which have ideal 2-D correlation properties within a rectangular zone around the origin. By developing a theory linking 2-D extended Boolean functions with 2-D arrays, we establish an efficient construction of 2-D ZCACSs. In contrast to previous work, the proposed construction is capable of producing 2-D ZCACSs with new flexible parameters. Constructions of three-dimensional (3-D) and higher-dimensional ZCACSs are further investigated. We remark that 2-D and higher-dimensional complete complementary codes (CCCs) can also be generated from the proposed constructions. © 1967-2012 IEEE.

关键词： Codes (symbols)

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Asymptotically Optimal Sequence Sets With Low/Zero Ambiguity Zone Properties

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IEEE Transactions on Information Theory 2025年第6期71卷 4785-4796页

作者： Tian, Liying Song, Xiaoshi Liu, Zilong Li, Yubo Northeastern University School of Computer Science and Engineering Shenyang110819 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110189 China University of Essex School of Computer Science and Electronics Engineering ColchesterCO4 3SQ United Kingdom Yanshan University School of Information Science and Engineering Qinhuangdao066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao066004 China

Sequences with low/zero ambiguity zone (LAZ/ZAZ) properties are useful in modern communication and radar systems operating over mobile environments. This paper first presents a new family of ZAZ sequence sets motivated by the "modulating" zero correlation zone (ZCZ) sequences which were first proposed by Popovic and Mauritz. We then introduce a second family of ZAZ sequence sets with comb-like spectrum, whereby the local Doppler resilience is guaranteed by their inherent spectral nulls in the frequency domain. Finally, LAZ sequence sets are obtained by exploiting their connection with a novel class of mapping functions. These proposed unimodular ZAZ and LAZ sequence sets are cyclically distinct and asymptotically optimal with respect to the existing theoretical bounds on ambiguity functions. © 1963-2012 IEEE.

关键词： Frequency domain analysis

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Bacterial community in response to packaging conditions in farmed gilthead seabream

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Aquaculture and Fisheries 2023年第4期8卷 410-421页

作者： Apollon Thomas Spyros J.Konteles Sotiris Ouzounis Spyros Papatheodorou Aliki Tsakni Dimitra Houhoula Theofania Tsironi Department of Food Science and Technology Faculty of Food SciencesUniversity of West AtticaAthens11521Greece Medical Image and Signal Processing Laboratory Department of Biomedical EngineeringUniversity of West AtticaAthens11521Greece Laboratory of Food Process Engineering Department of Food Science and Human NutritionAgricultural University of AthensAthens11855Greece

The objective of this study was to evaluate the effect of modified atmosphere packaging(MAP)on fish skin,gills and intestines bacterial *** gilthead seabream was packed aerobically or under modified atmospheres(60%CO_(2),30%N_(2),10%O_(2))and stored isothermally at 0℃.Next Generation Sequencing(NGS)analysis was applied for the characterization of fish microbiome on fish skin,gills,and intestines initially(time of packaging,1 day after harvesting)and after 10 days of isothermal storage at 0℃.NGS results indicated statistically significant differences in families’richness and diversity in the tested fish tissues between aerobic and MAP packaging during storage at 0℃.The most persistent bacteria were Proteobacteria for both packaging *** fish skin microbiota,the initially prevailing families were Comamonadaceae,Enterobacteriaceae and Moraxellaceae while in the intestines Comamonadaceae,Anaplasmataceae,Bacillaceae and Enterobacteriaceae were the dominant *** the end of storage period,the fish microbiota was dominated by psychotropic and psychrophilic families(Pseudoalteromonadaceae,Psychromonadaceae,and Shewanellaceae),while families such as Comamonadaceae were persistent under MAP *** 8 days of fish storage at 0℃,МАРsamples exhibited higher sensory scorings than the respective aerobically stored fish,indicating better retention of fish freshness and fish quality attributes under *** on the results of the study,MAP modified significantly the microbiological status and extended the shelf life of *** was a powerful tool that provided a more complete assessment compared to a culture-based analysis.

关键词： Fish Preservation Next generation sequencing Modified atmosphere packaging Microbiome

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Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning

arXiv

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arXiv 2024年

作者： He, Fan He, Mingzhen Shi, Lei Huang, Xiaolin Suykens, Johan A.K. STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory for Contemporary Applied Mathematics School of Mathematical Sciences Fudan University Shanghai200433 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China MOE Key Laboratory of System Control and Information Processing Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Ridgeless regression has garnered attention among researchers, particularly in light of the "Benign Overfitting" phenomenon, where models interpolating noisy samples demonstrate robust generalization. However, kernel ridgeless regression does not always perform well due to the lack of flexibility. This paper enhances kernel ridgeless regression with Locally-Adaptive-Bandwidths (LAB) RBF kernels, incorporating kernel learning techniques to improve performance in both experiments and theory. For the first time, we demonstrate that functions learned from LAB RBF kernels belong to an integral space of Reproducible Kernel Hilbert Spaces (RKHSs). Despite the absence of explicit regularization in the proposed model, its optimization is equivalent to solving an 0-regularized problem in the integral space of RKHSs, elucidating the origin of its generalization ability. Taking an approximation analysis viewpoint, we introduce an lq-norm analysis technique (with 0 © 2024, CC BY.

