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Supervisor of Doctorate Candidates

Supervisor of Master's Candidates


Administrative Position:系主任

Education Level:With Certificate of Graduation for Doctorate Study

Contact Information:024-83683200



Alma Mater:哈尔滨工业大学


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Current position: Home >> Scientific Research >> Research Field
Research Field

1.Estimation of Central Aortic Pulse Wave Based on Multi-channel Blind System Identification

    Central Aortic Pulse wave (CAP) is very important in the prediction and treatment of Cardiovascular System (CVS) diseases because it provides tremendous physiological and pathological information of human CVS. In order to monitor CVS non-invasively and effectively, a new method based on Multi-channel Blind System Identification (MBSI) was proposed in this project to reconstruct CAP from two channel noninvasive Peripheral Aortic Pulse wave (PAP).

    A "Gray box" model of CVS was designed based on two-channel "T-tube" model, providing the theoretic foundation for MBSI algorithm. Effectiveness of the MBSI algorithm can be verified by the data from the “T-tube?model. In this project, the equivalence of the IIR and high order FIR function has been proved. Based on this approximation, this project successfully converts the IIR model into a FIR model, which greatly simplifies the MBSI algorithm. Verification results show that MBSI algorithm can estimate CAP stably and accurately. The overall error rate, error of systolic pressures and error of diastolic pressures produce by the MBSI algorithm are less than 7.2%, 4.6 mmHg, 4.2 mmHg respectively.

    2.Cardiovascular Primary Parameters Analysis Based on Radial Pulse Wave

    A large number of clinical results have confirmed that pulse wave contains rich information on physiology and pathology. However, noises will be introduced during the acquisition of pulse wave signal, which can influence the accuracy of the estimation of cardiovascular parameters. In this project, two parts are studied, including the processing of pulse wave and the calculation of cardiovascular parameters.

    Firstly, we proposed an adaptive denoising method based on Empirical Mode Decomposition (EMD), combining wavelet threshold method and the characteristics of noise distribution in pulse signal. In the same way, baseline drift in pulse wave was removed effectively. Then several characteristics points used to calculate cardiovascular parameters were extracted based on the threshold method. Finally, the influence factors of pulse wave such as contact pressure, tilting degrees and time duration were analyzed.

    Secondly, cardiovascular parameters (cardiac output, peripheral resistance etc.) based on K value method were calculated and the performances of the estimation were evaluated. They are almost the same under the normal pulse pressure, but they have a big difference under the larger pulse pressure. An improved formula of Cardiac Output (CO) through multiple linear regression analysis was proposed. In order to test the validity of this method, we compared the revised CO with the CO measured by the Fick Thermal-dilution method. The relative error was within 5%.

  1. 3.Detection of R-wave in Dynamic ECG Based on Quality Analysis

  • In our work, power ratio in frequency domain is used to classify EMG in different types with a long window size; the threshold-cross rate is used to classify the static ECG and dynamic ECG in a short window; the short time energy is used to classify the state of holding objects and swing arms. Combined with the results of frequency domain and time domain, the classification is reliable in short time. Dmey wavelet transform is used for dynamic ECG of holding objects in multi-scale and multi-resolution. The tendency of ECG signals could be obtained by the EMD method which could decompose signals into a limited number of Intrinsic Mode Functions (IMF). The multi-scale analysis of IMFs is used to analyze the ECG of swing arms.

    The proposed adaptive R-wave detection method has been validated using the data collected in our laboratory. Experimental results demonstrate that the proposed method is significantly applicable in dynamic ECG.


    4.Comprehensive Analysis of Non-invasive Cardiovascular Videos, Images and Signals

    Early diagnosis of cardiovascular disease and prediction of cardiovascular risk based on the fusion of non-invasive cardiovascular images and non-invasive ambulatory physiological signals was proposed. Magnetic resonance imaging (MRI) has been the preferred modality for evaluation of left ventricular function and structure, whereas computed tomography (CT) has been used extensively for coronary artery calcification analysis. The analysis of volume and pressure pulse waves of peripheral arteries can be used to differentiate the functions and properties of peripheral arteries. However, in clinical practice, the analysis of these images and pulse wave signals is performed alone. These information needs to be fused in order to disclose the mechanism of the occurrence and development of cardiovascular diseases.


  1. 5.Optimization on RF Radiation Effect and Signal Efficiency of Wireless Medical Devices

  2. Recent advances in medical, computing and information technologies have amplified the desire and possibility to develop and apply advanced wireless medical devices to improve medical and healthcare service quality, possibility and accessibility. Examples include wireless capsule endoscopes for painless examination of gastrointestinal tract and body area network (BAN) of wireless bio-sensors for patient monitoring in hospital clinic or home environments. Current efforts on wireless medical devices are concentrated on the studies of the device design, reliable communications and innovative applications. During our research on wireless medical devices and our literature search, we found that little work has been reported on systematic studies of RF radiation effect and RF signal efficiency of wireless medical devices in interaction with human body, which is a fundamentally important issue for wireless medical devices, especially for wearable and in-vivo wireless medical devices. As a result, we propose that in this research we will forge the complementary expertise of biomedical engineering, RF communications and clinical medicine to systematically study the RF radiation effect and RF signal efficiency of wearable wireless medical devices such as BAN of bio-sensors on human body and in-vivo wireless medical devices such as the wireless capsule endoscopes from inside the human body. Our overall objective is to carry out a systematic study on this topic and establish a set of design reference metrics on RF radiation effect and RF signal efficiency of wireless medical devices in interaction with human body through thorough theoretical investigations and extensive experimental studies.