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Abstract The flow-mediated dilation (FMD) test is commonly utilized and is the only technique for the assessment of vascular endothelial cell function. With this test, the augmentation of a brachial artery diameter following reactive hyperemia is measured precisely using ultrasonography by a skilled operator. This is a hospital-only test, and would be more useful if conveniently performed at home. This paper describes a first approach for studying the impact of changes in peripheral arterial elasticity, with prospects towards possible assessment of functional reactivity. A recently developed smartphone-based instrument was used to measure elastic properties of finger and radial arteries, related to stiffness and vasodilatation, as a function of distending pressure derived by photo-plethysmographic volume-oscillometry. Elasticity changes in both arteries before and after a 5-min supra-systolic upper-arm cuff occlusion were successfully obtained in 15 normal volunteers. The index-values of stiffness and vasodilatation showed, respectively, a significant decrease and increase (p

Near infrared spectroscopy is the promising and noninvasive technique that can be used to detect the brain functional activation by monitoring the concentration alternations in the haemodynamic concentration. The acquired NIRS signals are commonly contaminated by physiological interference caused by breathing and cardiac contraction. Though the adaptive filtering method with least mean squares algorithm or recursive least squares algorithm based on multidistance probe configuration could improve the quality of evoked brain activity response, both methods can only remove the physiological interference occurred in superficial layers of the head tissue. To overcome the shortcoming, we combined the recursive least squares adaptive filtering method with the least squares support vector machine to suppress physiological interference both in the superficial layers and deeper layers of the head tissue. The quantified results based on performance measures suggest that the estimation performances of the proposed method for the evoked haemodynamic changes are better than the traditional recursive least squares method.

Continuous wave near-infrared spectroscopy (CW-NIRS) can be used to measure cerebral activity because it is noninvasive, simple and portable. However, the performance of the CW-NIRS is distorted by the presence of extracerebral layer. Change of optical parameters in gray matter layer will then be inappropriately converted into the brain activity response. In the current study, a five-layer structure model constitute of scalp, skull, cerebrospinal fluid, gray matter and white matter is adopted and the mixture of the Intralipid, India ink and agar is applied to fabricate human brain tissue. To simulate optical properties in deep layer due to the brain activity, the absorption coefficients of gray matter are increased by 5%, 10%, 15%, 20%, and 25% relative to the baseline. The NIRS measurement system was designed to detect the changes in the absorption coefficients of the gray matter and quantitatively analyse the influence of the extracerebral layers. Monte Carlo technique is performed to compensate partial volume effect (PVE) introduced by the extracerebral layers. The results of the in-vitro experiments show that the measured absorption coefficients are about 9% of the standard value and the relative error is about 91% due to the extracerebral layers. The influence of the extracerebral layers is suppressed by correcting PVE with Monte Carlo simulations and the average relative error is improved to only about 6% for the whole data set. Therefore, the measurement and analysis of the brain activity could be further strengthened if the anatomic structure of the head could be predicted with Monte Carlo method or other technologies.

Background Although cuff-sphygmomanometry is used worldwide in medical and healthcare fields, it is a fact that the use of an occlusive cuff to obtain blood pressure (BP) is troublesome and inconvenient. There have therefore been on-going efforts to devise methods that do not require the use of a cuff, almost all being based on the measurement of pulse wave velocity or pulse transit time, but so far few significant developments have been made, especially regarding measurement accuracy. We have previously reported a smartphone-based cuffless method using a linear multiple regression calibration model comprising of BP obtained with a cuff-sphygmomanometer as an objective variable and modified normalized pulse volume (mNPV: a measure of vasoconstrictive activity in a finger) and pulse rate (PR) as explanatory variables. This requires a number of subjects to construct a calibration model and thus is largely dependent on the accuracy due to the model. To address these drawbacks, we report here a new cuffless method to surpass considerably the results of our previous study as well as earlier works. Methods With this method we can estimate BP, with much higher accuracy, using mNPV and PR, both also obtained from a smartphone-derived photoplethysmogram. The subject firstly performs a cuff-based BP measurement in parallel with the acquisition of mNPV and PR from a smartphone. These parameters are set as initial values (BPc0, mNPV0 and PR0; initial calibration procedure). Then, the estimated BP (BPe) can be calculated from the relation: “BPe = (BPc0·PR·mNPV)/(PR0·mNPV0)”, which is derived from the so-called haemodynamic Ohm’s law. To validate this method, preliminary experiments using 13 volunteers were carried out to compare results from the new method with those from the cuff-sphygmomanometry, used as a reference. Results Altogether 299 paired data sets were analyzed: A good agreement was found between the cuff-based and the estimated BP values, with correlation coefficients of 0.968 for systolic BP (SBP), 0.934 for mean BP (MBP) and 0.844 for diastolic BP (DBP). Bland-Altman analyses for the BPe (SBPe, MBPe, DBPe) and the BPc (SBPc, MBPc, DBPc) values also supported these comparison results. Mean absolute differences between the BPe and the BPc values in total subjects were less than 5 mmHg. Fairly good tracking availability in terms of time series data of the BPc against the corresponding BPe values was also confirmed in each subject during the study periods (1–2 weeks for 12 subjects and about 4 months for one subject). Discussion The present study reported the successful development of the new cuffless BP estimation method, given as the status of a trial stage of investigation. This method could easily be used with various smartphones, smart watches, and finger-based devices, and it appears to have significant potential as a convenient substitute for conventional cuff-sphygmomanometers as well as for practical application to mobile healthcare.

