从事新型医疗电子及医学人工智能技术研究，目前主要研究方向如下：

1.柔性超声阵列技术及临床监测应用研究：聚焦柔性超声阵列的设计与研发，重点攻克其在血流动力学精准评估、组织病变可视化成像领域的核心监测技术，探索该技术在微创诊疗、动态生理指标追踪中的临床应用。

2.非侵入式超声脑机接口（BCI）技术研发：以超声为核心交互媒介，开展非侵入式脑机接口的信号采集、编码与解码技术研究，突破传统侵入式方案的安全性局限，推动其在神经康复、意识障碍评估等领域的应用。

3.无感式体征监测技术与系统开发：基于毫米波雷达、压电陶瓷等新型传感技术，研发可实现生理体征（呼吸、心率、睡眠结构等）无感采集、实时分析的监测系统，兼顾监测精度与舒适性，适配居家健康管理、临床监护等场景。

4.多模态医学大模型构建与临床应用研究：融合超声、影像、生理参数、临床文本等多源医学数据，构建具备数据融合、智能诊断、预后预测能力的多模态医学大模型，探索其在辅助临床决策、疾病早期筛查等临床应用。

080903 微电子学与固体电子学

0809Z1 生物电子学

欢迎电子、自动化、计算机等信息类专业优秀学生申报！

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2.Li S, Zhao D, Wang Z, et al. Fusing time-and frequency-domain information for effort-independent lung function evaluation using oscillometry[J]. Information Fusion, 2026: 104147.

3.Wang B, Feng Y, Wang P, et al. M3-UNet: Multi-frequency, Multi-scale and Multi-task U-Net for Intima-Media Complex Segmentation[J]. Knowledge-Based Systems, 2026: 115298.

4.Yao Y, Wang P, Bai Z, et al. Privacy-Preserving Unobtrusive Fall Detection for Older Adults: A Highly Generalized Deep Anomaly Detection Model[J]. Tsinghua Science and Technology, 2026, 31(3): 1802-1818.

5.Li S, Wang P, Yu X, et al. GANet: A Convolution Neural Network with Parallel Convolutions and Graph-Based Attention Mechanism for Event-Related Potential Classification in Brain-Computer Interface Task[J]. Tsinghua Science and Technology, 2026, 31(2): 920-931.

6.Li S, Wang P, Xue J, et al. Non-contact, real-time monitoring of patient thoracic cross-sectional area during CPR based on depth camera[J]. Biomedical Signal Processing and Control, 2026, 112: 108517.

7.He G, Wu W, Zhang H, et al. DRMTrack: An Extended Distributed Millimeter-Wave Radar Framework for Indoor Multi-Target Human Trajectory Tracking[J]. IEEE Internet of Things Journal, 2025.

8.Yu X, Wang P, Wang Y, et al. LSCS-BDA: A domain adaptation method for non-contact cross-subject emotion recognition based on ballistocardiogram[J]. Tsinghua Science and Technology, 2025.

9.Yao Y, Zhang H, Xia P, et al. mm2Sleep: Highly generalized dual-person sleep posture recognition using FMCW radar[J]. Biomedical Signal Processing and Control, 2025, 103: 107430.

10.Bai Z, Geng F, Zhang H, et al. Non-contact blood pressure estimation using FMCW radar: A two-stream approach focused on central arterial activity[J]. Biomedical Signal Processing and Control, 2025, 106: 107718.

11.Xia P, Bai Z, Yao Y, et al. Advanced deep neural network with unified feature-aware and label embedding for multi-label arrhythmias classification[J]. Tsinghua Science and Technology, 2024, 30(3): 1251-1269.

12.Wu P, Bai Z, Xia P, et al. Wearable Continuous Blood Pressure Monitoring Based on Pulsatile Cycle Volume Adjustment Method[J]. Tsinghua Science and Technology, 2024, 30(2): 650-669.

13.Shao Y, Huang B, Du L, et al. Reliable automatic sleep stage classification based on hybrid intelligence[J]. Computers in Biology and Medicine, 2024, 173: 108314.

14.Zou Y, Yu X, Li S, et al. A generalizable and robust deep learning method for atrial fibrillation detection from long-term electrocardiogram[J]. Biomedical Signal Processing and Control, 2024, 90: 105797.

15.Huang B, Shao Y, Zhang H, et al. Design and implementation of a silent speech recognition system based on sEMG signals: A neural network approach[J]. Biomedical Signal Processing and Control, 2024, 92: 106052.

16.Yao Y, Zhang H, Bai Z, et al. Unobtrusive High-Generalization Fall Detection: A Domain-Generalization Framework[J]. IEEE Sensors Journal, 2024.

17.Xia, P., Zhang, H., Wang, P., & Fang, Z.. Spatiotemporal 3-D Variations Modeling With Self-Attention for Multilabel ECG Classification. IEEE Sensors Journal, 24, 18710-18724.

18.Zhang H, Jian P, Yao Y, et al. Radar-Beat: Contactless beat-by-beat heart rate monitoring for life scenes[J]. Biomedical Signal Processing and Control, 2023, 86: 105360.

19.Wu P, Bai Z, Xu L, et al. New approaches for rapid setpoint determination and uninterrupted tracking in non-invasive continuous blood pressure monitoring based on volume-clamp method[J]. Biomedical Signal Processing and Control, 2023, 86: 105305.

20.Geng F, Bai Z, Zhang H, et al. Contactless and Continuous Blood Pressure Measurement According to caPTT Obtained from Millimeter Wave Radar[J]. Measurement, 2023: 113151.

