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Your search keyword '"Chaoming He"' showing total 13 results
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13 results on '"Chaoming He"'

1. Voice disorder classification using convolutional neural network based on deep transfer learning

Author

Xiangyu Peng, Huoyao Xu, Jie Liu, Junlang Wang, and Chaoming He

Subjects
Medicine, Science
Abstract

Abstract Voice disorders are very common in the global population. Many researchers have conducted research on the identification and classification of voice disorders based on machine learning. As a data-driven algorithm, machine learning requires a large number of samples for training. However, due to the sensitivity and particularity of medical data, it is difficult to obtain sufficient samples for model learning. To address this challenge, this paper proposes a pretrained OpenL3-SVM transfer learning framework for the automatic recognition of multi-class voice disorders. The framework combines a pre-trained convolutional neural network, OpenL3, and a support vector machine (SVM) classifier. The Mel spectrum of the given voice signal is first extracted and then input into the OpenL3 network to obtain high-level feature embedding. Considering the effects of redundant and negative high-dimensional features, model overfitting easily occurs. Therefore, linear local tangent space alignment (LLTSA) is used for feature dimension reduction. Finally, the obtained dimensionality reduction features are used to train the SVM for voice disorder classification. Fivefold cross-validation is used to verify the classification performance of the OpenL3-SVM. The experimental results show that OpenL3-SVM can effectively classify voice disorders automatically, and its performance exceeds that of the existing methods. With continuous improvements in research, it is expected to be considered as auxiliary diagnostic tool for physicians in the future.

Published
2023
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2. Lipid management in ischaemic stroke or transient ischaemic attack in China: result from China National Stroke Registry III

Author