Abstract Cuffless blood pressure (BP) measurement is an all-inclusive term for a method that aims to measure BP without using a cuff. Recent cuffless technology has made it possible to estimate BP with reasonable accuracy. However, mainstream methods require an electrocardiogram and photoplethysmogram measurements, and frequent calibration procedures using a cuff sphygmomanometer. We therefore developed a far simpler cuffless method, using only heart rate (HR) and modified normalized pulse volume (mNPV) that can be measured using a smartphone, based on the knowledge that ln BP = ln cardiac output (CO) + ln total peripheral resistance (TPR), where CO and TPR are correlated with HR and mNPV, respectively. Here, we show that mean arterial pressure (MAP), systolic BP (SBP), and diastolic BP (DBP) could be estimated using the exponential transformation of linear polynomial equation, (a × ln HR) + (b × ln mNPV) + constant, using only a smartphone, with an accuracy of R > 0.70. This implies that our cuffless method could convert a large number of smartphones or smart watches into simplified sphygmomanometers.

Near-infrared spectroscopy (NIRS) can provide the hemodynamics information based on the hemoglobin concentration representing the blood oxygen metabolism of the cerebral cortical, which can be deployed for the cerebral function study. However, NIRS-based cerebral function detection accuracy can be significantly influenced by the physiological activities such as cardic cycle, respiration, spontaneous low-frequency oscillation and ultra-low frequency oscillation. The distribution difference of the capillary, artery and vein leads to the heterogeneity feature of the cerebral tissues. In the case that the heterogeneity is not serious, good detection accuracy and stable performance can be achieved through the regression analysis as the reference signal can well represent the interference in the measurement signal when conducting the multi-distance measurement approach. The direct use of the reference signal to estimate the interference is not able to achieve good performance in the case that the heterogeneity is serious. In this study, the cerebral function activity signal is extracted using recursive least square (RLS) method based on the multi-distance measurement method in which the reference signal is processed by ensemble empirical mode decomposition (EEMD) algorithm. The temporal and dimensional correlation of the neighboring sampling values are applied to estimate the interference in the measurement signal. Monte Carlo simulation based on a heterogeneous model is adopted here to investigate the effectiveness of this methodology. The results show that this methodology can effectively suppress the physiological interference and improve the detection accuracy of cerebral activity signal.

The aim of this study was to compare conventional photoplethysmography (PPG) in a finger with PPG using an integrating sphere (ISPPG) to enhance scattered light collection. Two representative wavelengths were used; 1160 nm, a window through the absorption spectra of water and alcohol, and 1600 nm around where water absorption is high and there is an absorption peak of blood glucose. Simultaneous transmission-type measurements were made with conventional PPG and with ISPPG for each wavelength in the tips of index fingers of both hands in a total of 10 healthy young male and female volunteers (21.7 ± 1.6 years old). During a 5 min period in which subjects were in a relaxed state we determined the signal-to-noise ratio, SNR, and the PPG detectability (or sensitivity) by the two techniques. SNR during the test period was significantly higher with ISPPG as compared with conventional PPG, especially for the 1600 nm wavelength. PPG signals with 1600 nm could scarcely be detected by conventional PPG, while they could be detected with good sensitively by ISPPG. We conclude that under controlled conditions ISPPG has better SNR and higher sensitivity than conventional transmission PPG, especially in wavelength regions where water absorption is high but where there is potential for practical measurement of blood constituents including glucose.

Recent progress in information and communication technologies has made it possible to measure heart rate (HR) and normalized pulse volume (NPV), which are important physiological indices, using only a smartphone. This has been achieved with reflection mode photoplethysmography (PPG), by using a smartphone's embedded flash as a light source and the camera as a light sensor. Despite its widespread use, the method of PPG is susceptible to motion artifacts as physical displacements influence photon propagation phenomena and, thereby, the effective optical path length. Further, it is known that the wavelength of light used for PPG influences the photon penetration depth and we therefore hypothesized that influences of motion artifact could be wavelength-dependant. To test this hypothesis, we made measurements in 12 healthy volunteers of HR and NPV derived from reflection mode plethysmograms recorded simultaneously at three different spectral regions (red, green and blue) at the same physical location with a smartphone. We then assessed the accuracy of the HR and NPV measurements under the influence of motion artifacts. The analyses revealed that the accuracy of HR was acceptably high with all three wavelengths (all rs > 0.996, fixed biases: -0.12 to 0.10 beats per minute, proportional biases: r = -0.29 to 0.03), but that of NPV was the best with green light (r = 0.791, fixed biases: -0.01 arbitrary units, proportional bias: r = 0.11). Moreover, the signal-to-noise ratio obtained with green and blue light PPG was higher than that of red light PPG. These findings suggest that green is the most suitable color for measuring HR and NPV from the reflection mode photoplethysmogram under motion artifact conditions. We conclude that the use of green light PPG could be of particular benefit in ambulatory monitoring where motion artifacts are a significant issue.

Brain-computer interface (BCI) is one technology that allows a user to communicate with external devices through detecting brain activity. As a promising noninvasive technique, functional near-infrared spectroscopy (fNIRS) has recently earned increasing attention in BCI studies. However, in practice fNIRS measurements can suffer from significant physiological interference, for example, arising from cardiac contraction, breathing, and blood pressure fluctuations, thereby severely limiting the utility of the method. Here, we apply the multidistance fNIRS method, with short-distance and long-distance optode pairs, and we propose the combination of independent component analysis (ICA) and least squares (LS) with the fNIRS recordings to reduce the interference. The short-distance fNIRS measurement is treated as the virtual channel and the long-distance fNIRS measurement is treated as the measurement channel. Least squares is used to optimize the reconstruction value for brain activity signal. Monte Carlo simulations of photon propagation through a five-layered slab model of a human adult head were implemented to evaluate our methodology. The results demonstrate that the ICA method can separate the brain signal and interference; the further application of least squares can significantly recover haemodynamic signals contaminated by physiological interference from the fNIRS-evoked brain activity data.