关键词： Vector spaces

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The potential of convolutional neural network diagnosing prostate cancer

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Research on Biomedical Engineering 2021年第1期37卷 25-31页

作者： Kudo, Maíra Suzuka de Souza, Vinicius Meneguette Gomes de Souza Amaral, Gabriel de Souza Melo, Petrônio Augusto Estivallet, Carmen Liane Neubarth Santos, Eric Rocha de Amorim, Henrique Alves Moraes, Matheus Cardoso Leite, Katia Ramos Moreira Laboratory of Image and Signal Processing of the Institute of Science and Technology Federal University of São Paulo – UNIFESP 330 Talim St. Room 108 - Jardim Aeroporto Sao Jose dos CamposSP12231-280 Brazil Laboratory of Medical Investigations Number 55 Sao Paulo University Medical School – FMUSP Dr Arnaldo Avenue 455 Room 2145 – Cerqueira Cesar Sao PauloSP01246-903 Brazil

Introduction: There were more than 1,276,106 new cases of prostate cancer (PC) in 2018 worldwide (GLOBOCAN). Early and precise diagnosis leads to cure chances up to 90%. Digital rectal examination and PSA serum levels are employed for prostate cancer screening. If both exams are suspicious for cancer, the patient will be submitted to a prostate biopsy. Histological diagnosis and grading are crucial to the proper manage of the patients and are not always easy to evaluate, demanding experience of pathologists. To test the possibility to adopt artificial intelligent to diagnose PC, we studied a set of prostate biopsy sample images that were input to a specifically constructed convolutional neural network. Purpose: Evaluate the potential of the convolutional neural network for the classification of cancer and non-cancer patches extracted from prostate biopsy images. Methods: Thirty-two prostate cancer biopsy images were obtained and reviewed by a single uropathologist and then transformed into 2594 fragments to feed the CNN. The methodology has been divided into clinical approaches—to extract patches—and computational approaches—the CNN implementation. Results: The k-fold three-way cross-validation method was used, resulting in a 98.3% output accuracy in distinguishing cancer from non-cancer. Conclusion: The presented method proved to be robust and trustworthy comparing with an expert pathologist report. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Convolutional neural networks

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Robust multi-echo GRE phase processing using a unity rank enforced complex exponential model

arXiv

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arXiv 2021年

作者： Paul, Joseph Suresh Madhusoodhanan, Sreekanth Medical Image Computing and Signal Processing Laboratory Indian Institute of Information Technology and Management- Kerala Trivandrum695581 India

Purpose: Develop a processing scheme for Gradient Echo (GRE) phase to enable restoration of susceptibility-related (SuR) features in regions affected by imperfect phase unwrapping, background suppression and low signal-to-noise ratio (SNR) due to phase dispersion. Theory and Methods: The predictable components sampled across the echo dimension in a multi-echo GRE sequence are recovered by rank minimizing a Hankel matrix formed using the complex exponential of the background suppressed phase. To estimate the single frequency component that relates to the susceptibility induced field, it is required to maintain consistency with the measured phase after background suppression, penalized by a unity rank approximation (URA) prior. This is formulated as an optimization problem, implemented using the alternating direction method of multiplier (ADMM). Results: With in vivo multi-echo GRE data, the magnitude susceptibility weighted image (SWI) reconstructed using URA prior shows additional venous structures that are obscured due to phase dispersion and noise in regions subject to remnant non-local field variations. The performance is compared with the susceptibility map weighted imaging (SMWI) and the standard SWI. It is also shown using numerical simulation that quantitative susceptibility map (QSM) computed from the reconstructed phase exhibits reduced artifacts and quantification error. In vivo experiments reveal iron depositions in insular, motor cortex and superior frontal gyrus that are not identified in standard QSM. Conclusion: URA processed GRE phase is less sensitive to imperfections in the phase pre-processing techniques, and thereby enable robust estimation of SWI and QSM. © 2021, CC BY.