Xu Zhang, Yongjun Wang, Kangning Chen, Lianbo Gao, Ye Tian, Xu Chen, Yi Guo, Benyan Luo, Yan Wang, Jian Wu, Xiaohong Li, Yong-Jun Wang, Qiang Dong, Huisheng Chen, Wei Wang, Zhengqi Lu, Lijie Ren, Runqing Wang, Juan Feng, Zhangyong Xia, Hao Li, Lihua Wang, Qingwei Zhao, Hong Wang, Ting Wang, Jin Zhou, Xia Meng, Jun Wu, Qing Li, Shizheng Wu, Ping Zhang, Li-Ping Liu, Yue-song Pan, Yi-long Wang, Ming Yu, Guohua Chen, Zhimin Wang, Jianhua Li, Yi Wang, Yi Yang, Xinsheng Han, Songdi Wu, Yongming Chen, Liang Zhao, Qiang Ma, Xiaoying Zhang, Mei Zhang, Limin Wang, Hui Liang, Liping Wang, Jun Zhou, Jie Lin, Yu Geng, Ning Wang, Yun Xu, Jinsheng Zeng, Huijuan Wang, Xueli Cai, Jia Yin, Jun Xu, Feng Qiu, Jun Zhao, Baojun Wang, Hainan Zhang, Zi-Xiao Li, Xing-Quan Zhao, Mingzhi Zhao, Yingchun Wu, Junling Zhang, Xuerong Qiu, Zhongping An, Wenjun Xue, Lili Zhao, Hongyan Li, Junfeng Shi, Tao Gong, Qi Fang, Liyan Liu, Jianfeng Wang, Guang Huang, Chun Wang, Yuwu Zhao, Lei Sheng, Lei Xia, Yan Fang, Xiaoxiang Peng, Yanling Wang, Yangmei Chen, Deqin Geng, Jinfeng Liu, Weirong Li, Yuqing Wei, Yu-Yuan Xu, Jilai Li, Maolin He, Quping Ouyang, Fengchun Yu, Chenlong Wang, Jinli Zhang, Wenqing Wu, Yaoyu Yu, Meiyun Zhang, Junyan Liu, Wanying Shi, Baoquan Lu, Lijun Geng, Shujuan Wang, Ruifang Liu, Fengli Zhao, Xinping Liu, Xuebing Sun, Tianyuan Li, Youming Wang, Xinxia He, Weiqiang Yuan, Ronghua Dou, Lihai Liu, Haisong Du, Cunrui Wang, Xiaofei Chen, Fengyun Hu, Fanping He, Xingchen Wang, Hongjin Li, Mingzong Yan, Guiru Zhang, Yunlin Liu, Yuhui Han, Xianghong Meng, Hongtian Zhang, Congmin Ma, Gexia Liu, Wenxu Zheng, Haitao Chi, Hongbo Xiao, Lijun Xiao, Guozhong Li, Yulan Zhu, Yindong Yang, Xuhai Gong, Qunhui Liu, Shiping Gong, Hongbin Zhou, Haipeng Li, Yong You, Jinsheng Lin, Heqing Zhao, Aixia Zhuang, Kaifu Ke, Zhengxie Dong, Guiyun Cui, Liangqun Rong, Yi Wu, Weiguo Tang, Yanjiang Wang, Wenguang Bu, Xiaohua Cheng, Jifu Cai, Lvli Li, Jianping Niu, Liping Zhan, Runxiu Zhu, Yanhui Du, Yongxia Wen, Yongcai Qu, Yuncheng Wu, Jianren Liu, Qingke Bai, Chaoming He, Weiwen Qiu, Shufang Yao, Xuwen Sun, Ligong Gao, Xianglin Chen, Qiuyan Shi, Zhaochen Li, Tianbao Chen, SuYun Yang, Yazhou Han, Xinxiao Wu, Beihai Jiang, Lizhong Li, Weidong Lou, Weiming Lan, Aihu Zheng, Qifu Bai, Lifang Luan, Liqing Yan, Yanxia Wang, Xuerong Huang, Xiangting Chai, Yanshu Liu, Liangjun You, Hongqin Yang, Dongfang Li, Linying Gui, Aisheng Wu, Dengling Wang, Qinghua Zhang, Yunhong He, Ruiyou Guo, Jijun Teng, Ping Lou, Wei-Qi Chen, Meng-Xing Wang, Yu’an Zou, An’ding Xu, Yinzhou Wang Li Pan, Li’e Wu, Yanru Zhao Li Liu, and Xiaoqian Shen

