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Objective To evaluate the sensitivity, specificity and accuracy of smart wearable devices in identifying atrial fibrillation (AF). Methods We selected 410 patients who had undergone 24-hour ambulatory electrocardiography (AECG) monitoring; they wore smart wearable devices during the same period. A comparison was made between the consistency of the two examination methods for detecting AF; the sensitivity, specificity and accuracy of smart wearable devices were assessed. Results With a sensitivity of 99. 31%, specificity of 81. 48%, accuracy rate of 95. 41%, and a Kappa value of 0. 83, smart wearable devices showed a relatively strong consistency with AECG monitoring in the detection rate of AF. Conclusion Smart wearable devices are convenient to use with high compliance of patients. They are highly accurate in automatically detecting AF, which facilitates promoting daily and family-based AF screening. [ABSTRACT FROM AUTHOR]

Through logical analysis of the phenomenon of recovery faults after train arrivals in a semi-automatic receiving station from a departure station, this paper confirms the lighting status of the indicator lights on the console displays of the two stations under fault conditions, observes the operation of the relays in the recovery circuits of the two stations during faults, tests the receiving and sending voltage values and amplitude of the distributing terminal boards in the signal mechanical rooms of the two stations during faults, and observes the time length of the pulse signal. This paper then makes full use of microcomputer to monitor and replay the fault phenomenon, browses and compares the voltage curve status, data, amplitude and pulse time curve status and curve lengths of the negative pulses sent and received by the two stations under the fault conditions, so as to accurately determine the fault range, confirm the fault point, and quickly deal with the fault. [ABSTRACT FROM AUTHOR]

Objective In order to cope with the growing demand for ultra-gigabit, from gigabit to 10-gigabit, from The 5th Generation Fixed networks(F5G) to F5G-Advanced(F5. 5G), 50 Gbit/s Passive Optical Network (PON) is considered to be an important part of F5. 5G. Therefore, according to the current access network development situation, 50 Gbit/s PON technology is deeply studied in this paper. Methods This paper first introduces the difficulties in achieving high sensitivity of 50 Gbit/s PON, and proposes a solution. 50 Gbit/s Non-Return-Zero (NRZ) signals pass through Avalanche Photodiode (APD) detectors to form macroscopic currents that can be detected under the action of strong electric fields. The current is amplified by a Transresistance Amplifier (TIA) and converted into a voltage output. After balancing, the Feed Forward Equalizer (FFE) and Decision Feedback Equalizer (DFE) of optical Digital Signal Processing (oDSP) chip are used to compensate the trailing phenomenon of pulse signal, and then the influence of intersymbol interference is minimized by DFE. Then the paper analyses the key technologies such as APD, TIA and oDSP, and the receiving performance is compared with that of 25 Gbit/s APD. Results According to the experimental test results of the two groups, the received optical power of the 25 Gbit/s APD in the first group is -8.48 dBm when the receiving signal has no error code during the test time of 4 min, and the received optical power reaches -26.61 dBm when the Bit Error Rate(BER) is 2.78e-2. In the case of 50 Gbit/s APD, the received optical power is - 8.97 dBm when the received signal has no bit error in 4 min, and the received optical power reaches -27.05 dBm when the BER is 2.78e-2. The second set of 50 Gbit/s APD-2 data also achieves the same experimental results. Conclusion In conclusion, 50 Gbit/s APD has higher receiving sensitivity, better performance, and is more suitable for use in 50 Gbit/s PON optical modules to achieve high receiver sensitivity. Finally, a feasible scheme is proposed on the balance technology and APD for future cost reduction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this study is to evaluate the effectiveness of a smart non-invasive blood glucose monitor prototype during pregnancy through clinical validation. The monitor utilizes near-infrared spectroscopy combined with AI big data analysis of photoelectric volumetric pulse wave data to achieve non-invasive monitoring of blood glucose in women during pregnancy. The research team developed a monitor that employs a sensing chip, effectively overcoming the problems of weak signals and individual differences in non-invasive blood glucose monitoring. The user experience is enhanced by visualizing the test results on the accompanying cell phone APP (application) of the smart non-invasive pregnancy blood glucose monitor. Clinical validation revealed that the non-invasive monitoring data for pregnant women aged 20~30 years significantly differed from those obtained via traditional blood glucose measurement methods, whereas no significant difference ( P <0.05) was observed for pregnant women aged 31~42 years. The study concluded that further calibration of the monitor and an expansion of the sample size are necessary to enhance consistency with invasive glucose monitoring results.

