A Comparative Study of Brachial-Ankle Pulse Wave Velocity and Heart-Finger Pulse Wave Velocity in Korean Adults.

Arterial stiffness is considered an index of vascular aging. The brachial-ankle pulse wave velocity (baPWV) method is widely used because of its proven effectiveness; and the pulse wave velocity measurement method using both electrocardiogram (ECG) and photoplethysmogram (PPG) is actively being studied due to the convenience of measurement and the possibility of miniaturization. The aim of this study was to evaluate and compare the effects of age and gender in Korean adults using both the baPWV method and the PWV method with ECG and finger PPG (heart-finger PWV). The measurements have been carried out for 185 healthy subjects of Korean adults, and the results showed that the baPWV was highly correlated with age in both genders (r = 0.94 for both males and females). However, the correlation values in heart-finger PWV measurement were significantly lower than those of baPWV (r = 0.37 for males and r = 0.71 for females). Although the heart-finger PWV method is suitable for mobile applications because it can be easily miniaturized while maintaining its signal quality, these results show that the heart-finger PWV method is not as effective as baPWV at evaluating the arterial stiffness.

Novel heart rate variability index for wrist-worn wearable devices subject to motion artifacts that complicate measurement of the continuous pulse interval.

OBJECTIVE: Wearable health monitoring devices have recently become popular, but they can still only measure the average heart rate. Heart rate variability (HRV) is known to represent changes in the autonomic nervous system and analysis of HRV has the potential to be used for monitoring various wellness-related parameters such as sleep or stress. HRV analysis requires accurate measurement of the heartbeat interval. In wearable devices, it is difficult to accurately measure the heartbeat interval due to motion noise. In this paper we propose a new method for performing HRV analysis on photoplethysmographic (PPG) signals corrupted by motion artifacts measured at the wrist. APPROACH: A frequency-tracking algorithm based on the oscillator-based adaptive notch filter was used to measure instantaneous heart rate. The algorithm consists of a time-varying bandpass filter for enhancing the heartbeat signal and an adaptive mechanism for tracking heart rate frequency. By optimizing the filter bandwidth and forgetting factor of the adaptive mechanism, the frequency-tracking algorithm better reflects the variability of instantaneous heart rate. The new HRV index was calculated as the standard deviation of the heartbeat interval data converted using the heart rate estimated by the frequency-tracking algorithm. In order to verify the effectiveness of the proposed index, the new HRV index calculated for each sleep stage was compared with SDNN, the standard deviation of the heartbeat interval, which was calculated using simultaneous electrocardiogram measurements. In addition, changes in SDNN and the new index were compared during a socially evaluated speech task. Finally, the relationship between the new index and SDNN was compared with the data collected during daily activities over a 24 h period. MAIN RESULTS: Experimental results showed that statistically significant changes in HRV could be monitored in different sleep stages using the proposed method. In addition, when subjects were stressed by a socially evaluated speech task, significant reduction in HRV was observed using the proposed method. Finally, HRV values measured during daily activities over a 24 h period showed a high correlation coefficient of 0.812 with reference HRVs. SIGNIFICANCE: The new HRV index calculated by the proposed method is expected to be an effective new solution for noisy PPG signals.

Current Status and Prospects of Health-Related Sensing Technology in Wearable Devices.

The healthcare-related functions of wearable devices are very useful for continuous monitoring of biological information. Wearable devices equipped with communication function can be used for additional healthcare services. Among the wearable devices, the wristband type is most suitable for acquiring biological signals, and the wear preference of the user is high, so it is highly likely to be used more in the future. In this paper, the health-related functions of wristband were investigated and the technical limitations and prospects were also reviewed. Most current wristband-type devices are equipped with the combination of accelerometer, optical sensor, and electrodes for their health functions, and continuously measured data are expanding the possibility of discovering new medical meanings. The blood pressure measurement function without using cuff is the most useful and expected function among the health-related functions expected to be mounted on the wrist wearable device, in spite of its technical limits and difficulties.

Noise-Robust Heart Rate Estimation Algorithm from Photoplethysmography Signal with Low Computational Complexity.

