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Assessing Gender Fairness in EEG-Based Machine Learning Detection of Parkinson's Disease: A Multi-Center Study

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Assessing Gender Fairness in EEG-Based Machine Learning Dete...

European Signal Processing Conference (EUSIPCO)

作者： Anna Kurbatskaya Alberto Jaramillo-Jimenez John Fredy Ochoa-Gomez Kolbjørn Brønnick Alvaro Fernandez-Quilez Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Centre for Age-Related Medicine (SESAM) Stavanger University Hospital Stavanger Norway Department of Public Health Faculty of Health Sciences University of Stavanger Stavanger Norway Grupo de Neurociencias de Antioquia Universidad de Antioquia School of Medicine Medellin Colombia Grupo Neuropsicologia y Conducta Universidad de Antioquia School of Medicine Medellin Colombia Department of Radiology Stavanger Medical Imaging Laboratory (SMIL) Stavanger University Hospital Stavanger Norway

As the number of automatic tools based on machine learning (ML) and resting-state electroencephalography (rs-EEG) for Parkinson's disease (PD) detection keeps growing, the assessment of possible exacerbation of health disparities by means of fairness and bias analysis becomes more relevant. Protected attributes, such as gender, play an important role in PD diagnosis development. However, analysis of sub-group populations stemming from different genders is seldom taken into consideration in ML models' development or the performance assessment for PD detection. In this work, we perform a systematic analysis of the detection ability for gender sub-groups in a multi-center setting of a previously developed ML algorithm based on power spectral density (PSD) features of rs-EEG. We find significant differences in the PD detection ability for males and females at testing time (80.5% vs. 63.7% accuracy) and significantly higher activity for a set of parietal and frontal EEG channels and frequency sub-bands for PD and non-PD males that might explain the differences in the PD detection ability for the gender sub-groups.

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A Search for Technosignatures Around 11,680 Stars with the Green Bank Telescope at 1.15–1.73 GHz

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arXiv 2023年

作者： Margot, Jean-Luc Li, Megan G. Pinchuk, Pavlo Myhrvold, Nathan Lesyna, Larry Alcantara, Lea E. Andrakin, Megan T. Arunseangroj, Jeth Baclet, Damien S. Belk, Madison H. Bhadha, Zerxes R. Brandis, Nicholas W. Carey, Robert E. Cassar, Harrison P. Chava, Sai S. Chen, Calvin Chen, James Cheng, Kellen T. Cimbri, Alessia Cloutier, Benjamin Combitsis, Jordan A. Couvrette, Kelly L. Coy, Brandon P. Davis, Kyle W. Delcayre, Antoine F. Du, Michelle R. Feil, Sarah E. Fu, Danning Gilmore, Travis J. Grahill-Bland, Emery Iglesias, Laura M. Juneau, Zoe Karapetian, Anthony G. Karfakis, George Lambert, Christopher T. Lazbin, Eric A. Li, Jian H. Li, Zhuofu Liskij, Nicholas M. Lopilato, Anthony V. Lu, Darren J. Ma, Detao Mathur, Vedant Minasyan, Mary H. Muller, Maxwell K. Nasielski, Mark T. Nguyen, Janice T. Nicholson, Lorraine M. Niemoeller, Samantha Ohri, Divij Padhye, Atharva U. Penmetcha, Supreethi V. Prakash, Yugantar Qi, Xinyi Rindt, Liam Sahu, Vedant Scally, Joshua A. Scott, Zefyr Seddon, Trevor J. Shohet, Lara-Lynn V. Sinha, Anchal Sinigiani, Anthony E. Song, Jiuxu Stice, Spencer M. Tabucol, Nadine M. Uplisashvili, Andria Vanga, Krishna Vazquez, Amaury G. Vetushko, George Villa, Valeria Vincent, Maria Waasdorp, Ian J. Wagaman, Ian B. Wang, Amanda Wight, Jade C. Wong, Ella Yamaguchi, Natsuko Zhang, Zijin Zhao, Junyang Lynch, Ryan S. Department of Earth Planetary and Space Sciences University of California Los AngelesCA90095 United States Department of Physics and Astronomy University of California Los AngelesCA90095 United States National Renewable Energy Laboratory GoldenCO80401 United States Intellectual Ventures 3150 139th Ave SE BellevueWA98005 United States LXL Technology Las VegasNV89148 United States UCLA Samueli School of Engineering University of California Los AngelesCA90095 United States Department of Mathematics University of California Los AngelesCA90095 United States Department of Electrical Engineering University of California Los AngelesCA90095 United States Department of Public Affairs University of California Los AngelesCA90095 United States Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States Department of Computer Science University of California Los AngelesCA90095 United States Department of Materials Engineering University of California Los AngelesCA90095 United States UCLA College of Letters and Science University of California Los AngelesCA90095 United States Department of Mechanical Engineering University of California Los AngelesCA90095 United States Department of Aerospace Engineering University of California Los AngelesCA90095 United States Department of Neuroscience University of California Los AngelesCA90095 United States Green Bank Observatory P.O. Box 2 Green BankWV24494 United States