Aiming at the problem of low accuracy of communication signal recognition and difficult training of network in few-shot learning, a modulation recognition algorithm based on the residual generative adversarial network is proposed. Firstly, this algorithm deigns a new residual block unit uing Leakyrelu as the activation function of the hidden layer, which can aiso caiculate the gradient for the negative input data. Then, the residual network composed of the new residual units and the convolutional neural network are used as the basic network structure of this proposed algorithm. Moreover, we apply the asymmetric small convolutional kernel with a convolutional step of 1 to belter extract the edge feature information of the signal. Finally, Dropout is adopted to replace the pooling operation, and Adam gradient optimization algorithm is selected to complete the network training in an alternate iteration mode. Experimental results show that under the condition of a few-shot learning, the recognition accuracy of the reidual generation adversarial network algorithm for 10 modulated signals can reach 91% when the ignal to noise ratio (SNR) is higher than 0 dB, which verifies the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: Photoelectric volumetric tracing (PPG) exhibits high sensitivity and specificity in flap monitoring. Deep learning (DL) is capable of automatically and robustly extracting features from raw data. In this study, we propose combining PPG with 1D convolutional neural networks (1D-CNN) to preliminarily explore the method's ability to distinguish the degree of embolism and to localize the embolic site in skin flap arteries., Methods: Data were collected under normal conditions and various embolic scenarios by creating vascular emboli in a dermatome artery model and a rabbit dermatome model. These datasets were then trained, validated, and tested using 1D-CNN., Results: As the degree of arterial embolization increased, the PPG amplitude upstream of the embolization site progressively increased, while the downstream amplitude progressively decreased, and the gap between the upstream and downstream amplitudes at the embolization site progressively widened. 1D-CNN was evaluated in the skin flap arterial model and rabbit skin flap model, achieving average accuracies of 98.36% and 95.90%, respectively., Conclusion: The combined monitoring approach of DL and PPG can effectively identify the degree of embolism and locate the embolic site within the skin flap artery.

Due to the development of industrialization, low-oxygen environment has become common in the confined spaces of construction industries, chemical industries, military, urban underground spaces, and poorly ventilated crowed areas and caused a large number of hypoxic injuries. The traditional method of preventing hypoxic injuries is to monitor the oxygen concentration in the environment without considering the difference in oxygen tolerance limits when the human body is in different physiological states. Photoplethysmography (PPG) can comprehensively reflect physiological information, including heart rate, blood pressure, blood oxygen saturation, cardiovascular blood flow parameters, and respiratory rate. When the human body enters a hypoxic environment, the physiological parameters change rapidly, resulting in a change in the PPG signal. By measuring the PPG signal of the human body, the physiological state is considered to determine whether the human body reaches the oxygen tolerance limit. This study proposed a method for quickly identifying the hypoxic state of the human body using hypoxia experiment. According to the latest research on aviation medicine, mountain medicine and naval submarine medicine, 15.5% oxygen volume fraction can guarantee the basic life safety of personnel. Through the training experimental data of a constructed deep neural network, the PPG signal of a human in normal oxygen volume fraction (16% -21%) and extremely low-oxygen volume fraction (15.5% -16%) was determined to obtain the pattern recognition network of human physiological state. After testing, the recognition accuracy of the network could reach 92.8%. Using the confusion matrix and receiver operating characteristic curve analysis, the accuracy rate of training set, verification set, test set, and ensemble recognition of the confusion matrix reached 97.9%, 94.8%, 92.8%, and 96.3%, respectively. The area under the curve value is close to 1, the network classification performance is excellent, and the entire identification process could be completed within 4 s.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

