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.
Transit time flow measurement (TTFM),which is independent of vessel size and shape, has been considered to be an easy, reproducible and non-invasive method to assess the hemodynamic characteristics. Moreover, current studies have shown that TTFM has clinical application in identifying the function of grafted vessel and prognosis. Researchers have proved some reliable indicators for the function of grafted vessel as follows: mean graft flow (MGF) > 15 ml/min, diastolic flow (DF) >50% and pulsatility index (PI)<3 or 5. This article focuses on the review of clinical application and research progress of TTFM in CABG.
This paper presents the preliminary design of data acquisition system of a portable uroflowmeter. The system uses double-hole cantilever pressure sensor. The signal is transferred to ATmega644PA microprogrammed control unit (MCU), converted by A/D (analog to digital) convertor. Then the further data are processed and get the corresponding relationship of weight-time and two curves of urine flow and urinary flow rate. In the measurement accuracy of the device about urine flow, two factors about the placement and height of the data acquisition are analyzed to show the accuracy of the equipment through the Origin 8.0 data analysis software. The design is characterized by low cost and high speed of data collection, real-time, high accuracy.
ObjectiveTo measure anatomical parameters related to cervical uncovertebral joint and provide data support for the design of uncovertebral joint fusion cage.MethodsAccording to the inclusion and exclusion criteria, raw DICOM data of cervical CT scan in 60 patients (30 males and 30 females, aged 39-60 years) were obtained, then the three-dimensional cervical spine model was reconstructed for anatomical measurement by using the Mimics19.0 software. The height of the uncinate process, the length of the uncinate process, the width of the uncinate process, and the length of the uncovertebral joint in the intervertebral foramen region were measured bilaterally from C3 to C7. The anterior and posterior distances between the uncinate processes were measured from C3 to C7. The height of the uncovertebral joint space, the central height of the intervertebral disc space, and the depth of the intervertebral disc space were also measured from C2, 3 to C6, 7. The mean, standard deviation, maximum, and minimum were calculated by using the SPSS22.0 statistical software for the design of uncovertebral joint fusion cage.ResultsThe height of the uncinate process, the length of the uncinate process, the width of the uncinate process, and the length of the uncovertebral joint in the intervertebral foramen region of C3-C7 and the height of the uncovertebral joint space of C2, 3-C6, 7 showed no significant difference between two sides (P>0.05). The height of the uncovertebral joint space also had no significant difference between females and males (P>0.05). The anterior distances between the uncinate processes of C3-C7 were significantly larger than the posterior distances between the uncinate processes (P<0.05), the uncovertebral joint presented a posterior cohesive shape. The central height of the intervertebral disc space in male group was slightly higher than that in female group, and the differences were significant (P<0.05) at C2, 3 and C5, 6; the depth of the intervertebral disc space in male group was significantly higher than that in female group (P<0.05). The central height of the intervertebral disc space was (4.94±0.49) mm (range, 3.81-5.90 mm), the depth of the intervertebral disc space was (15.78±1.23) mm (range, 12.94-18.85 mm), the anterior and posterior distances between the uncinate processes were (17.19±2.39) mm (range, 13.39-24.63 mm) and (10.84±2.12) mm (range, 7.19-16.64 mm), respectively. According to the results of the anatomical research, the height of the uncovertebral joint fusion cage was designed as 5, 6, 7, and 8 mm; the depth of the uncovertebral joint fusion cage was designed as 12, 13, 14, 15, and 16 mm; the width of the uncovertebral joint fusion cage was designed as 14-18 mm; and the two wings are designed as arc-shape with 2 and 3 mm in width.ConclusionThere are certain differences in the anatomical parameters of the uncovertebral joint between different segments. The uncovertebral joint fusion cage that designed based on the results of anatomical research is suitable for most patients.
Objective To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.
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.
Objective To explore the correlation between liver volume variation of posthepatitic cirrhosis patients and the severity of the disease. Methods One hundred and eleven patients with normal livers and 74 posthepatitic cirrhosis patients underwent volume CT scan. The relation between normal liver volume and body height, body weight and body surface area was studied by linear regression and correlation method, the standard liver volume equation was deduced. The change ratio of liver volume in cirrhotic patients was calculated and compared with Child classification. Results The mean normal liver volume of Chinese adults was （1 225.15±216.23） cm3, there was a positive correlation between liver volume and body height, body weight 〔liver volume (cm3)=12.712×body weight (kg)+450.44〕 and body surface area 〔liver volume (cm3)=876.02×body surface area (m2)-297.17〕. The mean liver volume of Child A, B and C patients were （1 077.77±347.01） cm3, (1 016.35±348.60) cm3 and (805.73±208.85) cm3 respectively. The liver volume and liver volume index was significantly smaller in Child C patients than those in Child A and B patients (P＜0.05); while liver volume change ratio was higher in Child C patients (P＜0.05). Conclusion Liver volume variation of cirrhotic patients can be quantitatively assessed by 16 slices helical CT volume measurement and standard liver volume equation. The change of the liver volume is correlated with the severity of liver cirrhosis.
Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
In order to quantitatively evaluate the performance of dry electrode for fabric surface bioelectricity, a set of active measuring devices that can simulate electrocardiosignal has been developed on the basis of passive system by our group. Five Ag/AgCl fabric dry electrodes were selected to test and evaluate the devices. The results show that the deviation ratios of peak time interval of the five electrodes are all less than 1%. The maximum voltage amplitude decay rate is 7.2%, and the noise amplitudes are lower than 0.004 mV. The variable coefficient of peak time offset is less than 8%. The variable coefficient of voltage amplitude is less than 2%. The variable coefficient of noise amplitude is less than 10%. Research shows the devices has good repeatability and stability in measuring the simulated electrocardiosignal. The active measuring devices proposed in this paper can provide a new method for performance evaluation and standard formulation of surface bioelectricity dry electrode.
Accurate measurements of voltage and current from electrosurgery are the basis of development of electrosurgery with feedback function. We, therefore, developed a parameter measurement system based on PC, with high voltage and current from electrosurgery being sensed with transformers, amplified, filtered, transformed into single-ended signals, and then into RMS signals. The root mean square (RMS) signals were transformed into digital signals through DAQ card and the data was processed in PC with Labview. The process included sampling, displaying and storage. The experiment results indicated that the measurement system could measure the output parameters from electrosurgery steadily and correctly so that the development of the system has been successful. It can be the basis of development of embedded parameters measurement system and can provide accurate feedback information for intellectual electrosurgery.