We conducted a search for narrowband radio signals over four observing sessions in 2020–2023 with the L-band receiver (1.15–1.73 GHz) of the 100 m diameter Green Bank Telescope. We pointed the telescope in the directions of 62 TESS Objects of Interest, capturing radio emissions from a total of ∼11,680 stars and planetary systems in the ∼9 arcminute beam of the telescope. All detections were either automatically rejected or visually inspected and confirmed to be of anthropogenic nature. We also quantified the end-to-end efficiency of radio SETI pipelines with a signal injection and recovery analysis. The UCLA SETI pipeline recovers 94.0% of the injected signals over the usable frequency range of the receiver and 98.7% of the injections when regions of dense radio frequency interference are excluded. In another pipeline that uses incoherent sums of 51 consecutive spectra, the recovery rate is ∼15 times smaller at ∼6%. The pipeline efficiency affects calculations of transmitter prevalence and SETI search volume. Accordingly, we developed an improved Drake Figure of Merit and a formalism to place upper limits on transmitter prevalence that take the pipeline efficiency and transmitter duty cycle into account. Based on our observations, we can state at the 95% confidence level that fewer than 6.6% of stars within 100 pc host a transmitter that is detectable in our search (EIRP > 1013 W). For stars within 20,000 ly, the fraction of stars with detectable transmitters (EIRP > 5 × 1016 W) is at most 3 × 10−4. Finally, we showed that the UCLA SETI pipeline natively detects the signals detected with AI techniques by Ma et al. (2023). © 2023, CC BY-NC-SA.

关键词： Transmitters

MOTH- Mobility-induced Outages in THz: A Beyond 5G (B5G) application

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MOTH- Mobility-induced Outages in THz: A Beyond 5G (B5G) app...

Consumer Communications and Networking Conference, CCNC IEEE

作者： Rohit Singh Douglas Sicker Kazi Mohammed Saidul Huq Engineering & Public Policy Carnegie Mellon University Pittsburgh USA School of Computer Science Carnegie Mellon University Pittsburgh USA Instituto de Telecomunicaes Aveiro Portugal

ISBN: (数字)9781728138930

ISBN: (纸本)9781728138947

5G will enable the growing demand for Internet of Things (IoT), high-resolution video streaming, and low latency wireless services. Demand for such services is expected to growth rapid, which will require a search for Beyond 5G technological advancements in wireless communications. Part of these advancements is the need for additional spectrum, namely moving toward the terahertz (THz) range. To compensate for the high path loss in THz, narrow beamwidths are used to improve antenna gains. However, with narrow beamwidths, even minor fluctuations in device location (such as through body movement) can cause frequent link failures due to beam misalignment. In this paper, we provide a solution to these small-scale indoor movement that result in mobility-induced outages. Like a moth randomly flutters about, Mobility-induced Outages in THz (MOTH) can be ephemeral in nature and hard to avoid. To deal with MOTH we propose two methods to predict these outage scenarios: (i) Align-After-Failure (AAF), which predicts based on fixed time margins, and (ii) Align-Before-Failure (ABF), which learns the time margins through user mobility patterns. In this paper, two different online classifiers were used to train the ABF model to predicate if a mobility-induced outage is going to occur; thereby, significantly reducing the time spent in outage scenarios. Simulation results demonstrate a relationship between optimal beamwidth and human mobility patterns. Additionally, to cater to a future with dense deployment of Wireless Personal Area Network (WPAN), it is necessary that we have efficient deployment of resources (e.g., THz-APs). One solution is to maximize the user coverage for a single AP, which might be dependent on multiple parameters. We identify these parameters and observe their tradeoffs for improving user coverage through a single THz-AP.

