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Machine learning-optimized non-invasive brain stimulation and treatment response classification for major depression

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Bioelectronic Medicine 2024年第1期10卷 1-14页

作者： Albizu, Alejandro Indahlastari, Aprinda Suen, Paulo Huang, Ziqian Waner, Jori L. Stolte, Skylar E. Fang, Ruogu Brunoni, Andre R. Woods, Adam J. Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida Gainesville United States Department of Neuroscience College of Medicine University of Florida Gainesville United States J. Crayton Pruitt Family Department of Biomedical Engineering Herbert Wertheim College of Engineering University of Florida Gainesville United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida Gainesville United States Department of Clinical and Health Psychology College of Public Health and Health Professions University of Florida PO Box 100165 1225 Center Drive GainesvilleFL32610-0165 United States Faculdade de Medicina da Universidade de São Paulo São Paulo Brazil

Background/Objectives: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation intervention that shows promise as a potential treatment for depression. However, the clinical efficacy of tDCS varies, possibly due to individual differences in head anatomy affecting tDCS dosage. While functional changes in brain activity are more commonly reported in major depressive disorder (MDD), some studies suggest that subtle macroscopic structural differences, such as cortical thickness or brain volume reductions, may occur in MDD and could influence tDCS electric field (E-field) distributions. Therefore, accounting for individual anatomical differences may provide a pathway to optimize functional gains in MDD by formulating personalized tDCS dosage. Methods: To address the dosing variability of tDCS, we examined a subsample of sixteen active-tDCS participants’ data from the larger ELECT clinical trial (NCT01894815). With this dataset, individualized neuroimaging-derived computational models of tDCS current were generated for (1) classifying treatment response, (2) elucidating essential stimulation features associated with treatment response, and (3) computing a personalized dose of tDCS to maximize the likelihood of treatment response in MDD. Results: In the ELECT trial, tDCS was superior to placebo (3.2 points [95% CI, 0.7 to 5.5;P = 0.01]). Our algorithm achieved over 90% overall accuracy in classifying treatment responders from the active-tDCS group (AUC = 0.90, F1 = 0.92, MCC = 0.79). Computed precision doses also achieved an average response likelihood of 99.981% and decreased dosing variability by 91.9%. Conclusion: These findings support our previously developed precision-dosing method for a new application in psychiatry by optimizing the statistical likelihood of tDCS treatment response in MDD. © The Author(s) 2024.

关键词： Noninvasive medical procedures

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Capturing Physiological Correlates of Stress-Induced Blood Pressure Elevation Using a Multimodal Wearable Sensing Patch in Patients with a History of Myocardial Infarction 46

Capturing Physiological Correlates of Stress-Induced Blood P...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Nawar, Afra Sanchez-Perez, Jesus Antonio Rahman, Farhan N. Gazi, Asim H. Chan, Michael Ziegler, Carrie Daaboul, Obada Haddad, George Al-Abboud, Omar A. Ahmed, Hashir Murrah, Nancy Vaccarino, Viola Shah, Amit J. Inan, Omer T. Emory University Rollins School of Public Health Department of Epidemiology AtlantaGA United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States Georgia Institute of Technology School of Biomedical Engineering AtlantaGA United States

ISBN: (纸本)9798350371499

Persons who have experienced a prior myocardial infarction (MI) are at a greater risk of experiencing a secondary event. External factors, such as stress, can cause transient hypertension, further increasing cardiovascular disease risk. However, challenges to monitoring stress-induced hypertension include obtrusiveness of monitoring devices, unknown validity in high-risk populations, and unsubstantiated sensitivity in stressful environments. In this pilot study, we investigated data from a validated, multimodal, wearable patch to examine physiological correlates of laboratory-based hypertensive stress responses. The device collected electrocardiogram, seismocardiogram, and photoplethysmogram signals from 35 participants (26 post-MI and 9 healthy participants) during a protocol involving a public speaking stressor. We calculated the change from rest to stress in 10 features extracted from these signals and assessed their correlation to stress-induced changes in mean arterial pressure (MAP) derived from a validated brachial pressure cuff. MAP changes correlated significantly with changes in heart rate (p © 2024 IEEE.

