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Changhong Wang

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4 papers

1 author row

JBHI Journal 2025 Journal Article

An EEG Screening Method for Severe Obstructive Sleep Apnea Based on Limited Penetrable Difference Visibility Graph and Graph Convolutional Network

Yuchen Zhang
Zhengyuan Li
Yanxun Lu
Yuxia Zhang
Guanzheng Liu
Changhong Wang

Obstructive Sleep Apnea (OSA) is a common respiratory disease characterized by recurrent airway block during sleep, which does great harm to the human body. Utilizing the electroencephalogram (EEG) has been proven instrumental in OSA detection, as sleep apnea occurrences induce discernible alterations in EEG patterns. In this study, we propose a Limited Penetrable Difference Visibility Graph (LPDVG) method to screen severe OSA. This method exhibits strong anti-noise performance, effective information extraction, and a certain degree of generalization ability. First of all, this study constructed LPDVG complex network and calculated the information entropy of the degree sequence in six leads. Subsequently, this study weighted the information entropy of each lead using the mutual information between leads to fuse information from the whole brain. Eventually, a classification model for the Graph Convolutional Network (GCN) was trained to detect patients with severe OSA. Using a dataset of 88 participants, we tested and evaluated this approach. The results showed a strong correlation between the extracted feature and AHI, with a Pearson correlation of 0. 792. The accuracy, specificity, sensitivity, and area under the curve (AUC) of the GCN classification were 82. 95%, 83. 87%, 80. 77%, and 0. 905. Moreover, there are significant differences in LPDVG wSEN between patients in severe and non-severe OSA groups. Compared to those with non-severe OSA, brain activity in patients with severe OSA is more disorganized, especially in the theta frequency band. EEG data indicating this elevated activity is associated with disturbed sleep patterns in individuals with OSA.

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NeurIPS Conference 2024 Conference Paper

Regularized Conditional Diffusion Model for Multi-Task Preference Alignment

Xudong Yu
Chenjia Bai
Haoran He
Changhong Wang
Xuelong Li

Sequential decision-making can be formulated as a conditional generation process, with targets for alignment with human intents and versatility across various tasks. Previous return-conditioned diffusion models manifest comparable performance but rely on well-defined reward functions, which requires amounts of human efforts and faces challenges in multi-task settings. Preferences serve as an alternative but recent work rarely considers preference learning given multiple tasks. To facilitate the alignment and versatility in multi-task preference learning, we adopt multi-task preferences as a unified framework. In this work, we propose to learn preference representations aligned with preference labels, which are then used as conditions to guide the conditional generation process of diffusion models. The traditional classifier-free guidance paradigm suffers from the inconsistency between the conditions and generated trajectories. We thus introduce an auxiliary regularization objective to maximize the mutual info

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AAMAS Conference 2019 Conference Paper

Reaching Cooperation using Emerging Empathy and Counter-empathy

Jize Chen
Changhong Wang

According to social neuropsychology, the cooperative behavior is largely influenced by empathy, which is deemed essential of emotional system and has wide impact on social interaction. In the work reported here, we believe that the emergence of empathy and counter-empathy is closely related to creatures’ inertial impression on intragroup coexistence and competition. Based on this assumption, we establish a unified model of empathy and counter-empathy in light of Hebb’s rule. We also present Adaptive Empathetic Learner (AEL), a training method for agents to enable affective utility evaluation and learning procedure in multi-agent system. In AEL, the empathy model is integrated into the adversarial bandit setting in order to achieve a high degree of versatility. Our algorithm is first verified in the survival game, which is designed to simulate the primitive hunting environment. In this game, empathy and cooperation emerge among agents with different power. In another test about Iterated Prisoners’ Dilemma, cooperation was reached even between an AEL agent and a rational one. Moreover, when confronted with hostile, the AEL agent showed sufficient goodwill and vigilantly protected its safe payoffs. In the Ultimatum Game, it’s worth mentioning that absolute fairness could be achieved on account of the self-adaptation of empathy and counter-empathy.

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JBHI Journal 2018 Journal Article

A Low-Power Fall Detector Balancing Sensitivity and False Alarm Rate

Changhong Wang
Wei Lu
Stephen J. Redmond
Michael C. Stevens
Stephen R. Lord
Nigel H. Lovell

Falls in older people are a major challenge to public health. A wearable fall detector can detect falls automatically based on kinematic information of the human body, allowing help to arrive sooner. To date, most studies have focused on the accuracy of an offline algorithm to distinguish real-world or simulated falls from activities of daily living, while neglecting the false alarm rate and battery life of a real device. To address these two important metrics, which significantly influence user compliance, this paper proposes a low-power fall detector using triaxial accelerometry and barometric pressure sensing. This fall detector minimizes power consumption using both hardware- and firmware-based techniques. Additionally, the fall detection algorithm used in this device is optimized to achieve a balance between sensitivity and false alarm rate, while minimizing the power consumption due to algorithm execution. The fall detector achieved a high sensitivity (91%) with a low false alarm rate (0. 1149 alarms per hour), and a commercially-viable battery life (1125 days).

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