关键词： 5G mobile communication personal area networks telecommunication network reliability Personal area networks telecommunication network reliability terahertz Beam width Wireless personal area networks path loss body movement Moth

Machine learning based potentiating impacts of 12-lead ECG for classifying paroxysmal versus non-paroxysmal atrial fibrillation

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International Journal of Arrhythmia 2022年第1期23卷 1-9页

作者： Sungsoo Kim Alan C. Bovik Sohee Kwon Sung-Hwi Hong JunYong Kim Boyoung Joung Hui-Nam Pak Moon-Hyeong Lee Mia K. Markey Hye Jin Hwang Junbeom Park Electrical and Computer Engineering The University of Texas at Austin Austin USA Deparment of Internal Medicine College of Medicine Yonsei University Seoul South Korea Department of Epidemiology Harvard T. H. Chan School of Public Health Boston MA USA Harvard University Cambridge USA Department of Biomedical Engineering The University of Texas at Austin Austin USA Department of Imaging Physics The University of Texas MD Anderson Cancer Center Houston USA Cardiovascular Division Department of Medicine Beth Israel Deaconess Medical Center Boston MA USA Department of Cardiology College of Medicine Ewha Womans University 1071 Annyangcheon-ro Yangcheon-gu Seoul Republic of Korea

BackgroundConventional modality requires several days observation by Holter monitor to differentiate atrial fibrillation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PA... 详细信息

Background

Conventional modality requires several days observation by Holter monitor to differentiate atrial fibrillation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed.

Objective

To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF.

Methods

In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model.

Results

In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteristics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set.

Conclusions

Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of Echocar

关键词： Machine learning 12 leads surface electrocardiogram Atrial fibrillation

Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges

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arXiv 2025年

作者： Zhang, Yuan Zhang, Xinfeng Qi, Xiaoming Wu, Xinyu Chen, Feng Yang, Guanyu Fu, Huazhu Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications [Southeast University Ministry of Education Nanjing China School of Biomedical Engineering Tsinghua University Beijing China Department of Biomedical Engineering Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Information Science and Engineering Southeast University Nanjing China Department of Biostatistics Center for Global Health School of Public Health Nanjing Medical University Nanjing China Institute of High-Performance Computing Agency for Science Technology and Research Singapore

Generative modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and multimodal representation across diverse diagnostic tasks. This review provides a comprehensive synthesis of recent progress in the field, organized into four key domains: image generation, text generation, multimodal image–text generation, and other generative applications, including spatial simulation and molecular inference. By analyzing over 150 representative studies, we trace the evolution of generative architectures—from early generative adversarial networks to recent advances in diffusion models and foundation models with generative capabilities. We further examine the datasets and evaluation protocols commonly used in this domain and highlight ongoing limitations, including challenges in generating high-fidelity whole slide images, clinical interpretability, and concerns related to the ethical and legal implications of synthetic data. The review concludes with a discussion of open challenges and prospective research directions, with an emphasis on developing unified, multimodal, and clinically deployable generative systems. This work aims to provide a foundational reference for researchers and practitioners developing and applying generative models in computational pathology. © 2025, CC BY-NC-ND.

关键词： Generative adversarial networks

If the prospect of some occupations are stagnating with technological advancement? A task attribute approach to detect employment vulnerability

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arXiv 2020年

作者： Islam, Iftekhairul Shaon, Fahad School of Economics Public Policy and Political Science University of Texas Dallas United States Erik Jonsson School of Engineering and Computer Science University of Texas Dallas United States

Two distinct trends can prove the existence of technological unemployment in the US. First, there are more open jobs than the number of unemployed persons looking for a job, and second, the shift of the Beveridge curve. There have been many attempts to find the cause of technological unemployment. However, all of these approaches fail when it comes to evaluating the impact of modern technologies on employment future. This study hypothesizes that rather than looking into skill requirement or routine non-routine discrimination of tasks, a holistic approach is required to predict which occupations are going to be vulnerable with the advent of this 4th industrial revolution, i.e., widespread application of AI, ML algorithms, and Robotics. Three critical attributes are considered: bottleneck, hazardous, and routine. Forty-five relevant attributes are chosen from the O-NET database that can define these three types of tasks. Performing Principal Axis Factor Analysis, and K-medoid clustering, the study discovers a list of 367 vulnerable occupations. The study further analyzes the last nine years of national employment data and finds that over the previous four years, the growth of vulnerable occupations is only half than that of non-vulnerable ones despite the long rally of economic expansion. Copyright © 2020, The Authors. All rights reserved.

