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 6 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、教育部全国研究生教育评估监测专家库专家（202401——202412），聘书编号：XWZX24104220089
3、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。
4、Information science, Methods, applied soft method, Artificial Intelligence Review，Knowledge-Based Systems IEEE access等期刊审稿人
吾于不惑观古人兴感之由，有感于俯仰之间，或欣于所遇暂得于己，或放浪山水怡然自得，或悔于既往颓惰今日；其际遇各异，修短随化；凡此种种，不过因得而兴，或为失而废耳。噫嘻，从古如斯，时耶命耶！
尝读师旷语于平公，七十炳烛而读，犹未晚也。吾日中而读，信可乐哉，足矣！何哀于少年之不永，何劳于事功之不待？自此所为，只慰初心，兴至骋怀而已。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
论文成果（仅列一作与通讯作者，期刊论文）：
1. Shipeng Jiang, Dong Li, Yatao Zhang*. A Deep Neural Network Using Multi-model and Multi-scale for Arrhythmia Classification, Biomedical Signal Processing and Control, 2023，Accepted (SCI JCR Q2，中科院2区，期刊论文，独立通讯)
2. YongJian Li, Yatao Zhang*, Diagnosis of Atrial Fibrillation Using Self-complementary Attentional Convolutional Neural Network, Computer methods and programs in biomedicine, 2023，Available online (SCI JCR Q1，中科院2区，中信所2区，期刊论文，共同通讯)
3. 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)
4. 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)
5. 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)
6. 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)
7. 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)
8. 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)
9. 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)
10. 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)
11. 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 )
12. 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 )
13. 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)
14. 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)
15. 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):625–634. (EI 期刊论文)
16. Yatao Zhang, Wei S, Di Maria C, et al. Using Lempel-Ziv Complexity to Assess ECG Signal Quality. Journal mecidal biological engineering.2016,36 :625–634 (SCI 期刊论文)
17. 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)
18. Yatao Zhang，Changzhi Wei, Feifei Liu，Shoushui 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)