PPG (photoplethysmography) holds significant application value in wearable and intelligent health devices. However, during the acquisition process, PPG signals can generate motion artifacts due to inevitable coupling motion, which diminishes signal quality. In response to the challenge of real-time detection of motion artifacts in PPG signals, this study analyzed the generation and significant features of PPG signal interference. Seven features were extracted from the pulse interval data, and those exhibiting notable changes were filtered using the dual-sample Kolmogorov-Smirnov test. The real-time detection of motion artifacts in PPG signals was ultimately based on decision trees. In the experimental phase, PPG signal data from 20 college students were collected to formulate the experimental dataset. The experimental results demonstrate that the proposed method achieves an average accuracy of (94.07±1.14)%, outperforming commonly used motion artifact detection algorithms in terms of accuracy and real-time performance.

Copyright of Journal of Tiangong University is the property of Journal of Tianjin Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Early diagnosis and effective management are important to reduce atrial fibrillation-related adverse events. Photoplethysmography (PPG) is often used to assist wearables for continuous electrocardiograph monitoring, which shows its unique value. The development of PPG has provided an innovative solution to AF management. Serial studies of mobile health technology for improving screening and optimized integrated care in atrial fibrillation have explored the application of PPG in screening, diagnosing, early warning, and integrated management in patients with AF. This review summarizes the latest progress of PPG analysis based on artificial intelligence technology and mobile health in AF field in recent years, as well as the limitations of current research and the focus of future research.

Photoplethysmography (PPG) is often affected by interference, which could lead to incorrect judgment of physiological information. Therefore, performing a quality assessment before extracting physiological information is crucial. This paper proposed a new PPG signal quality assessment by fusing multi-class features with multi-scale series information to address the problems of traditional machine learning methods with low accuracy and deep learning methods requiring a large number of samples for training. The multi-class features were extracted to reduce the dependence on the number of samples, and the multi-scale series information was extracted by a multi-scale convolutional neural network and bidirectional long short-term memory to improve the accuracy. The proposed method obtained the highest accuracy of 94.21%. It showed the best performance in all sensitivity, specificity, precision, and F1-score metrics, compared with 6 quality assessment methods on 14 700 samples from 7 experiments. This paper provides a new method for quality assessment in small samples of PPG signals and quality information mining, which is expected to be used for accurate extraction and monitoring of clinical and daily PPG physiological information.

Objective To envaluate the accuracy of Photoplethysmograph pulse wave in monitoring stress reaction to harmful stimulation. Methods Patients (n=75) were randomly assigned to three groups according to the dosages of fentanyl that were administrated (4, 5 and 6 g/kg were given to F4, F5, F6 group respectively (n=25) in each group. Heart rate (HR), Systemic blood pressure (SBP), Diastolic blood pressure (DBP), SpO2, the skin temperature of finger, perfusion index (PI), epinephrine(E) and Norepinephrine (NE) were recorded at following time points:just in operation room (T1), 2 min after induction (T2), at intubation (T3),1 min (T4) and 5 min (T5) after intubation. And the PPGPW was printed and measured at the same time points. Results PPGA and PI were significantly increased while E and NE were significantly decreased in T2 and T5, compared with those baseline at T1 (P < 0.05). At T3, PPGA and PI were significantly decreased while NE markedly increased in group F4 and F5 compared to T1. At T3, E in group F4 were remarkably increased h while E in group F5 were of no obvious changed, compared with those at T1(P<0.05). PPGA and PI increased while E and NE decreased in group F6 at T2, T3 ,T4 and T5 compared with those at T1 (P<0.05). In summary, PI was negatively correlated with plasma E and NE while PPGA was negatively correlated with plasma E and NE. Conclusion PPGA and PI are negatively associated with intensity of stress response. And they can reflect the stress reaction during the induction period of intravenous anesthesia accurately. [ABSTRACT FROM AUTHOR]

Breathing is of great significance in the monitoring of patients with obstructive sleep apnea hypopnea syndrome, perioperative monitoring and intensive care. In this study, a respiratory monitoring and verification system based on optical capacitance product pulse wave (PPG) is designed, which can synchronously collect human PPG signals. Through algorithm processing, the characteristic parameters of PPG signal are calculated, and the respiratory signal and respiratory frequency can be extracted in real time. In order to verify the accuracy of extracting respiratory signal and respiratory rate by the algorithm, the system adds the nasal airflow respiratory signal acquisition module to synchronously collect the nasal airflow respiratory signal as the standard signal for comparison and verification. Finally, the root mean square error between the respiratory rate extracted by the algorithm from the pulse wave and the standard respiratory rate is only 1.05 times/min.

Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: -0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.

Oxygen saturation and respiratory signals are important physiological signals of human body, respiratory monitoring plays an important role in clinical and daily life. A system was established to extract respiratory signals from photoplethysmography in this study. Including the collection of pulse wave signal, the extraction of respiratory signal, and the calculation of respiratory rate and pulse rate transmitted from the slave computer to the host computer in real time.

Emotion is a series of reactions triggered by a specific object or situation that affects a person's physiological state and can, therefore, be identified by physiological signals. This paper proposes an emotion recognition model. Extracted the features of physiological signals such as photoplethysmography, galvanic skin response, respiration amplitude, and skin temperature. The SVM-RFE-CBR(Recursive Feature Elimination-Correlation Bias Reduction-Support Vector Machine) algorithm was performed to select features and support vector machines for classification. Finally, the model was implemented on the DEAP dataset for an emotion recognition experiment. In the rating scale of valence, arousal, and dominance, the accuracy rates of 73.5%, 81.3%, and 76.1% were obtained respectively. The result shows that emotional recognition can be effectively performed by combining a variety of physiological signals.

In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.

The study was intended to introduce a novel method for aided diagnosis of cardiovascular diseases based on photoplethysmography (PPG). For this purpose, 40 volunteers were recruited in this study, of whom the physiological and pathological information was collected, including blood pressure and simultaneous PPG data on fingertips, by using a sphygmomanometer and a smart fingertip sensor. According to the PPG signal and its first and second derivatives, 52 features were defined and acquired. The Relief feature selection algorithm was performed to calculate the contribution of each feature to cardiovascular diseases. And then 10 core features which had the greatest contribution were selected as an optimal feature subset. Finally, the efficiency of the Relief feature selection algorithm was demonstrated by the results of k-nearest neighbor (kNN) and support vector machine (SVM) classifier applications of the features. The prediction accuracy of kNN model and SVM reached 66.67% and 83.33% respectively, indicating that: ① Age was the foremost feature for aided diagnosis of cardiovascular diseases; ② The optimal feature subset provided an important evaluation of cardiovascular health status. The obtained results showed that the application of the Relief feature selection algorithm provided high accuracy in aided diagnosis of cardiovascular diseases.

Aiming at the defects that the traditional pulse transit time (PTT) detection methods are sensitive to changes in photoplethysmography (PPG) signal and require heavy computation, we proposed a new algorithm to detect PTT based on waveform time domain feature and dynamic difference threshold. We calculated the PTT by using dynamic difference threshold method to detect the R-waves of electrocardiogram (ECG), shortening the main peak detection range in PPG signal according to the characteristics of the waveform time domain, and using R wave to detect the main peak of PPG signal. We used the American MIMIC database and laboratory test data to validate the algorithm. The experimental results showed that the proposed method could accurately extract the feature points and detect PTT, and the PTT detection accuracies of the measurements and the database samples were 99.1% and 97.5%, respectively. So the proposed method could be better than the traditional methods.

Blind source separation technique based on independent component analysis (ICA) can separate blood volume pulse (BVP) from the facial video and then realize the telemetry of heart rate, blood oxygen saturation, respiratory rate and other vital signs parameters. However, the superiority of ICA in BVP extraction has not been demonstrated in the existing researches. Some researchers suggested using traditional G-channel method for BVP extraction (G-BVP) instead of ICA method (ICA-BVP). This study investigated the applicability of ICA-BVP comparatively. To solve the inherent permutation problem of ICA, a spectral kurtosis-based method was proposed for BVP identification. The experimental results based on the facial video datasets from 9 subjects shows that ICA-BVP method has apparent advantages in motion artifacts attenuation and ambient light changes elimination. The kurtosis-based method achieved a good performance in BVP identification and dynamic heart rate (HR) estimation. In practical application, the proposed ICA-BVP method could present a better stability and accuracy in vital signs parameters extraction.