This paper introduces a noise-robust HR estimation algorithm using wrist-type PPG signals that consist of preprocessing block, motion artifact reduction block, and frequency tracking block. The proposed algorithm has not only robustness for motion noise but also low computational complexity. The proposed algorithm was tested on a data set of 12 subjects and recorded during treadmill exercise in order to verify and compare with other existing algorithms.

The Effect of Optical Crosstalk on Accuracy of Reflectance-Type Pulse Oximeter for Mobile Healthcare.

According to the theoretical equation of the pulse oximeter expressed by the ratio of amplitude (AC) and baseline (DC) obtained from the photoplethysmographic signal of two wavelengths, the difference of the amount of light absorbed depending on the melanin indicating the skin color is canceled by normalizing the AC value to the DC value of each wavelength. Therefore, theoretically, skin color does not affect the accuracy of oxygen saturation measurement. However, if there is a direct path for the light emitting unit to the light receiving unit instead of passing through the human body, the amount of light reflected by the surface of the skin changes depending on the color of the skin. As a result, the amount of crosstalk that varies depending on the skin color affects the ratio of AC to DC, resulting in errors in the calculation of the oxygen saturation value. We made crosstalk sensors and crosstalk-free sensors and performed desaturation experiments with respiratory gas control on subjects with various skin colors to perform oxygen saturation measurements ranging from 60 to 100%. Experimental results showed that there was no difference in the measurement error of oxygen saturation according to skin color in the case of the sensor which prevented crosstalk (-0.8824 ± 2.2859 for Asian subjects, 0.6741 ± 3.2822 for Caucasian subjects, and 0.9669 ± 2.2268 for African American subjects). However, a sensor that did not prevent crosstalk showed a large error in dark skin subjects (0.8258 ± 2.1603 for Asian subjects, 0.8733 ± 1.9716 for Caucasian subjects, and -3.0591 ± 3.9925 for African Americans). Based on these results, we reiterate the importance of sensor design in the development of pulse oximeters using reflectance-type sensors.

Reliability of the Parabola Approximation Method in Heart Rate Variability Analysis Using Low-Sampling-Rate Photoplethysmography.

Photoplethysmographic signals are useful for heart rate variability analysis in practical ambulatory applications. While reducing the sampling rate of signals is an important consideration for modern wearable devices that enable 24/7 continuous monitoring, there have not been many studies that have investigated how to compensate the low timing resolution of low-sampling-rate signals for accurate heart rate variability analysis. In this study, we utilized the parabola approximation method and measured it against the conventional cubic spline interpolation method for the time, frequency, and nonlinear domain variables of heart rate variability. For each parameter, the intra-class correlation, standard error of measurement, Bland-Altman 95% limits of agreement and root mean squared relative error were presented. Also, elapsed time taken to compute each interpolation algorithm was investigated. The results indicated that parabola approximation is a simple, fast, and accurate algorithm-based method for compensating the low timing resolution of pulse beat intervals. In addition, the method showed comparable performance with the conventional cubic spline interpolation method. Even though the absolute value of the heart rate variability variables calculated using a signal sampled at 20 Hz were not exactly matched with those calculated using a reference signal sampled at 250 Hz, the parabola approximation method remains a good interpolation method for assessing trends in HRV measurements for low-power wearable applications.

Sleep Period Time Estimation Based on Electrodermal Activity.

We proposed and tested a method to estimate sleep period time (SPT) using electrodermal activity (EDA) signals. Eight healthy subjects and six obstructive sleep apnea patients participated in the experiments. Each subject's EDA signals were measured at the middle and ring fingers of the dominant hand during polysomnography (PSG). For nine of the 17 participants, wrist actigraphy was also measured for a quantitative comparison of EDA- and actigraphy-based methods. Based on the training data, we observed that sleep onset was accompanied by a gradual reduction of amplitude of the EDA signals, whereas sleep offset was accompanied by a rapid increase in amplitude of EDA signals. We developed a method based on these EDA fluctuations during sleep-wake transitions, and applied it to a test dataset. The performance of the method was assessed by comparing its results with those from a physician's sleep stage scores. The mean absolute errors in the obtained values for sleep onset, offset, and period time between the proposed method, and the results of the PSG were 4.1, 3.0, and 6.1 min, respectively. Furthermore, there were no significant differences in the corresponding values between the methods. We compared these results with those obtained by applying actigraphic methods, and found that our algorithm outperformed these in terms of each estimated parameter of interest in SPT estimation. Long awakening periods were also detected based on sympathetic responses reflected in the EDA signals. The proposed method can be applied to a daily sleep monitoring system.