关键词： Blood pressure

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DAY-AHEAD DISPATCH RESCHEDULING FOR CONGESTION MITIGATION UNDER OPTIMAL POWER FLOW 13

DAY-AHEAD DISPATCH RESCHEDULING FOR CONGESTION MITIGATION UN...

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13th Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion, MEDPOWER 2022

作者： Kelepouris, Nikolaos S. Bouhouras, Aggelos S. Koltsaklis, Nikolaos Panapakidis, Ioannis Tsiatsios, Constantinos Christoforidis, Georgios C. Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Energy & Environmental Policy Laboratory School of Economics Business and International Studies University of Piraeus Piraeus Greece Department of Electrical and Computer Engineering University of Thessaly Volos Greece

ISBN: (纸本)9781839538445

In this paper the problem of congestion management in the power grid under the application of the day-ahead dispatch schedule, is faced based on developing a day-ahead guideline regarding the required modifications to the power units’ output to yield a more uniform loading to the grid’s lines. These modifications result by comparing the dispatch schedule provided by an optimal power flow (OPF) analysis for the grid, under the objective function of power loss minimization, with the schedule for the day-ahead dispatch schedule. The point here is that the solution for the latter approach is based on an economical perspective while the former one on a technical one. The proposed approach is tested on the benchmarked 118 bus system which was modified to simulate the Greek transmission network. The day-ahead schedule is initially applied on the system to depict possible congestion issues via a power flow (PF) analysis. Then, an OPF analysis is applied in order to reschedule the power output of the power units, within their operational limits, aiming at power loss minimization. The results indicate that congestion mitigation in terms of lines’ loading below their thermal limit could result under proper and guided alterations for the power units output. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Electric power transmission networks

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Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

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Digital Communications and Networks 2023年第2期9卷 296-305页

作者： Jing Zhang Qichen Yan Xiaogang Zhu Keping Yu Department of Computer Science and Technology Xi'an University of Science and TechnologyXi'an710054China Department of Information Science and Technology Northwest UniversityXi’an 710127China School of Public Policy and Administration Nanchang UniversityNanchangJiangxi330031China Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence ***,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not *** solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal ***,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position ***,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground *** them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,***,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

关键词： Crowd sensing Industrial Internet of things Safety monitoring Coal mine

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cpp11armadillo: An R Package to Use the Armadillo C++ Library

arXiv

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arXiv 2024年

作者： Sepúlveda, Mauricio Vargas Malamud, Jonathan Schneider Department of Political Science University of Toronto Munk School of Global Affairs and Public Policy University of Toronto Canada Department of Electrical and Computer Engineering University of Toronto Canada

This article introduces ‘cpp11armadillo’, a new R package that integrates the powerful Armadillo C++ library for linear algebra into the R programming environment. Targeted primarily at social scientists and other non-programmers, this article explains the computational benefits of moving code to C++ in terms of speed and syntax. We provide a comprehensive overview of Armadillo’s capabilities, highlighting its user-friendly syntax akin to MATLAB and its efficiency for computationally intensive tasks. The ‘cpp11armadillo’ package simplifies a part of the process of using C++ within R by offering additional ease of integration for those who require high-performance linear algebra operations in their R workflows. This work aims to bridge the gap between computational efficiency and accessibility, making advanced linear algebra operations more approachable for R users without extensive programming backgrounds. © 2024, CC BY-NC-SA.

关键词： MATLAB

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Classifying Bicycle Infrastructure Using On-Bike Street-Level Images

Classifying Bicycle Infrastructure Using On-Bike Street-Leve...