2 Faculty of Math and Natural Science, Kalashnikov Izhevsk State Technical University, Izhevsk, 426069, Russian Federation Photoplethysmography is a simple technology with low-cost. It can be used to detect blood volume changes in the microvascular. Because the photoplethysmographic devices, which are produced by this technology, are portable and noninvasive, and the data which is received consists of many physiological information of human body, this technology can give great convenience to clinical diagnostic and personal daily health monitoring. This is why it has very broad application prospects. Photoplethysmogram usual y consists of an AC component which depends on the blood volume changes in cardiac system, and a DC component which is decided by respiration, tissue, inherent components of blood. Although the origin of the photoplethysmographic signal are being researched stil , we can get many valuable information of cardiac system.

The traditional method of measuring arterial oxygen saturation is that R value, the ratio of alternating component of the logarithmic photoplethysmography, is firstly computed and then the linear regression model is established by experiment. The R value computation is a dimension reduction process based on Lambert-beer law, which aims at eliminating the influence of optical path and minimizing the impact of individual differences. When taking scattering into consideration, the dimension reduction process loses information, introduces the system error and limits the precision of measurement. In order to reduce the measurement error resulting from the scattering effects, this paper presents a new method that the peak and valley values of dual-wavelength logarithmic photoplethysmography waves are used as the independent variables to develop a linear regression model to predict the arterial oxygen saturation. During the experiment, the in-vivo measurements were carried out on 23 healthy volunteer and 133 samples of photoplethysmography waves and the reference value of oxygen saturation were recorded. To compare the predictive performance between the new method and the R value method, 90 samples were randomly selected as modeling sets and the remaining 43 samples were used as prediction sets. Random selection of modeling sets and prediction are executed 10 times. The average related coefficients of the prediction sets of the new method and the R value method are 0.890 6 and 0.846 8, and the average root mean square errors are 0.889 6% and 1.037 3% respectively. Results indicate that the performance of the new method is better than the one of the R value method, and the predictivemodel based on 4 parameters can improve the stability and accuracy of measurement. And the new method has guiding significance to the measurement of human body's blood physiological information based on limited wavelength spectrum data.

In order to improve the storage and transmission efficiency of dynamic photoplethysmography (PPG) signals in the detection process and reduce the redundancy of signals, the modified adaptive matching pursuit (MAMP) algorithm was proposed according to the sparsity of the PPG signal. The proposed algorithm which is based on reconstruction method of sparse adaptive matching pursuit (SAMP), could improve the accuracy of the sparsity estimation of signals by using both variable step size and the double threshold conditions. After experiments on the simulated and the actual PPG signals, the results show that the modified algorithm could estimate the sparsity of signals accurately and quickly, and had good anti-noise performance. Contrasting with SAMP and orthogonal matching pursuit (OMP), the reconstruction speed of the algorithm was faster and the accuracy was high.

In comparison with the measurement of heart rate variability from ECG and from photoplethysmographic signal from 46 healthy adults in their spontaneous breathing state. The beat-to-beat intervals in ECG and pulse-to-pulse intervals in photoplethysmographic signal are extracted, and then the parameters of heart rate variability are calculated. Three kinds of algorithms are chosen to get the pulse-to-pulse intervals, which are the intervals of maximum of second derivative, the maximum of PPG signal and the tangent intersection. The results show that the correlation coefficients of the HRV parameters in the two calculation methods are highly correlated. The Bland-Altman scattered plots show the relative bias results from the algorithm of the maximum of PPG signal are smallest and singular points that deviate from the consistent limits are the least compared with the other two algorithms.

In this paper, a reflection type photoelectric pulse wave sensor was designed for short-term pulse rate variability analysis. Photoplethysmography (PPG) signals and ECG signals (obtained with the Dimetek MicroECG recorder Dicare-m1CP) were recorded synchronously from 20 healthy subjects. The analytical results show a significant correlation (correlation coefficient r > 0.99) between the PPG-derived peak-to-peak (PP) intervals and the ECG-derived RR intervals. Besides, there are no significant differences (P > 0.05) between the HRV measured by ECG and the PRV quantified by the PPG whether in time domain, frequency domain, or the Poincaré plot parameters. The experimental results suggest that the PPG-based short-term PRV analysis can be consistent with the ECG-based HRV measurement in wearable smart devices.

