教师简介

张亚涛,工学博士,副教授,硕士生导师,在山东大学控制科学与工程学院获得工学博士学位,2015年至2021年期间在山东大学控制科学与工程学院开展博士后研究。2011年之前长期从事WLD和ACM两种型号地毯织机自动化项目研发,该成果获2010年度山东省科学技术进步二等奖(位3),2010年度日照市科学技术进步一等奖(位3),2011年之后从事智慧医疗,人工智能与医学交叉领域科学研究。2019年在澳大利亚国立大学访学。主持中国博士后面上基金 1 项,参与国家与省部级项目多项发表SCI、EI论文 30 余篇,其中一作与通讯作者 20 余篇,SCI论文 10 余篇,中科院2区以上论文 篇,中科院 top 2 篇。

研究方向

      方向1:智慧医疗与可穿戴监护数据分析(机器学习、深度学习)

方向2:人工智能与生理数据科学(机器学习、深度学习)

方向3:人工智能与数据质量评估(机器学习、深度学习)

方向4:智能信息处理与模式识别(机器学习、深度学习)

方向5:人工智能与图像识别(机器学习、深度学习)

学术兼职

1、山东省生物医学工程学会心电学工程专业委员会副主委,2021.12-2025.12

2、SCIE期刊Journal of Healthcare Engineering (SCIE, JCR 2, IF 2.682 )的special issue "Machine Learning for Physiological Data Analytics" 的               Leader guest editor。

3、Information science, Methods, applied soft method,  Artificial Intelligence Review,Knowledge-Based Systems IEEE access等期刊审稿人


       于不惑观古人兴感之由,有感于俯仰之间,或悔于当初哀在今日,或欣于所遇暂得于己,或放浪山水怡然自得;其际遇各异,修短随化;噫嘻,从古如斯,时耶命耶!然平公七十炳烛而读,犹未晚也,吾日中而读,信可乐哉,足矣,何求于事功?亦何哀于往昔?自此,所为只为兴之所至,以骋怀之乐。因作《弦歌》以记之,并与学生共勉:

大道无遮,弦歌不辍;且思且行,且行且歌;往者不及,来者可掇;踏歌行止,兴怀其所。——老张

教育经历
  • 1997-9 — 2001-7
    西北师范大学
    计算机及应用
    本科(学士)
  • 2005-9 — 2008-6
    山东大学
    计算机技术
    研究生(硕士)毕业
  • 2011-9 — 2015-12
    山东大学
    生物医学工程
    研究生(博士)毕业
  • 2016-1 — 2020-1
    山东大学
    生物医学工程
    研究生(博士后)
工作经历
  • 2001-7 — 至今
     山东大学(威海) 
研究概况

论文成果仅列一作与通讯作者):

1.     Bao, Z., Li, D., Jiang, S., Zhang, L., & Yatao Zhang*. Atrial Fibrillation Detection with Low Signal-to-Noise Ratio Data Using Artificial Features and Abstract Features[J]. Journal of Healthcare Engineering, 2023, 2023. (SCI JCR 2  期刊论文)

2.     Yatao Zhang*, Ma Z, Song J, et al. Comparing performance of iterative and non-iterative algorithms on various feature schemes for arrhythmia analysis[J]. Methods, 2022, 202: 144-151. (SCI JCR 2,中科院3区,中信所3区  期刊论文)

3.     Mei N, Wang H, Yatao Zhang*, et al. Classification of heart sounds based on quality assessment and wavelet scattering transform. Computers in Biology and Medicine, 2021, 137: 104814. (SCI JCR 1,中科院2区,中信所2区  期刊论文)

4.     Yatao Zhang*, Li J, Wei S, et al. Heartbeats classification using hybrid time-frequency analysis and transfer learning based on ResNet[J]. IEEE Journal of Biomedical and Health Informatics, 2021, 25(11): 4175-4184. (SCI JCR 1,中信所1区,中科院TOP  期刊论文)

5.     Li, X., Zhang, F., Sun, Z., Li, D., Kong, X., & Yatao Zhang*. Automatic heartbeat classification using S-shaped reconstruction and a squeeze-and-excitation residual network. Computers in Biology and Medicine, 2022, 140: 105108. (SCI JCR 1,中科院2区,中信所2区  期刊论文)