关键词： Matrix algebra

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A linear phase evolution model for reduction of temporal unwrapping and field estimation errors in multi-echo GRE

arXiv

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arXiv 2021年

This article aims at developing a model based optimization for reduction of temporal unwrapping and field estimation errors in multi-echo acquisition of Gradient Echo (GRE) sequence. Using the assumption that the phase is linear along the temporal dimension, the field estimation is performed by application of unity rank approximation (URA) to the Hankel matrix formed using the complex exponential of the channel-combined phase at each echo time. For the purpose of maintaining consistency with the observed complex data, the linear phase evolution (LPE) model is formulated as an optimization problem with a cost function that involves a fidelity term and a unity rank prior, implemented using alternating minimization. Itoh's algorithm applied to the multi-echo phase estimated from this LPE model is able to reduce the unwrapping errors as compared to the unwrapping when directly applied to the measured phase. Secondly, the improved accuracy of the frequency fit in comparison to estimation using weighted least-square regression (wLSR) and penalized maximum likelihood (pML) is demonstrated using numerical simulation of field perturbation due to magnetic susceptibility effect. It is shown that the field can be estimated with 80% reduction in mean absolute error (MAE) in comparison to wLSR and 66% reduction with respect to pML. The improvement in performance becomes more pronounced with increasing strengths of field gradient magnitudes and echo spacing. © 2021, CC BY.

关键词： Matrix algebra

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Multiscale Low-Frequency Memory Network for Improved Feature Extraction in Convolutional Neural Networks

arXiv

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arXiv 2024年

作者： Wu, Fuzhi Wu, Jiasong Kong, Youyong Yang, Chunfeng Yang, Guanyu Shu, Huazhong Carrault, Guy Senhadji, Lotfi Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Bangladesh Laboratoire Traitement du Signal et de l'Image Univ Rennes France Centre de Recherche en Information Biomédicale Sino-français CRIBs France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast University Bangladesh

Deep learning and Convolutional Neural Networks (CNNs) have driven major transformations in diverse research areas. However, their limitations in handling low-frequency information present obstacles in certain tasks like interpreting global structures or managing smooth transition images. Despite the promising performance of transformer structures in numerous tasks, their intricate optimization complexities highlight the persistent need for refined CNN enhancements using limited resources. Responding to these complexities, we introduce a novel framework, the Multiscale Low-Frequency Memory (MLFM) Network, with the goal to harness the full potential of CNNs while keeping their complexity unchanged. The MLFM efficiently preserves low-frequency information, enhancing performance in targeted computer vision tasks. Central to our MLFM is the Low-Frequency Memory Unit (LFMU), which stores various low-frequency data and forms a parallel channel to the core network. A key advantage of MLFM is its seamless compatibility with various prevalent networks, requiring no alterations to their original core structure. Testing on imageNet demonstrated substantial accuracy improvements in multiple 2D CNNs, including ResNet, MobileNet, EfficientNet, and ConvNeXt. Furthermore, we showcase MLFM's versatility beyond traditional image classification by successfully integrating it into image-to-image translation tasks, specifically in semantic segmentation networks like FCN and U-Net. In conclusion, our work signifies a pivotal stride in the journey of optimizing the efficacy and efficiency of CNNs with limited resources. This research builds upon the existing CNN foundations and paves the way for future advancements in computer vision. Our codes are available at https://***/AlphaWuSeu/MLFM. © 2024, CC BY-NC-ND.

关键词： Computer vision

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Optimal Training Positive Sample Size Determination for Deep Learning Caries Recognition Model Establishment with Cbct images

SSRN

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SSRN 2024年

作者： Wang, Yanlin Li, Gang Zhang, Xinyue Wang, Yue Zhang, Zhenhao Li, Jupeng Ma, Junqi Wang, Lingxing Department of Oral and Maxillofacial Radiology Peking University School Hospital of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases National Engineering Research Center of Oral Biomaterials and Digital Medical Device Beijing Key Laboratory of Digital Stomatology NHC Key Laboratory of Digital Stomatology Beijing China Signal and image processing laboratory School of Electronic Information Engineering Beijing Jiao tong University Beijing China Algorithm Department YOFO Medical Technology Co. Ltd Anhui China

Background and objective When performing model training, it is necessary to consider the balance between the impact of sample size, actual resource and time constraints. To do so, the single-arm objective performance criteria for deep learning optimal training positive sample size determination was proposed for caries recognition and identified with three network models. Methods The expected sensitivity of 0.6 and specificity of 0.85 were applied to the single-arm objective performance criteria calculation formula, resulting in an optimal training set with 263 positive samples. According to a ratio of 8:1:1 for the distribution of training set, validation set and test set, the U-Net, YOLOv5n and CariesDetectNet were trained and validated on the clinically self-collected cone-beam computed tomography (CBCT) images. To evaluate performance, an additional set of test sets was used to test the three models. The accuracy of the three best-performed models for caries detection was evaluated through comparisons with the detection accuracy of caries by two dental radiologists. Results When the number of carious teeth reached about 250, the models reached the optimal level of performance. Among the three models, the U-Net performed best, with accuracy, sensitivity, specificity, F1-Score, Dice similarity coefficients, and intersection over union of 0.9929, 0.9307, 0.9989, 0.9590, 0.9435 and 0.9008, respectively. And the three models performed higher accuracy than dental radiologists for caries recognition. Conclusions This study indicated that the optimal number of images with caries in the training set was predictable and it could be calculated through the single-arm objective performance criteria. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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