Empirical mode decomposition (EMD) algorithm combined with the theory of dynamic spectrum extraction at frequency domain was applied to the noninvasive measurement of hemoglobin concentration. Fifty seven cases' photoplethysmography was collected in the range of 636.98-1086.86 nm in vivo. After the denoising preprocess through the EMD method for each wavelength pulse wave of each sample separately, dynamic spectrum of each sample was made up of all peaks extracted by Fourier transform. Partial least squares regression model was used to establish the calibration and prediction of hemoglobin concentration. Compared to the modeling results without EMD, the correlation coefficient of predicted values and the real values was increased from 0.8798 up to 0.9176. The root mean square error of prediction set was reduced from 6.6759 to 5.3001 g x L(-1) and the relative error was reduced from 8.45 to 6.71%. The modeling accuracy has been greatly improved. The results showed that EMD algorithm can be effectively applied to denoise the spectral data and improve the accuracy of the non-invasive measurement of blood components.

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 re- mote and non-contact measurement of multi-parameter physiological measurements.

An improved spectrum extraction method is proposed based on the dynamic spectrum theory to improve the precision. First, the drift baseline is fitted to restrain the effect of relative motion between the finger and the probe. Then, the spectrum is extracted using the square-sum-root method as a replacement of the peak-peak value method to reduce the impact of system random error. Taking the measurement of oxygen saturation for instance, uncertainty analysis is performed for the oxygen saturation coefficient Q. Compared with the peak-peak value method, the square-sum-root method can reduce the uncertainty of oxygen saturation coefficient Q to 38% of the original value. Ten data sets were sampled from ten volunteers, and the uncertainty of the coefficient Q obtained using the two methods was directly evaluated and compared. The average ratio of the Q uncertainty by the two methods turned to be 42%. Both the theoretical analysis and the experimental results show that the square-sum-root method proposed can be used to extract the effective spectra more accurately from the photoplethysmography, suggesting a meaningful progress in noninvasive blood components measurement.

The technology of non-invasive near-infrared spectral detection of biological tissue blood oxygen saturation has great research and application prospects, and has widespread clinical applications. However, the conventional finger oximeter only reflects local oxygen saturation values, and has limitations in the application. These oxygenation readings become unreliable or cease, too. In the present paper, a new method is described that the reflectance pulse oximetry with variable incident light intensity is employed to measure biological multilayer tissue. The photoplethysmographic (PPG) signals significantly change with the change in the incident light intensity in the experiment on the fingertip. Further analysis shows that the changes of the PPG signals correspond to the multilayer structure of finger. It means that the blood oxygen saturation of different tissue level must be calculated from the PPG signals. These features show that this method must be feasible.

Objectives: To assess the scope of photoplethysmography (PPG), color duplex ultrasonography, percutaneous transpopliteal venography for evaluating chronic venous disease., Methods: Color duplex ultrasonography, percutaneous transpopliteal venography, and PPG and were used in 40 patients their results were compared., Results: Color duplex ultrasonography and percutaneous transpopliteal venography for evaluating venous reflux was not significant (P > 0.05) in the femoral-popliteal venous segment. The accuracy of PPG in evaluating venous reflux was 67% compared with that of color duplex ultrasonography., Conclusions: PPG as the first test is often used for chronic venous disease. Color duplex ultrasonography can provide sufficient information for treatment of patients the. Percutaneous transpopliteal venography is necessary when noninvasive test can not provide sufficient information.

Doppler ultrasound and photoplethysmography for the diagnosis of thoracic outlet syndrome was described in this article. There were 14 cases clinically confirmed, 16 cases highly suspected, and 10 cases of normal control. The positive rates were 92% (13/14), 50% (8/16) and 0% (0/10) respectively. The results showed remarkably consistent with the clinical diagnosis. This method has provided a reliable objective evidence for the clinical diagnosis and treatment of this condition.