Subjects
Medicine
Abstract

Objectives The aims of the study were to assess the management of low-density lipoprotein cholesterol (LDL-C) and the goal achievement, as well as to investigate the association between baseline LDL-C level, lipid-lowering treatment (LLT), and stroke recurrence in patients with ischaemic stroke or transient ischaemic attack (TIA).Design Our study was a post hoc analysis of the Third China National Stroke Registry (CNSR-III).Setting We derived data from the CNSR-III - a nationwide clinical registry of ischaemic stroke and TIA based on 201 participating hospitals in mainland China.Participants 15,166 patients were included in this study with demographic characteristics, etiology, imaging, and biological markers from August 2015 to March 2018.Primary and secondary outcome measures The primary outcome was a new stroke, LDL-C goal (LDL-C

Published
2023
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3. Prognosis and antiplatelet therapy of small single subcortical infarcts in penetrating artery territory: a post hoc analysis of the Third China National Stroke Registry

Author

Xu Zhang, Yongjun Wang, Kangning Chen, Lianbo Gao, Ye Tian, Xu Chen, Yi Guo, Benyan Luo, Yan Wang, Jian Wu, Xiaohong Li, An-Xin Wang, Yong-Jun Wang, Qiang Dong, Huisheng Chen, Wei Wang, Zhengqi Lu, Jing Jing, Lijie Ren, Runqing Wang, Juan Feng, Zhangyong Xia, Hao Li, Lihua Wang, Qingwei Zhao, Hong Wang, Ting Wang, Jin Zhou, Xia Meng, Jun Wu, Qing Li, Shizheng Wu, Ping Zhang, Li-Ping Liu, Yi-long Wang, Ming Yu, Guohua Chen, Zhimin Wang, Jianhua Li, Yi Wang, Yi Yang, Xinsheng Han, Songdi Wu, Yongming Chen, Liang Zhao, Qiang Ma, Xiaoying Zhang, Mei Zhang, Limin Wang, Hui Liang, Liping Wang, Jun Zhou, Jie Lin, Yu Geng, Ning Wang, Yun Xu, Jinsheng Zeng, Huijuan Wang, Xueli Cai, Jia Yin, Jun Xu, Feng Qiu, Jun Zhao, Baojun Wang, Hainan Zhang, Zi-Xiao Li, Xing-Quan Zhao, Mingzhi Zhao, Yingchun Wu, Junling Zhang, Xuerong Qiu, Zhongping An, Wenjun Xue, Lili Zhao, Hongyan Li, Junfeng Shi, Mingzhen Li, Tao Gong, Qi Fang, Liyan Liu, Jianfeng Wang, Guang Huang, Chun Wang, Yuwu Zhao, Lei Sheng, Lei Xia, Yan Fang, Xiaoxiang Peng, Yanling Wang, Yangmei Chen, Deqin Geng, Jinfeng Liu, Weirong Li, Yuqing Wei, Yu-Yuan Xu, Yi-Jun Zhang, Jilai Li, Maolin He, Quping Ouyang, Fengchun Yu, Chenlong Wang, Jinli Zhang, Wenqing Wu, Yaoyu Yu, Meiyun Zhang, Junyan Liu, Wanying Shi, Baoquan Lu, Lijun Geng, Shujuan Wang, Ruifang Liu, Fengli Zhao, Xinping Liu, Xuebing Sun, Tianyuan Li, Youming Wang, Xinxia He, Weiqiang Yuan, Ronghua Dou, Lihai Liu, Haisong Du, Cunrui Wang, Yu'an Zou, Xiaofei Chen, Fengyun Hu, Fanping He, Xingchen Wang, Hongjin Li, Mingzong Yan, Guiru Zhang, Yunlin Liu, Yuhui Han, Xianghong Meng, Hongtian Zhang, Congmin Ma, Gexia Liu, Wenxu Zheng, Haitao Chi, Hongbo Xiao, Lijun Xiao, Guozhong Li, Yulan Zhu, Yindong Yang, Xuhai Gong, Qunhui Liu, Shiping Gong, Hongbin Zhou, Haipeng Li, Yong You, Jinsheng Lin, Heqing Zhao, Aixia Zhuang, Kaifu Ke, Zhengxie Dong, Guiyun Cui, Liangqun Rong, Yi Wu, Weiguo Tang, Yanjiang Wang, Wenguang Bu, Xiaohua Cheng, An'ding Xu, Jifu Cai, Lvli Li, Li Pan; Yinzhou Wang, Jianping Niu, Liping Zhan, Li'e Wu, Li Liu; Yanru Zhao, Runxiu Zhu, Yanhui Du, Yongxia Wen, Yongcai Qu, Yuncheng Wu, Jianren Liu, Qingke Bai, Chaoming He, Weiwen Qiu, Shufang Yao, Xuwen Sun, Ligong Gao, Xianglin Chen, Qiuyan Shi, Zhaochen Li, Tianbao Chen, SuYun Yang, Yazhou Han, Xinxiao Wu, Beihai Jiang, Lizhong Li, Weidong Lou, Weiming Lan, Aihu Zheng, Qifu Bai, Lifang Luan, Liqing Yan, Yanxia Wang, Xuerong Huang, Xiangting Chai, Yanshu Liu, Liangjun You, Hongqin Yang, Dongfang Li, Linying Gui, Aisheng Wu, Jianling Zhang, Dengling Wang, Qinghua Zhang, Yunhong He, Ruiyou Guo, Jijun Teng, and Ping Lou

