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A comprehensive review of deep learning power in steady-state visual evoked potentials

Neural Computing and Applications 2024年第27期36卷 16683-16706页

作者： Al-Qaysi, Z.T. Albahri, A.S. Ahmed, M.A. Hamid, Rula A. Alsalem, M.A. Albahri, O.S. Alamoodi, A.H. Homod, Raad Z. Shayea, Ghadeer Ghazi Duhaim, Ali M. Tikrit Iraq Technical College Imam Ja’afar Al-Sadiq University Baghdad Iraq Baghdad Iraq Department of Management Information Systems College of Administration and Economics University of Mosul Mosul Iraq Australian Technical and Management College Melbourne Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Applied Science Research Center Applied Science Private University Amman Jordan Department of Oil and Gas Engineering Basra University of Oil and Gas Basra1004 Iraq Ministry of Education Thi-Qar Education Directorate Thi Qar Iraq MEU Research Unit Middle East University Amman Jordan Baghdad Iraq

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, include

关键词： Deep learning

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Multi-objective optimization determines when, which and how to fuse deep networks: an application to predict COVID-19 outcomes

arXiv

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arXiv 2022年

作者： Guarrasi, Valerio Soda, Paolo Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Italy

The COVID-19 pandemic has caused millions of cases and deaths and the AI-related scientific community, after being involved with detecting COVID-19 signs in medical images, has been now directing the efforts towards the development of methods that can predict the progression of the disease. This task is multimodal by its very nature and, recently, baseline results achieved on the publicly available AIforCOVID dataset have shown that chest X-ray scans and clinical information are useful to identify patients at risk of severe outcomes. While deep learning has shown superior performance in several medical fields, in most of the cases it considers unimodal data only. In this respect, when, which and how to fuse the different modalities is an open challenge in multimodal deep learning. To cope with these three questions here we present a novel approach optimizing the setup of a multimodal end-to-end model. It exploits Pareto multi-objective optimization working with a performance metric and the diversity score of multiple candidate unimodal neural networks to be fused. We test our method on the AIforCOVID dataset, attaining state-of-the-art results, not only outperforming the baseline performance but also being robust to external validation. Moreover, exploiting XAI algorithms we figure out a hierarchy among the modalities and we extract the features' intra-modality importance, enriching the trust on the predictions made by the model. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Classification of Central and Obstructive Sleep Apnea Using Respiratory Inductance Plethysmography and Convolutional Neural Networks

Classification of Central and Obstructive Sleep Apnea Using ...

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IEEE Sensors Applications Symposium, SAS

作者： Matthew Stewart Caitlin Higginson Julien Lariviere-Chartier Elliott Lee James Green Rafik Goubran Frank Knoefel Rebecca Robillard Systems and Computer Engineering Carleton University Ottawa Canada Sleep Research Unit University of Ottawa Institute of Mental Health Research at The Royal Ottawa Canada Bruyere Research Institute Systems and Computer Engineering Carleton University Ottawa Canada Department of Psychiatry University of Ottawa Sleep Research Unit University of Ottawa Institute of Mental Health Research at The Royal Ottawa Canada Faculty of Medicine University of Ottawa Bruyere Research Institute Systems and Computer Engineering Carleton University Ottawa Canada School of Psychology University of Ottawa Sleep Research Unit University of Ottawa Institute of Mental Health Research at The Royal Ottawa Canada

ISBN: (数字)9798350369250

ISBN: (纸本)9798350369267

In this study, we employ a pre-trained Convolutional Neural Network (CNN), specifically ResNetl8, for automated sleep apnea classification using spectral images derived from Respiratory Inductance Plethysmography (RIP) signals. Using data from 130 patients who underwent diagnostic polysomnogram (PSG) at the Sleep Disorders Clinic of the Royal Ottawa Mental Health Centre, we compare the performance of three models fine-tuned using spectrograms of 60 sec segments of the RIPsum, RIPflow, and the concurrent thoracic (THO) and abdominal (ABD) RIP signals. In the case of the THO/ABD model, concurrent spectrograms of the THO and ABD signals are combined into a single image. The resulting models classified any apnea events (central and obstructive apnea/hypopnea) from normal respiration accuracy ranging from 87-90% and F1 scores ranging 90–91 %. We also examine the models' performance on classification of central sleep apnea, obstructive sleep apnea, and normal respiration. The THO/ ABD model classified central apnea events with 89.4% precision, 90.2% recall, 89.8% F1 score and 94.4% specificity. Our results show that RIP belts can be used as an effective screening tool for sleep apnea, and central sleep apnea more specifically, utilizing smaller datasets and minimal training. This approach may be useful for non-invasive, cost-effective diagnostic methodologies at home and in clinical environments.

关键词： Training Inductance Mental health Sleep apnea Distance measurement Sensors Convolutional neural networks

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Realizing Global Citizen Solutions for Sustainable Development Goals: A Collaborative engineering Course Between Thai and Japanese Students 23

Realizing Global Citizen Solutions for Sustainable Developme...

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23rd International Symposium on Communications and Information Technologies, ISCIT 2024

作者： Sae-Jia, Thanachot Leelawat, Natt Tang, Jing Murakami, Rie Taoka, Yuki Ota, Eri Punyabukkana, Proadpran Chulalonkorn University Faculty of Engineering Department of Environmental Engineering Bangkok Thailand Chulalongkorn University Faculty of Engineering Department of Industrial Engineering Bangkok Thailand Chulalongkorn University Disaster and Risk Management Information Systems Research Unit Bangkok Thailand Graduate School of System Design and Management Keio University Yokohama Japan International School of Engineering Chulakongkorn University Faculty of Engineering Bangkok Thailand Global Education Section Center for Entrepreneurship Education Tokyo Institute of Technology Tokyo Japan School of Environment and Society Tokyo Institute of Technology Tokyo Japan Chulalongkorn University Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350353952

One of the problems faced by engineering and science students is focusing solely on technical knowledge while needing more cultural context. In today's interconnected world, addressing global challenges necessitates collaborative efforts from individuals across nations. Accordingly, a joint course between Chulalongkorn University in Thailand and the Tokyo Institute of Technology in Japan has been established. The recent theme of the course, 'Gamification towards Sustainable Development Goals (SDGs),' aimed to foster global citizenship and address SDGs through multicultural collaboration. This study examines the similarities and differences in problem identification between Thai and Japanese student groups participating in the course. Data was collected from discussions, presentations, and group assignments throughout the course. The findings indicate that while both groups share common concerns, such as waste management, language barriers, and transportation issues, their perspectives and proposed solutions are influenced by cultural characteristics. Thai students emphasized infrastructural inadequacies and local traditions, whereas Japanese students focused on efficiency and the impacts of tourism. The study highlights the importance of understanding cultural nuances in collaborative international projects to achieve effective and innovative solutions to global challenges. © 2024 IEEE.

关键词： Sustainable development goals

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Pathologic Complete Response Prediction with Machine Learning Using Hierarchical Attention Feature Extraction 27th

Pathologic Complete Response Prediction with Machine Learni...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Paolo, Domenico Russo, Ciro Russo, Giulio Greco, Carlo Cortellini, Alessio Russano, Marco Ramella, Sara Soda, Paolo Marrocco, Claudio Bria, Alessandro Sicilia, Rosa Unit of Computer Systems and Bioinformatics Department of Engineering Universita‘ Campus Bio-Medico di Roma Selcetta Italy Department of Electrical and Information Engineering University of Cassino and Lazio Meridionale FR Cassino03043 Italy Research Unit of Radiation Oncology Department of Medicine and Surgery University Campus Bio-Medico di Roma Selcetta Italy Operative Research Unit of Medical Oncology Fondazione Policlinico Universitario Campus Bio-Medico Portillo Italy Department of Diagnostics and Intervention Umeå University Umeå Sweden

ISBN: (纸本)9783031876592

Predicting pathologic complete response in non-small cell lung cancer is crucial for tailoring effective treatment strategies and to improve patient outcomes. With the increasing application of artificial intelligence in cancer research, machine learning is poised to play a significant role in prognostication and decision-making. This paper presents a novel approach that utilizes named entity recognition and attention mechanisms applied to electronic health records to predict the pathologic complete response. We first employ named entity recognition to extract relevant biomedical entities from unstructured clinical notes within reports. These entities, combined with structured data, are then processed using a hierarchical attention mechanism to generate comprehensive patient representations. This approach captures complex relationships and contextual information within electronic health records compared to traditional methods. The results highlight the potential of advanced natural language processing techniques to enhance clinical decision-making and support personalized treatment planning in oncology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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From Pre-Training to Fine-Tuning: An In-Depth Analysis of Large Language Models in the Biomedical Domain

SSRN

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SSRN 2024年

作者： Bonfigli, Agnese Bacco, Luca Merone, Mario Dell’Orletta, Felice ItaliaNLP Lab Institute of Computational Linguistics "Antonio Zampolli" National Research Council Via Giuseppe Moruzzi 1 Pisa56124 Italy Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy

In this study, we delve into the adaptation and effectiveness of Transformer-based, pre-trained Large Language Models (LLMs) within the biomedical domain, a field that poses unique challenges due to its complexity and the specialized nature of its data. Building on the foundation laid by the transformative architecture of Transformers, we investigate the nuanced dynamics of LLMs through a multifaceted lens, focusing on two domain-specific tasks, i.e., Natural Language Inference (NLI) and Named Entity Recognition (NER).Our objective is to bridge the knowledge gap regarding how these models’ downstream performances correlate with their capacity to encapsulate task-relevant *** achieve this goal, we probed and analyzed the inner encoding and attention mechanisms in LLMs, both encoder- and decoder-based, tailored for either general or biomedical-specific applications. This examination occurs before and after the models are fine-tuned across various data volumes. Our findings reveal that the models' downstream effectiveness is intricately linked to specific patterns within their internal mechanisms, shedding light on the nuanced ways in which LLMs process and apply knowledge in the biomedical context. The source code for this paper is available at https://***/agnesebonfigli99/LLMs-in-the-Biomedical-Domain. © 2024, The Authors. All rights reserved.

关键词： Signal encoding

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Quantifying, Activating and Rewarding Flexibility in Renewable Energy Communities

Quantifying, Activating and Rewarding Flexibility in Renewab...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Julien Allard François Vallée Zacharie De Grève Thomas Stegen Mevludin Glavic Bertrand Cornélusse Power Systems and Markets Research Group Electrical Power Engineering Unit University of Mons Mons Belgium Department of Electrical Engineering and Computer Science University of Liège Liège Belgium

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Demand-side flexibility from renewable energy community members increases the benefits of local production and exchanges. To effectively harness this flexibility, end-users must be rewarded for their efforts regarding the shifted energy volumes and the associated discomfort. This work quantifies these last two quantities for three types of loads (electric vehicles, thermal loads and wet appliances). Then, a two-step model is proposed which first optimally activates the offered flexibility and then shares the benefits of this activation among the renewable energy community members through a rule-based or an optimization-based approach. Results show that the community framework can further increase the benefits brought by flexible demand.

关键词： Renewable energy sources Europe Tariffs Production Pricing Thermal management Electric vehicles Smart grids Thermal loading Load modeling

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Few-Shot Fruit Segmentation via Transfer Learning