Microwave non-contact imaging of subcutaneous human body tissues.

A small-size microwave sensor is developed for non-contact imaging of a human body structure in 2D, enabling fitness and health monitoring using mobile devices. A method for human body tissue structure imaging is developed and experimentally validated. Subcutaneous fat tissue reconstruction depth of up to 70 mm and maximum fat thickness measurement error below 2 mm are demonstrated by measurements with a human body phantom and human subjects. Electrically small antennas are developed for integration of the microwave sensor into a mobile device. Usability of the developed microwave sensor for fitness applications, healthcare, and body weight management is demonstrated.

Mind Your Composition: Clinical validation of Samsung's pocket-based bioelectrical impedance analyzers may increase consumer interest in personal health management.

When asked about our weight, most of us can name a figure based on prior knowledge. And while stepping on a scale gives us the ability to know that exact number and track it routinely, it does not provide insights into our body?s composition. This, at the basic level, refers to proportions of fat and lean or fat-free mass (FFM) that comprise the human body. Conventionally, the body mass index (BMI), which is the ratio of body weight in kilograms to the square of its height in meters, and anthropometric parameters like waist circumference, waist-to-hip ratio, and skinfold thickness have been used to estimate the level of fatness. In fact, BMI is the de facto marker for stratifying individuals into underweight (<18.5 kg/m2), normal (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2), and obese (>30 kg/m2) categories. Nonetheless, these metrics are limited in precisely characterizing individuals by percentages of body fat and muscle mass, particularly in epidemiological studies where these proportions vary across age, sex, and ethnic groups. Of note is also how, solely on the basis of BMI, a physically fit individual may be classified as overweight due to having a higher proportion of lean body mass, which outweighs fat. This highlights the importance of body composition in weight tracking and management.

Smartphone-Based Bioelectrical Impedance Analysis Devices for Daily Obesity Management.

Current bioelectric impedance analysis (BIA) systems are often large, cumbersome devices which require strict electrode placement on the user, thus inhibiting mobile capabilities. In this work, we developed a handheld BIA device that measures impedance from multiple frequencies (5 kHz~200 kHz) with four contact electrodes and evaluated the BIA device against standard body composition analysis systems: a dual-energy X-ray absorptiometry (DXA) system (GE Lunar Prodigy, GE Healthcare, Buckinghamshire, UK) and a whole-body BIA system (InBody S10, InBody, Co. Ltd, Seoul, Korea). In the study, 568 healthy participants, varying widely in body mass index, age, and gender, were recruited at two research centers: the Samsung Medical Center (SMC) in South Korea and the Pennington Biomedical Research Center (PBRC) in the United States. From the measured impedance data, we analyzed individual body fat and skeletal muscle mass by applying linear regression analysis against target reference data. Results indicated strong correlations of impedance measurements between the prototype pathways and corresponding InBody S10 electrical pathways (R = 0.93, p < 0.0001). Additionally, body fat estimates from DXA did not yield significant differences (p > 0.728 (paired t-test), DXA mean body fat 29.45 ± 10.77 kg, estimated body fat 29.52 ± 12.53 kg). Thus, this portable BIA system shows a promising ability to estimate an individual's body composition that is comparable to large stationary BIA systems.

Interference Reduction in Glucose Detection by Redox Potential Tuning: New Glucose Meter Development.

A new glucose meter was developed employing a novel disposable glucose sensor strip comprising a nicotinamide adenine dinucleotide-glucose dehydrogenase (NAD-GDH) and a mixture of Fe compounds as a mediator. An iron complex, 5-(2,5-di(thiophen-2-yl)-1H-pyrrol-1-yl)-1,10-phenanthroline iron(III) chloride (Fe-PhenTPy), was synthesized as a new mediator for the NAD-GDH system. Due to the high oxidation potential of the mediator, the detection potential was tuned to be more closely fitted toward the enzyme reaction potential, less than 400 mV (vs. Ag/AgCl), by mixing with an additional iron mediator. The impedance spectrometry for the enzyme sensor containing the mixed mediators showed an enhanced charge transfer property. In addition, a new cartridge-type glucose meter was manufactured using effective aligned-electrodes, which showed an enhanced response compared with conventional electrode alignment. The proposed glucose sensor resulted in a wide dynamic range in the concentration range of 30 - 500 mg dL(-1) with a reduced interference effect and a good sensitivity of 0.57 µA mM(-1).