6.     Yin, J., Xiao, P., Li, J., Liu, Y., Yan, C., & Yatao Zhang*. Parameters analysis of sample entropy, permutation entropy and permutation ratio entropy for RR interval time series. Information Processing & Management, 2020, 57(5): 102283. (SCI JCR 1,SSCI  1区,中科院1区TOP,中信所2区  期刊论文)

7.     Chen Y, Wei S, Yatao Zhang*. Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network. Medical & Biological Engineering & Computing, 2020, 58: 2039-2047. (SCI  期刊论文)

8.     Yatao Zhang, * Ma Z, Dong W. Nonlinear quality indices based on a novel Lempel-Ziv complexity for assessing quality of multi-lead ECGs collected in real time[J]. Journal of Information Processing Systems, 2020, 16(2): 508-521. (EI  期刊论文)

9.     Ma, Z., Chen, B., Xu, Y., Li, Y., & Yatao Zhang*. Comparing Performance of Iterative and Non-Iterative Classifiers for 2-Lead ECGs on Multi-Feature Schemes[C]//2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2019: 1-6. (EI  会议论文)

10.  Yatao Zhang*, Liu C, Wei S, et al. Complexity analysis of physiological time series using a novel permutation-ratio entropy[J]. IEEE Access, 2018, 6: 67653-67664. (SCI  JCR 2区 期刊论文 )

11.  Yatao Zhang*, Wei S, Zhang L, et al. Comparing the performance of random forest, SVM and their variants for ECG quality assessment combined with nonlinear features[J]. Journal of medical and biological engineering, 2019, 39: 381-392. (SCI  期刊论文 )

12.  Wang Y, Wei S, Zhang S, Yatao Zhang*. Comparison of time-domain, frequency-domain and non-linear analysis for distinguishing congestive heart failure patients from normal sinus rhythm subjects[J]. Biomedical Signal Processing and Control, 2018, 42: 30-36. (SCI  期刊论文)

13.  Yatao Zhang*, Liu H, Wei S, et al. To improve performance of entropy methods for analyzing physiological signals using a novel symbolic approach[C]//2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2017: 1-6.  (EI  会议论文)

14.  Yatao Zhang*, Wei S. Normal and abnormal phonocardiogram classification based waveform and frequency features using LS-SVM[C]//2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2017: 2033-2037. (EI  会议论文)

15.  Yatao Zhang, Wei S, Zhang L, et al. A signal quality assessment method for mobile ECG using multiple features and fuzzy support vector machine[C]//2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016: 966-971. (EI  会议论文)

16.  Yatao Zhang, Wei S, Liu H, et al. A novel encoding Lempel–Ziv complexity algorithm for quantifying the irregularity of physiological time series[J]. Computer methods and programs in biomedicine, 2016, 133: 7-15. (SCI JCR 2,中科院2区,期刊论文)

17.  张亚涛*,魏守水.一种编码式Lempel-Ziv复杂度用于生理信号复杂度分析.生物医学工程学杂志.2016,33 (6):625634. (EI  期刊论文)

18.  Yatao Zhang, Wei S, Di Maria C, et al. Using Lempel-Ziv Complexity to Assess ECG Signal Quality. Journal mecidal biological engineering.2016,36 :625634 (SCI  期刊论文)

19.  Yatao Zhang, Shoushui W, Yutao L, et al. On applying multi-scale entropy to quality assessment of ECG collected via mobile phone[C]//2015 34th Chinese Control Conference (CCC). IEEE, 2015: 4767-4771. (EI  会议论文)

20.  Yatao Zhang, Wei, S., Long, Y., & Liu, C. (2015). Performance analysis of multiscale entropy for the assessment of ECG signal quality. Journal of Electrical and Computer Engineering, 2015, 31-31. (EI  期刊论文)

21.  Yatao ZhangChangzhi Wei, Feifei LiuShoushui Wei*,Chengyu Liu*ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix, 2014, 15(7), 564-574. (SCI  期刊论文)

专利
版权所有   ©山东大学 地址:中国山东省济南市山大南路27号 邮编:250100 
查号台:(86)-0531-88395114
值班电话:(86)-0531-88364731 建设维护:山东大学信息化工作办公室   
技术支持qq群:820336447