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Yuheng He

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JBHI Journal 2025 Journal Article

A Cuffless Blood Pressure Estimation Method Using Dimensionality Increasing and Two-Dimensional Convolution

  • Shouyi Cui
  • Guowei Yang
  • Jingxuan Guan
  • Yuheng He
  • Xuefang Zhou
  • Meihua Bi
  • Hanghai Shen
  • Yuansheng Xu

Blood pressure (BP) monitoring is a basic way to evaluate hypertension and its related diseases. Since non-invasive measurement with cuff is not real-time and invasive measurement with vessel puncture is not practical in daily life, this paper proposes a cuffless BP estimation method using two-dimensional (2D) convolution. Dimensionality increasing algorithms including recurrence plot and Gramian angular field are firstly used to convert electrocardiography (ECG) and photoplethysmography (PPG) signals into 2D images. New fused Gramian angular field (FGAF) and combined Gramian angular field (CGAF) are proposed to reduce the input 2D images data and enhance the signals’ relevance. The converted images are used to train 2D convolutional models and estimate BP values. The 2D models effectively improved BP estimation accuracy, and the accuracy of the VGGNet 2D model using Gramian angular difference field (GADF) is improved by 38% compared with the corresponding 1D convolutional model. The proposed FGAF and CGAF can reduce input data by 50% while maintaining estimation accuracy, and the minimum mean absolute errors of the estimated BP values could reach 2. 71 and 1. 74 mmHg for systolic and diastolic blood pressures, respectively. To reduce model size, the VGGNet BP estimation model is pruned by reducing 60% of channel numbers while maintain the model performance. The pruned VGGNet model using the FGADF is then fine-tuned and validated by MIMIC-III dataset to show its generalization ability. Furthermore, a simple monitor system is built to show the feasibility of signal collection and BP estimation.

Details DOI

NeurIPS Conference 2024 Conference Paper

Lambda: Learning Matchable Prior For Entity Alignment with Unlabeled Dangling Cases

  • Hang Yin
  • Liyao Xiang
  • Dong Ding
  • Yuheng He
  • Yihan Wu
  • Pengzhi Chu
  • Xinbing Wang
  • Chenghu Zhou

We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), yet these entities are unlabeled. The problem arises when the source and target graphs are of different scales, and it is much cheaper to label the matchable pairs than the dangling entities. To address this challenge, we propose the framework \textit{Lambda} for dangling detection and entity alignment. Lambda features a GNN-based encoder called KEESA with a spectral contrastive learning loss for EA and a positive-unlabeled learning algorithm called iPULE for dangling detection. Our dangling detection module offers theoretical guarantees of unbiasedness, uniform deviation bounds, and convergence. Experimental results demonstrate that each component contributes to overall performances that are superior to baselines, even when baselines additionally exploit 30\% of dangling entities labeled for training.

PDF Details DOI
v2026.09.13