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International Conference on Intelligent Transportation

作者： Kal Backman Ben Beck Dana Kulić Department of Electrical and Computer Systems Engineering School of Public Health and Preventive Medicine Monash University

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

While cycling offers an attractive option for sus-tainable transportation, many potential cyclists are discouraged from taking up cycling due to the lack of suitable and safe infrastructure. Efficiently mapping cycling infrastructure across entire cities is necessary to advance our understanding of how to provide connected networks of high-quality infrastructure. Therefore we propose a system capable of classifying available cycling infrastructure from on-bike smartphone camera data. The system receives an image sequence as input, temporally analyzing the sequence to account for sparsity of signage. The model outputs cycling infrastructure class labels defined by a hi-erarchical classification system. Data is collected via participant cyclists covering 7,006Km across the Greater Melbourne region that is automatically labeled via a GPS and OpenStreetMap database matching algorithm. The proposed model achieved an accuracy of 95.38%, an increase in performance of 7.55% compared to the non-temporal model. The model demonstrated robustness to extreme absence of image features where the model lost only 6.6% in accuracy after 90% of images being replaced with blank images. This work is the first to classify cycling infrastructure using only street-level imagery collected from bike-mounted mobile phone cameras, while demonstrating robustness to feature sparsity via long temporal sequence analysis.

关键词： Accuracy Databases Bicycles Feature extraction Cameras Robustness Vectors Image sequences Data mining Global Positioning System

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Colorectal Polyp Localization: From Image Restoration to Real-time Detection with Deep Learning 17

Colorectal Polyp Localization: From Image Restoration to Rea...

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17th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2023

作者： Manshadi, Mahsa Dehghan Mousavi, Milad Golzarian, Arian Soltani, Madjid Mosavi, Amir K.N. Toosi University of Technology Department of Mechanical Engineering Tehran Iran University of Waterloo Department of Electrical and Computer Engineering Ontario Canada Obuda University John von Neumann Faculty of Informatics Budapest Hungary University of Public Service Budapest Hungary

ISBN: (纸本)9798350321104

Increasing demands for artificial intelligence tools in the medical sector show the huge interest of physicians. In recent years, one of the most effective assistant systems in this field is the real-time detection of early-stage colorectal polyps during colonoscopy. Since even experienced physicians may miss polyps during colonoscopy, the real-time assistance system is designed to prevent this and hence contributes to diminish the number of missed critical cases. One challenge in this field is the detection of false positives. These systems are prone to mistake artifacts for colorectal polyps. This review provides an overview over current quality assessment and restoration techniques to make a high-quality training dataset for training deep neural network algorithms. Furthermore, four of the latest, fastest, and most accurate methods are introduced and analyzed in the rest of the review. Our main contribution is to provide an analysis of current methods used to detect colorectal polyps. We present a list of available datasets and present a range of challenges colorectal cancer detection systems face. © 2023 IEEE.

关键词： Image reconstruction

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Towards Adaptive Human-centric Video Anomaly Detection: A Comprehensive Framework and A New Benchmark

arXiv

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arXiv 2024年

作者： Pazho, Armin Danesh Yao, Shanle Noghre, Ghazal Alinezhad Ardabili, Babak Rahimi Katariya, Vinit Tabkhi, Hamed Department of Electrical and Computer Engineering University of North Carolina Charlotte United States Public Policy Program University of North Carolina Charlotte United States