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Background Small single subcortical infarction (SSSI) may be classified as parent artery disease-related or only branch involved according to the stenosis of parent artery. The study aimed to evaluate short-term and long-term prognoses and the effectiveness of antiplatelet therapy in SSSI.Methods We prospectively enrolled 2890 patients with SSSI from the Third China National Stroke Registry (CNSR-III) database from August 2015 to March 2018. We assessed clinical outcomes and antiplatelet treatment effects in patients with SSSI with and without parent artery stenosis (PAS) identified by magnetic resonance angiography.Results Among 2890 patients with SSSI in the perforator territory of the middle cerebral artery and the basilar artery, there were 680 (23.53%) patients with PAS and 2210 (76.47%) patients without PAS, respectively. After adjusting for potential confounders, the PAS group had a greater initial stroke severity (OR 1.262, 95% CI 1.058 to 1.505; p=0.0097) and a higher risk of ischaemic stroke recurrence at 3 months (OR 2.266, 95% CI 1.631 to 3.149; p

Published
2022
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9. Pathological voice classification based on multi-domain features and deep hierarchical extreme learning machine

Author

Junlang Wang, Huoyao Xu, Xiangyu Peng, Jie Liu, and Chaoming He

Subjects
Acoustics and Ultrasonics, Arts and Humanities (miscellaneous)
Abstract

The intelligent data-driven screening of pathological voice signals is a non-invasive and real-time tool for computer-aided diagnosis that has attracted increasing attention from researchers and clinicians. In this paper, the authors propose multi-domain features and the hierarchical extreme learning machine (H-ELM) for the automatic identification of voice disorders. A sufficient number of sensitive features are first extracted from the original voice signal through multi-domain feature extraction (i.e., features of the time domain and the sample entropy based on ensemble empirical mode decomposition and gammatone frequency cepstral coefficients). To eliminate redundancy in high-dimensional features, neighborhood component analysis is then applied to filter out sensitive features from the high-dimensional feature vectors to improve the efficiency of network training and reduce overfitting. The sensitive features thus obtained are then used to train the H-ELM for pathological voice classification. The results of the experiments showed that the sensitivity, specificity, F1 score, and accuracy of the H-ELM were 99.37%, 98.61%, 99.37%, and 98.99%, respectively. Therefore, the proposed method is feasible for the initial classification of pathological voice signals.

Published
2023
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11. A bagging-strategy based heterogeneous ensemble deep neural networks approach for the multiple components fault diagnosis of hydraulic systems

Author

Junlang Wang, Huoyao Xu, Jie Liu, Xiangyu Peng, and Chaoming He

Subjects
Applied Mathematics, Instrumentation, Engineering (miscellaneous)
Abstract

Hydraulic systems faults have the characteristics of being highly concealed and unclear. Due to the characteristics of the complex vibration transmission mechanism and strong nonlinear time-varying signals in hydraulic systems, it is extremely difficult to achieve fault diagnosis for hydraulic systems. Different components of the system can fail individually or simultaneously. Signal processing faces the problem of coupling between multi-component faults, which makes it more difficult to realise multi-component fault diagnosis. On the one hand, existing techniques rely on hand-designed features and only use a traditional single shallow machine model as the base classifier, and these do not have the ability to self-learn meaningful features. On the other hand, the diagnostic performance of a single base classifier sometimes does not meet engineering requirements. To handle the above problems, a bagging strategy based heterogeneous ensemble deep neural networks (DNNs) approach is proposed for the multiple components fault diagnosis of hydraulic systems. First, Pearson correlation coefficient and neighbourhood component analysis are developed for data channel selection and feature dimensionality reduction. Second, two distinct DNNs are constructed as base learners: a stacked sparse autoencoder and a deep hierarchical extreme-learning machine. Finally, a bagging strategy is adopted to integrate different DNNs to obtain robust diagnostic results. The results from this experiment demonstrate that the proposed method can precisely diagnose hydraulic system faults compared with comparative methods.

Published
2023
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