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Xiaohong Wang

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4 papers

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JBHI Journal 2026 Journal Article

Automatic Sleep Staging of Single-Channel Ear-EEG Signals With a Probabilistic Ensemble Learning Approach

Hongyu Liang
Yongxuan Wang
Le Yang
Meimei Wu
Dan Wang
Xiaohong Wang
Rong Liu

Accurate sleep staging is crucial for the early diagnosis of neurodegenerative diseases and the management of sleep disorders. To provide a user-friendly, non-intrusive, and long-term monitoring solution, we explored the potential clinical applications of ear-electroencephalogram (ear-EEG). This study proposes a probabilistic ensemble learning approach for automatic sleep staging using single-channel ear-EEG data. The proposed method integrates Extreme Gradient Boosting (XGBoost) with Linear Discriminant Analysis (LDA), augmented by transition matrix correction and probability weighting strategies, to capture temporal sleep patterns without compromising data integrity or requiring intensive preprocessing. An ear-EEG with polysomnography (ear-PSG) dataset collected from twenty subjects using our custom-developed ear-EEG sensor, was compared with two public datasets, ear-Feature and Sleep-EDF, to validate both the reliability of the data and the effectiveness of the proposed approach. The results indicate that transition matrix correction is particularly effective when training and testing are conducted using single-epoch inputs, whereas model weighting demonstrates greater stability as the number of epochs increases. When using seven-epochs input sequences, leave-one-subject-out (LOSO) cross-validation achieved 0. 814 accuracy with 0. 749 kappa coefficient on ear-PSG (earL-R), and 0. 841 accuracy with 0. 779 kappa coefficient on the ear-Feature dataset. The design of a single-channel cross-ear intra-auricular ear-EEG configuration, combined with an ensemble learning framework, effectively balances device portability and classification performance, offering new insights for the clinical translation of wearable sleep monitoring technology and laying a foundation for the development of portable sleep monitoring devices.

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JBHI Journal 2025 Journal Article

A Multi-Modality Attention Network for Coronary Artery Disease Evaluation From Routine Myocardial Perfusion Imaging and Clinical Data

Xiaohong Wang
Junmengyang Zhang
Xuefen Teng
Kok Wei Aik
Larry Natividad
Charmaine Cheng
Abigail Pui Choo Wong
Felix Yung Jih Keng

Myocardial perfusion imaging (MPI) is an essential tool for diagnosing and evaluating coronary artery disease (CAD). However, the diagnosis using MPI remains laborious as it involves multi-step readouts and meticulous image processing. These challenges impact current attempts at automating image interpretation of MPI. In this paper, we propose a multi-modality attention network (MMAN) that leverages information from clinical and MPI data for CAD diagnosis. Specifically, we propose an image-correlated cross-attention (ICCA) module that fuses information from both stress and rest MPI to enhance feature representation at the image level. Furthermore, we design a clinical data-guided attention (CDGA) module that integrates clinical data with image features to improve overall feature understanding for CAD evaluation. In addition, we employ self-learning for network pre-training, which further enhances the diagnostic performance using MPI on CAD. Experiments on a myocardial perfusion imaging dataset demonstrate that the proposed method is effective for CAD evaluation using myocardial perfusion imaging and clinical data.

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NeurIPS Conference 2025 Conference Paper

Hierarchical Optimization via LLM-Guided Objective Evolution for Mobility-on-Demand Systems

Yi Zhang
Yushen Long
Yun Ni
Liping Huang
Xiaohong Wang
Jun Liu

Online ride-hailing platforms aim to deliver efficient mobility-on-demand services, often facing challenges in balancing dynamic and spatially heterogeneous supply and demand. Existing methods typically fall into two categories: reinforcement learning (RL) approaches, which suffer from data inefficiency, oversimplified modeling of real-world dynamics, and difficulty enforcing operational constraints; or decomposed online optimization methods, which rely on manually designed high-level objectives that lack awareness of low-level routing dynamics. To address this issue, we propose a novel hybrid framework that integrates large language model (LLM) with mathematical optimization in a dynamic hierarchical system: (1) it is training-free, removing the need for large-scale interaction data as in RL, and (2) it leverages LLM to bridge cognitive limitations caused by problem decomposition by adaptively generating high-level objectives. Within this framework, LLM serves as a meta-optimizer, producing semantic heuristics that guide a low-level optimizer responsible for constraint enforcement and real-time decision execution. These heuristics are refined through a closed-loop evolutionary process, driven by harmony search, which iteratively adapts the LLM prompts based on feasibility and performance feedback from the optimization layer. Extensive experiments based on scenarios derived from both the New York and Chicago taxi datasets demonstrate the effectiveness of our approach, achieving an average improvement of 16% compared to state-of-the-art baselines.

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EAAI Journal 2018 Journal Article

Single-step prediction method of burning zone temperature based on real-time wavelet filtering and KELM

Shizeng Lu
Hongliang Yu
Huijun Dong
Xiaohong Wang
Yongjian Sun

The single-step prediction of burning zone temperature plays an important role in the safety and stability control of cement rotary kiln. This is because, the abnormal temperature events can be found as early as possible and the operator can take effective emergency measures in time. In this paper, the burning zone temperature single-step prediction method based on real-time wavelet filtering and kernel extreme learning machine is studied. Firstly, the visual inspection device is used to detect the burning zone temperature. And then, the amplitude limited filtering method is used to weaken the effects of temperature anomalies. On this basis, the real-time filtering of the burning zone temperature is realized by combining the sliding time window and wavelet filtering method. After that, the single-step prediction of burning zone temperature is realized by combining the sliding time window and kernel extreme learning machine method. At last, the burning zone temperature prediction method is validated. The minimum root mean squared error of the 5 consecutive days is 0. 4259 ° C. The single average running time of model training and prediction of kernel extreme learning machine is much less than support vector regression, which is very helpful for the online prediction of burning zone temperature. The result shows that the burning zone temperature single-step prediction method proposed in this paper is feasible and effective.

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