Human-centric Video Anomaly Detection (VAD) aims to identify human behaviors that deviate from normal. At its core, human-centric VAD faces substantial challenges, such as the complexity of diverse human behaviors, the rarity of anomalies, and ethical constraints. These challenges limit access to high-quality datasets and highlight the need for a dataset and framework supporting continual learning. Moving towards adaptive human-centric VAD, we introduce the HuVAD (Human-centric privacy-enhanced Video Anomaly Detection) dataset and a novel Unsupervised Continual Anomaly Learning (UCAL) framework. UCAL enables incremental learning, allowing models to adapt over time, bridging traditional training and real-world deployment. HuVAD prioritizes privacy by providing de-identified annotations and includes seven indoor/outdoor scenes, offering over 5× more pose-annotated frames than previous datasets. Our standard and continual benchmarks, utilize a comprehensive set of metrics, demonstrating that UCAL-enhanced models achieve superior performance in 82.14% of cases, setting a new state-of-the-art (SOTA). The dataset can be accessed at https://***/TeCSAR-UNCC/HuVAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Differential privacy

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Predicting COVID-19 Transmission in Southern California with Machine Learning Methods

Predicting COVID-19 Transmission in Southern California with...

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IEEE International Conference on Big Data Analysis (ICBDA)

作者： Han Li Ran Wei Wenyu Wang Nanpeng Yu School of Computer Science and Engineering University of California Riverside Riverside USA School of Public Policy University of California Riverside Riverside USA Department of Electrical and Computer Engineering University of California Riverside Riverside USA

ISBN: (数字)9798350352962

ISBN: (纸本)9798350352979

The COVID-19 pandemic has posed significant chal-lenges to global health, economies, and social structures since its emergence in late 2019. Southern California, a region with unique characteristics and diverse communities, has experienced difficul-ties in controlling the virus's spread. Understanding the factors that influence COVID-19 transmission in Southern California is crucial for informing public health policies and mitigating the virus's impact on the communities. In this study, we collected weekly zip code level data from March to December 2020 in Southern California and applied various machine learning algorithms, such as graph neural networks (GNN), multi-layer perceptrons (MLP), and gradient boosting trees (XGBoost), to model the spread of COVID-19. Our main contributions include the development of various COVID-19 forecasting models that incorporate socioeconomically significant variables and zip code level mobility data, coupled with variable impact assessments to provide valuable insights for informing future public health policies and strategies. Our predictive model not only attains high levels of accuracy in forecasting but also facilitates the understanding of how each variable contributes to the final outcome. These findings have the potential to enhance our understanding of COVID-19 transmission patterns and inform targeted mitigation strategies in the region.

关键词： COVID-19 Analytical models Pandemics Sociology Predictive models Data models public healthcare

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Sensorless End-to-End Freehand Ultrasound with Physics Inspired Network

Sensorless End-to-End Freehand Ultrasound with Physics Inspi...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Dou, Yimeng Mu, Fangzhou Li, Yin Varghese, Tomy -Madison Department of Electrical and Computer Engineering MadisonWI United States University of Wisconsin School of Medicine and Public Health Department of Medical Physics MadisonWI United States UW-Madison Department of Computer Science MadisonWI United States University of Wisconsin School of Medicine and Public Health Department of Biostatistics & Medical Informatics MadisonWI United States

ISBN: (纸本)9798350346459

Three-dimensional ultrasound (3D US) imaging finds broad applications in clinical practice. Compared to using 'wobbler' (motor embedded) or 'matrix array' transducers which suffers from a restricted field of view, freehand US offers more flexibility in mapping the 3D volume along the scanning path. Nevertheless, current methods for reconstructing 3D US volumes using freehand scanning are challenged by significant elevational shifts along the scanning trajectory. Previous research explored the integration of motion sensors to resolve transducer drift, yet frequent motion artifacts often compromise the benefits brought by these sensors. Recent work turned to deep neural networks (DNNs) for estimating the relative pose of imaging planes between frames in the scanning trajectory, allowing for sensorless freehand US. However, these data-driven models fall short in accuracy as they lack physical constraints, hence limiting their use in clinical practice. Inspired by the physical properties of US, we design a physics inspired DNN architecture that can better leverage the contextual cues between elevational frames for sensorless freehand 3D US reconstruction, and demonstrate substantially improving reconstruction accuracy. © 2023 IEEE.

关键词： Deep neural networks

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