Few-Shot Fruit Segmentation via Transfer Learning

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jordan A. James Heather K. Manching Amanda M. Hulse-Kemp William J. Beksi Department of Computer Science and Engineering The University of Texas at Arlington Arlington TX USA Department of Crop and Soil Sciences North Carolina State University Raleigh NC USA Genomics and Bioinformatics Research Unit USDA Agricultural Research Service Raleigh NC USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Advancements in machine learning, computer vision, and robotics have paved the way for transformative solutions in various domains, particularly in agriculture. For example, accurate identification and segmentation of fruits from field images plays a crucial role in automating jobs such as harvesting, disease detection, and yield estimation. However, achieving robust and precise infield fruit segmentation remains a challenging task since large amounts of labeled data are required to handle variations in fruit size, shape, color, and occlusion. In this paper, we develop a few-shot semantic segmentation framework for infield fruits using transfer learning. Concretely, our work is aimed at addressing agricultural domains that lack publicly available labeled data. Motivated by similar success in urban scene parsing, we propose specialized pre-training using a public benchmark dataset for fruit transfer learning. By leveraging pre-trained neural networks, accurate semantic segmentation of fruit in the field is achieved with only a few labeled images. Furthermore, we show that models with pre-training learn to distinguish between fruit still on the trees and fruit that have fallen on the ground, and they can effectively transfer the knowledge to the target fruit dataset.

关键词： computer vision Accuracy Shape Image color analysis Semantic segmentation Transfer learning Neural networks

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

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Frontiers of Medicine 2022年第3期16卷 496-506页

作者： Yaxin Chen Tianyi Yang Xiaofeng Gao Ajing Xu Department of Pharmacy Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Clinical Research Unit Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia,usage of diabetes drugs,changes in insulin levels,and excretion,and this risk begins as early as *** factors including demographic data(such as age,height,weight,and gender),medical history(such as smoking,drinking,and menopause),and examination(such as bone mineral density,blood routine,and urine routine)may be related to bone metabolism in patients with ***,most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of *** addition,the fracture risk of patients with diabetes and osteoporosis has not been further studied *** this paper,a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis,and investigate the effect of patients’physiological factors on fracture risk.A total of 147 raw input features are considered in our *** presented model is compared with several benchmarks based on various metrics to prove its ***,the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction

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An optimized ensemble search approach for classification of higher-level gait disorder using brain magnetic resonance images

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computers in Biology and Medicine 2025年 184卷 109457-109457页

作者： Mogensen, Klara Guarrasi, Valerio Larsson, Jenny Hansson, William Wåhlin, Anders Koskinen, Lars-Owe Malm, Jan Eklund, Anders Soda, Paolo Qvarlander, Sara Department of Diagnostics and Intervention Biomedical Engineering and Radiation Physics Umeå University 90187 Sweden Unit of Computer Systems and Bioinformatics Department of Engineering University Campus Bio-Medico of Rome 00128 Italy Department of Clinical Sciences Neurosciences Umeå University 90187 Sweden Umeå Center for Functional Brain Imaging Umeå University 90187 Sweden Department of Applied Physics and Electronics Umeå University 90187 Sweden

Higher-Level Gait Disorder (HLGD) is a type of gait disorder estimated to affect up to 6% of the older population. By definition, its symptoms originate from the higher-level nervous system, yet its association with brain morphology remains unclear. This study hypothesizes that there are patterns in brain morphology linked to HLGD. For the first time in the literature, this work investigates whether deep learning, in the form of convolutional neural networks, can capture patterns in magnetic resonance images to identify individuals affected by HLGD. To handle this new classification task, we propose setting up an ensemble of models. This leverages the benefits of combining classifiers instead of determining which network is the most suitable, developing a new architecture, or customizing an existing one. We introduce a computationally cost-effective search algorithm to find the optimal ensemble by leveraging a cost function of both traditional performance scores and the diversity among the models. Using a unique dataset from a large population-based cohort (VESPR), the ensemble identified by our algorithm demonstrated superior performance compared to single networks, other ensemble fusion techniques, and the best linear radiological measure. This emphasizes the importance of implementing diversity into the cost function. Furthermore, the results indicate significant morphological differences in brain structure between HLGD-affected individuals and controls, motivating research about which areas the networks base their classifications on, to get a better understanding of the pathophysiology of HLGD. © 2024 The Authors

关键词： Cost functions

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