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Jeng-Shyang Pan

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10

JBHI Journal 2025 Journal Article

SleepHybridNet: A Lightweight Hybrid CNN-Transformer Model for Enhanced N1 Sleep Staging From Single-Channel EEG

  • Hao Zhou
  • Mengxiang Su
  • Jeng-Shyang Pan
  • Chenglong Dai
  • Ying Chen
  • Shu-Chuan Chu

This study introduces SleepHybridNet, a lightweight hybrid CNN-Transformer model designed to enhance the classification of non-rapid eye movement stage 1 (N1) sleep using single-channel electroencephalogram (EEG) signals. Accurate identification of the N1 stage is of critical importance in both sleep neuroscience and clinical practice. However, due to the ambiguous features during N1 stage, current deep learning models still struggle to achieve satisfactory performance. To address these challenges, SleepHybridNet integrates multi-scale feature fusion and sequence modeling through a novel architecture. It consists of a Multi-Scale Convolutional Neural Network (MSCNN) module, a Transformer encoder, a spectral feature extraction unit, and a multi-task classifier. Experimental results based on the publicly available Sleep-EDF Expanded dataset demonstrate that SleepHybridNet outperforms existing methods in both classification accuracy and generalization capability. Specifically, the model achieves an overall accuracy of 88. 2% and an F1-score of 0. 633 for the N1 stage, showing superior performance particularly in underrepresented classes such as N1 and N3 stages. With only 5. 1 M parameters, the lightweight design of the model can enable practical deployment in clinical settings, bridging the gap between high-performance deep learning algorithms and practical applicability in sleep medicine. Future work may explore the integration of multimodal data from wearable sensors to further expand its use in diverse application scenarios.

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AAAI Conference 2016 Conference Paper

BRBA: A Blocking-Based Association Rule Hiding Method

  • Peng Cheng
  • Ivan Lee
  • Li Li
  • Kuo-Kun Tseng
  • Jeng-Shyang Pan

Privacy preserving in association rule mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.

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JBHI Journal 2014 Journal Article

A Collaborative Computing Framework of Cloud Network and WBSN Applied to Fall Detection and 3-D Motion Reconstruction

  • Chin-Feng Lai
  • Min Chen
  • Jeng-Shyang Pan
  • Chan-Hyun Youn
  • Han-Chieh Chao

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

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AAAI Conference 2014 Conference Paper

Association Rule Hiding Based on Evolutionary Multi-Objective Optimization by Removing Items

  • Peng Cheng
  • Jeng-Shyang Pan

Today, people benefit from utilizing data mining technologies, such as association rule mining methods, to find valuable knowledge residing in a large amount of data. However, they also face the risk of exposing sensitive or confidential information, when data is shared among different organizations. Thus, a question arises: how can we prevent that sensitive knowledge is discovered, while ensuring that ordinary non-sensitive knowledge can be mined to the maximum extent possible. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A new hiding method based on evolutionary multi-objective optimization (EMO) is proposed and the side effects generated by the hiding process are formulated as optimization goals. EMO is used to find candidate transactions to modify so that side effects are minimized. Comparative experiments with exact methods on real datasets demonstrated that the proposed method can hide sensitive rules with fewer side effects.

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