关键词： Employment

Time Dynamics of the Dutch Municipality Network

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arXiv 2023年

作者： Jokić, Ivan van Boven, Edgar Manolopoulos, Ioannis Verma, Trivik Buiten, Gert Pijpers, Frank van Hooff, Hans Van Mieghem, Piet Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology P.O. Box 5031 Delft2600 GA Netherlands Faculty of Technology Policy and Management Delft University of Technology P.O. Box 5031 Delft2600 GA Netherlands KPN Royal P.O. Box 30.000 The Hague2500 GA Netherlands Statistics Netherlands P.O. Box 24.500 The Hague2490 HA Netherlands Kortweg-de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands

Based on data sets provided by Statistics Netherlands and the International Institute of Social History, we investigate the Dutch municipality merging process and the survivability of municipalities over the period 1830 − 2019. We examine the dynamics of the population and area per municipality and how their distributions evolved during the researched period. We apply a Network Science approach, where each node represents a municipality and the links represent the geographical interconnections between adjacent municipalities via roads, railways, bridges or tunnels which were available in each specific yearly network instance. Over the researched period, we find that the distributions of the logarithm of both the population and area size closely follow a normal and a logistic distribution respectively. The tails of the population distributions follow a power-law distribution, a phenomenon observed in community structures of many real-world networks. The dynamics of the area distribution are mainly determined by the merging process, while the population distribution is also driven by the natural population growth and migration across the municipality network. Finally, we propose a model of the Dutch Municipality Network that captures population increase, population migration between municipalities and the process of municipality merging. Our model allows for predictions of the population and area distributions over time. © 2023, CC BY.

关键词： Population distribution

Generalizability of splint therapy for temporomandibular joint disorders in the era of precision medicine

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International journal of rheumatic diseases 2023年第1期26卷 170-171页

作者： Shu-Hao Hsu Wei-Wei Liu Yung-Heng Lee Kevin Sheng-Kai Ma School of Medicine Chung Shan Medical University Taichung Taiwan Republic of China. Department of Orthopedics Cishan Hospital Ministry of Health and Welfare Kaohsiung Taiwan Republic of China. Center for Global Health Perelman School of Medicine University of Pennsylvania Philadelphia Pennsylvania USA. Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan Republic of China. Department of Epidemiology Harvard T.H. Chan School of Public Health Boston Massachusetts USA. Department of Dermatology Massachusetts General Hospital Boston Massachusetts USA.

A Multi-Perspective Machine Learning Approach to Evaluate Police-Driver Interaction in Los Angeles

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arXiv 2024年

作者： Graham, Benjamin A.T. Brown, Lauren Chochlakis, Georgios Dehghani, Morteza Delerme, Raquel Friedman, Brittany Graeden, Ellie Golazizian, Preni Hebbar, Rajat Hejabi, Parsa Kommineni, Aditya Salinas, Mayagüez Sierra-Arévalo, Michael Trager, Jackson Weller, Nicholas Narayanan, Shrikanth Department of Political Science and International Relations University of Southern California Los Angeles90007 United States School of Public Policy University of Southern California Los Angeles90007 United States University of Southern California Los Angeles90007 United States Department of Computer Science University of Southern California Los Angeles90007 United States Brain and Creativity Institute University of Southern California Los Angeles90007 United States Department of Sociology University of Southern California Los Angeles90007 United States Center for Global Health Science and Security Georgetown University WashingtonDC20057 United States Department of Electrical and Computer Engineering University of Southern California Los Angeles90007 United States The Lewis Registry Los AngelesCA90049 United States Department of Sociology The University of Texas at Austin Austin78712 United States Department of Psychology University of Southern California Los Angeles90007 United States Department of Political Science University of California Riverside Riverside92521 United States

Interactions between the government officials and civilians affect public wellbeing and the state legitimacy that is necessary for the functioning of democratic society. Police officers, the most visible and contacted agents of the state, interact with the public more than 20 million times a year during traffic stops. Today, these interactions are regularly recorded by body-worn cameras (BWCs), which are lauded as a means to enhance police accountability and improve police-public interactions. However, the timely analysis of these recordings is hampered by a lack of reliable automated tools that can enable the analysis of these complex and contested police-public interactions. This article proposes an approach to developing new multi-perspective, multimodal machine learning (ML) tools to analyze the audio, video, and transcript information from this BWC footage. Our approach begins by identifying the aspects of communication most salient to different stakeholders, including both community members and police officers. We move away from modeling approaches built around the existence of a single ground truth and instead utilize new advances in soft labeling to incorporate variation in how different observers perceive the same interactions. We argue that this inclusive approach to the conceptualization and design of new ML tools is broadly applicable to the study of communication and development of analytic tools across domains of human interaction, including education, medicine, and the workplace. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning