Based on the imaging photoplethysmography (iPPG) and blind source separation (BSS) theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i.e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis (ICA) theory and joint approximate diagonalization of eigenmatrices (JADE) algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of Bland-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2.06 beat/min. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the remote and non-contact measurement of multi-parameter physiological measurements.
Heart rate is a crucial indicator of human health with significant physiological importance. Traditional contact methods for measuring heart rate, such as electrocardiograph or wristbands, may not always meet the need for convenient health monitoring. Remote photoplethysmography (rPPG) provides a non-contact method for measuring heart rate and other physiological indicators by analyzing blood volume pulse signals. This approach is non-invasive, does not require direct contact, and allows for long-term healthcare monitoring. Deep learning has emerged as a powerful tool for processing complex image and video data, and has been increasingly employed to extract heart rate signals remotely. This article reviewed the latest research advancements in rPPG-based heart rate measurement using deep learning, summarized available public datasets, and explored future research directions and potential advancements in non-contact heart rate measurement.
The velocity of blood in vessels is an important indicator that reflects the microcirculatory status. The core of the measurement technology, which is based on spatiotemporal (ST) image, is to map the cell motion trace to the two-dimensional ST image, and transfer the measurement of flow velocity to the detection of trace orientation in ST image. This paper proposes a trace orientation measurement algorithm is based on Randomized Hough Transformation and projection transformation, and it is able to estimate trace orientation and flow velocity in noisy ST images. Experiments showed that the agreement between the results by manual and by the proposed algorithm reached over 90%.
目的:探讨上颌单颌拔牙矫治的的临床效果、适应症。方法:选择18例安氏II类1分类错牙合患者采用上颌单颌拔牙模式矫治,对矫治前后X线头影测量数据进行对比分析。结果:① U1-SN增加4.84°,L1-MP增加2.78°,前牙覆盖减少4.06 mm,覆牙合减少3.39 mm,上唇突度减小1.39 mm、鼻唇角增大3.06°,Z角增大3.22°。② 18例患者矫治后侧貌明显改善,前牙覆牙合覆盖关系正常,磨牙呈完全远中关系,尖牙为中性关系。结论:上颌单颌拔牙模式适用于下唇突度小的轻度拥挤的轻度骨性和牙源性的Ⅱ类1分类错牙合。
The validated finite element head model (FEHM) of a 3-year-old child, a 6-year-old child and a 50th percentile adult were used to investigate the effects of head dimension and material parameters of brain tissues on the head rotational responses based on experimental design. Results showed that the effects of head dimension and directions of rotation on the head rotational responses were not significant under the same rotational loading condition, and the same results appeared in the viscoelastic material parameters of brain tissues. However, the head rotational responses were most sensitive to the shear modulus (G) of brain tissues relative to decay constant (β) and bulk modulus (K). Therefore, the selection of material parameters of brain tissues is most important to the accuracy of simulation results, especially in the study of brain injury criterion under the rotational loading conditions.
Stage ⅠA lung adenocarcinoma presented as ground glass dominant on thin-section high-resolution CT scan is a special subtype of lung cancer. The characteristics of this subtype are quite different from the other patients, which presented as lower malignancy and better prognosis. Clinical, pathological and imaging studies have revealed that the proportion of the solid component in part-solid ground glass nodule is closely related with the pathological type and the prognosis of lung cancer. The methods for the assessment of the solid components in the ground glass nodule can be classified into three types, including subjective assessment, two dimensional measurement and three dimensional measurement. This review summarized the advantages and the limitations of these three methods. We also reviewed the clinical application of these techniques.
Measurement properties studies of patient-reported outcome measures (PROMs) aims to validate the measurement properties of PROMs. In the process of designing and statistical analysis of these measurement properties studies, bias will occur if there are any defects, which will affect the quality of PROMs. Therefore, the COSMIN (consensus-based standards for the selection of health measurement instruments) team has developed the COSMIN risk of bias (COSMIN-RoB) checklist to evaluate risk of bias of studies on measurement properties of PROMs. The checklist can be used to develop systematic reviews of PROMs measurement properties, and for PROMs developers, it can also be used to guide the research design in the measurement tool development process for reducing bias. At present, similar assessment tools are lacking in China. Therefore, this article aims to introduce the primary contents of COSMIN-RoB checklist and to interpret how to evaluate risk of bias of the internal structure studies of PROMs with examples.
Forty two eyes of 37 coses of age-related macular degeneration with subretinal neovascularization were followed up for more than 5 yeors.Vision, fundus change and fluorescein angiograms were with electrical image analysis system. （Chin J Ocul Fundus Dis,1994,10:1-3）
Repeated measurement quantitative data is a common data type in clinical studies, and is frequently utilized to assess the therapeutic effects of the intervention measures at a single time point in clinical trials. This study clarifies the concepts and calculation methods for sample size estimation of repeated measurement quantitative data, in order to explore the research question of "comparing group differences at a single time point", from three perspectives: the primary research questions in clinical studies, the main statistical analysis methods and the definitions of the primary outcome indicators. Discrepancies in sample sizes calculated by various methods under different correlation coefficients and varying numbers of repeated measurements were examined. The study revealed that the sample size calculation method based on the mixed-effects model or generalized estimating equations accounts for both the correlation coefficient and the number of repeated measurements, resulting in the smallest estimated sample size. Secondly, the sample size calculation method based on covariance analysis considers the correlation coefficient and produces a smaller estimated sample size than the t-test. The t-test based sample size calculation method requires an appropriate approach to be selected according to the definition of the primary outcome measure. The alignment between the sample size calculation method, the statistical analysis method and the definition of the primary outcome measure is essential to avoid the risk of overestimation or underestimation of the required sample size.
Multi-task learning (MTL) has demonstrated significant advantages in the field of physiological signal measurement. This approach enhances the model's generalization ability by sharing parameters and features between similar tasks, even in data-scarce environments. However, traditional multi-task physiological signal measurement methods face challenges such as feature conflicts between tasks, task imbalance, and excessive model complexity, which limit their application in complex environments. To address these issues, this paper proposes an enhanced multi-scale spatiotemporal network (EMSTN) based on Eulerian video magnification (EVM), super-resolution reconstruction and convolutional multilayer perceptron. First, EVM is introduced in the input stage of the network to amplify subtle color and motion changes in the video, significantly improving the model's ability to capture pulse and respiratory signals. Additionally, a super-resolution reconstruction module is integrated into the network to enhance the image resolution, thereby improving detail capture and increasing the accuracy of facial action unit (AU) tasks. Then, convolutional multilayer perceptron is employed to replace traditional 2D convolutions, improving feature extraction efficiency and flexibility, which significantly boosts the performance of heart rate and respiratory rate measurements. Finally, comprehensive experiments on the Binghamton-Pittsburgh 4D Spontaneous Facial Expression Database (BP4D+) fully validate the effectiveness and superiority of the proposed method in multi-task physiological signal measurement.