no code implementations • 22 Oct 2023 • Hongxiang Gao, Xiangyao Wang, Zhenghua Chen, Min Wu, Zhipeng Cai, Lulu Zhao, Jianqing Li, Chengyu Liu
To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena.
no code implementations • 10 Apr 2023 • Hongxiang Gao, Xingyao Wang, Zhenghua Chen, Min Wu, Jianqing Li, Chengyu Liu
From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.
no code implementations • 19 Sep 2022 • Xingyao Wang, Yuwen Li, Hongxiang Gao, Xianghong Cheng, Jianqing Li, Chengyu Liu
To address this issue, we establish a structural causal model as the foundation to customize the intervention approaches on Am and Ar, respectively.
1 code implementation • 19 Aug 2022 • Chengyu Liu, Wei Wang
We showed that this linear model could predict gene expression levels using promoter sequences with a performance comparable to deep neural network models.
no code implementations • 23 Aug 2021 • Yongming Li, Chengyu Liu, Pin Wang, Hehua Zhang, Anhai Wei
The results show that the proposed algorithm is effective.
1 code implementation • 28 Apr 2021 • Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan
The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.
Ranked #1 on Automatic Sleep Stage Classification on Sleep-EDF
1 code implementation • Computing in Cardiology 2020 • Erick A. Perez Alday, Annie Gu, Amit Shah, Chad Robichaux, An-Kwok Ian Wong, Chengyu Liu, Feifei Liu, Ali Bahrami Rad, Andoni Elola, Salman Seyedi, Qiao Li, ASHISH SHARMA, Gari D. Clifford, Matthew A. Reyna
Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry.
no code implementations • 9 May 2020 • Xingyao Wang, Chengyu Liu, Yuwen Li, Xianghong Cheng, Jianqing Li, Gari D. Clifford
Moreover, the TFAN-based method achieved an overall F1 score of 99. 2%, 94. 4%, 91. 4% on LEVEL-I, -II and -III data respectively, compared to 98. 4%, 88. 54% and 79. 80% for the current state-of-the-art method.
no code implementations • 26 Mar 2019 • Dongrui Wu, Feifei Liu, Chengyu Liu
Moreover, active learning can be used to optimally select a few trials from a new subject to label, based on which a stacking ensemble regression model can be trained to aggregate the base estimators.
no code implementations • JMIHI 2018 • Feifei Liu, Chengyu Liu, Lina Zhao, Xiangyu Zhang, Xiaoling Wu, Xiaoyan Xu, Yulin Liu, Caiyun Ma, Shoushui Wei, Zhiqiang He, Jianqing Li, Eddie Ng Yin Kwee
Over the past few decades, methods for classification and detection of rhythm or morphology abnormalities in ECG signals have been widely studied.
1 code implementation • 30 Oct 2017 • Chengyu Liu, Wei Wang
We demonstrate its high prediction accuracy and sensitivity through the task of predictive feature selection on a simulated dataset and the application of predicting open chromatin sites in the human genome.