21.Yao Y, Zhang H, Liu C, et al. Unsupervised Learning-Based Unobtrusive Fall Detection Using FMCW Radar[J]. IEEE Internet of Things Journal, 2023. doi: 10.1109/JIOT.2023.3301887.

22.Yao Y, Zhang H, Xia P, et al. mmSignature: Semi-supervised human identification system based on millimeter wave radar[J]. Engineering Applications of Artificial Intelligence, 2023, 126: 106939.

23.Yan B, Wang P, Du L, et al. mmGesture: Semi-supervised gesture recognition system using mmWave radar[J]. Expert Systems with Applications, 2023, 213: 119042.

24.He Z, Tang M, Wang P, et al. Cross-scenario automatic sleep stage classification using transfer learning and single-channel EEG[J]. Biomedical Signal Processing and Control, 2023, 81: 104501.

25.Yao Y, Liu C, Zhang H, et al. Fall detection system using millimeter-wave radar based on neural network and information fusion[J]. IEEE Internet of Things Journal, 2022, 9(21): 21038-21050.

26.Yan B, Zhang H, Yao Y, et al. Heart signatures: Open-set person identification based on cardiac radar signals[J]. Biomedical Signal Processing and Control, 2022, 72: 103306.

27.He Z, Du L, Wang P, et al. Single-channel EEG sleep staging based on data augmentation and cross-subject discrepancy alleviation[J]. Computers in Biology and Medicine, 2022, 149: 106044.

28.He Z, Zhang H, Chen X, et al. Hemorrhagic risk prediction in coronary artery disease patients based on photoplethysmography and machine learning[J]. Scientific Reports, 2022, 12(1): 19190.

29.Tang M, Zhang Z, He Z, et al. Deep adaptation network for subject-specific sleep stage classification based on a single-lead ECG[J]. Biomedical Signal Processing and Control, 2022, 75: 103548.

30.He Z, Du L, Du S, et al. Machine learning for the early prediction of head-up tilt testing outcome[J]. Biomedical Signal Processing and Control, 2021, 69: 102904.

31.Yan B, Zhang H, Yao Y, et al. Heart signatures: Open-set person identification based on cardiac radar signals[J]. Biomedical Signal Processing and Control, 2022, 72: 103306.

32.Zhou R, Wang C, Zhang P, et al. ECG-based biometric under different psychological stress states[J]. Computer Methods and Programs in Biomedicine, 2021, 202: 106005.

33.Qu X, Huang Z, Xu Y, et al. Cheetah: An accurate assessment mechanism and a high-throughput acceleration architecture oriented toward resource efficiency[J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, 40(5): 878-891.

34.Wang C, Chen X, Du L, et al. Comparison of machine learning algorithms for the identification of acute exacerbations in chronic obstructive pulmonary disease[J]. Computer methods and programs in biomedicine, 2020, 188: 105267.

35.He Z, Du L, Zhang P, et al. Early sepsis prediction using ensemble learning with deep features and artificial features extracted from clinical electronic health records[J]. Critical care medicine, 2020, 48(12): e1337-e1342.

方震，安徽合肥人，博士/研究员/博导，研究室主任，中国科学院大学教授，中国科学院王宽诚人才奖，中国医学科学院创新单元主任，中国医学科学院医学与健康科技创新工程首席专家。国家重点研发计划战略性专项领域专家，基金委、科技部、农村农业部评审专家，互联网+医疗健康工作委员会副主任委员、医学装备人工智能联盟皮肤科委员会副主任委员、中关村标准化协会智能电气分会副主任委员、移动医疗专业委员会常委等兼职。

从事新型医疗电子和医学人工智能技术研究，完成国家级科研项目30余项，在可穿戴式技术、新原理新方法医疗电子技术和医学人工智能等研究领域取得多项创新性研究成果，其代表性成果如下：

1. 在国内率先开展压电陶瓷、毫米波雷达等无感式监测技术研究，基于人工智能技术开展无感式技术在心率失常、脑卒中、慢阻肺、呼吸暂停等领域的数字疗法研究，以及基于无感或穿戴技术的精神压力、情绪和疲劳度实时评估技术；

2. 完成基于恒定容积法及容积控制技术的无创连续血压及血流动力学检测技术的国内首台套设备研制，并在此基础上开展多模态信息智能麻醉态势感知系统和低血压预测等技术研究；

3. 研制新型容积二氧化碳图设备、运动肺功能设备、呼吸机和高流量经鼻氧疗一体化设备，以及基于多模态临床数据的肺功能评估模型，构建一体化呼吸慢病监测、评估和治疗闭环管理系统；

4. 研制多模态柔性/刚性高密度超声阵列电子皮肤，实现多脏器、血管等人体组织的穿戴式连续高分辨率成像，血管的多普勒血流速流量监测，以及灌注实时监测技术等。

发表SCI/EI检索论文200余篇，授权国家发明专利50余项。获第十届国际发明展览会“发明创业奖”金奖（2018年），产学研促进会创新成果一等奖（2020年），军事科学技术进步奖一等奖（2022年），国家科学技术进步奖二等奖（2024年）等奖项。参与HealthCom、COMPSAC、APCOT、ICIA、ICMAN等医疗电子、计算机、传感器相关领域国际会议筹备，并做会议特邀报告和主持会议。担任Computers in industry、Microsystems & Nanoengineering等SCI期刊的专刊主编。