Reliability of ultra-short-term analysis as a surrogate of standard 5-min analysis of heart rate variability.

BACKGROUND: Despite the increasing demands of ultra-short-term heart rate (HR) variability (HRV) for practical ambulatory applications, there have been few studies that have investigated R-R interval recording for less than 5 min for HRV analysis. It has not been extensively validated, and, currently, no normative data for ultra-short-term HRV exist. The aim of this study was to investigate the relationship between standard 5-min and ultra-short-term HRV by collecting data from a large population consisting of a wide range of age groups. MATERIALS AND METHODS: The 5-min R-R interval series were obtained from 467 healthy volunteers ranging from 8 to 69 years of age. The original R-R interval was segmented into 270, 240, 210, 180, 150, 120, 90, 60, 30, 20, and 10 s, and those HRV features most commonly reported within the literature were calculated and compared with those using the original 5-min R-R interval series. The Pearson correlation r, the p value by the Kruskal-Wallis test, and the Bland-Altman plot analysis computations were performed for each HRV variable calculated using different lengths of R-R interval series. RESULTS: For each HRV variable, the minimum length of the R-R interval required to reliably estimate the 5-min HRV was identified. The results were different for each age group: 10 s for HR, 20 s for high-frequency, 30 s for root mean square difference, 60 s for proportion of the number of interval differences of successive NN intervals greater than 50 ms divided by total number of NNs, 90 s for low-frequency, normalized low-frequency, normalized high-frequency, and low-frequency/high-frequency, 240 s for standard deviation of successive NN interval differences and time-frequency, and 270 s for very low-frequency. In addition, the reference value for short-term HRV from normal healthy subjects was also presented. CONCLUSIONS: Some HRV variables calculated from R-R interval series shorter than 5 min were well matched with those calculated from the 5-min R-R interval. Thus, ultra-short-term HRV is likely to be a good surrogate method to assess trends in HRV.

Unconstrained snoring detection using a smartphone during ordinary sleep.

BACKGROUND: Snoring can be a representative symptom of a sleep disorder, and thus snoring detection is quite important to improving the quality of an individual's daily life. The purpose of this research is to develop an unconstrained snoring detection technique that can be integrated into a smartphone application. In contrast with previous studies, we developed a practical technique for snoring detection during ordinary sleep by using the built-in sound recording system of a smartphone, and the recording was carried out in a standard private bedroom. METHOD: The experimental protocol was designed to include a variety of actions that frequently produce noise (including coughing, playing music, talking, rining an alarm, opening/closing doors, running a fan, playing the radio, and walking) in order to accurately recreate the actual circumstances during sleep. The sound data were recorded for 10 individuals during actual sleep. In total, 44 snoring data sets and 75 noise datasets were acquired. The algorithm uses formant analysis to examine sound features according to the frequency and magnitude. Then, a quadratic classifier is used to distinguish snoring from non-snoring noises. Ten-fold cross validation was used to evaluate the developed snoring detection methods, and validation was repeated 100 times randomly to improve statistical effectiveness. RESULTS: The overall results showed that the proposed method is competitive with those from previous research. The proposed method presented 95.07% accuracy, 98.58% sensitivity, 94.62% specificity, and 70.38% positive predictivity. CONCLUSION: Though there was a relatively high false positive rate, the results show the possibility for ubiquitous personal snoring detection through a smartphone application that takes into account data from normally occurring noises without training using preexisting data.

Robust sleep quality quantification method for a personal handheld device.

OBJECTIVE: The purpose of this study was to develop and validate a novel method for sleep quality quantification using personal handheld devices. MATERIALS AND METHODS: The proposed method used 3- or 6-axes signals, including acceleration and angular velocity, obtained from built-in sensors in a smartphone and applied a real-time wavelet denoising technique to minimize the nonstationary noise. Sleep or wake status was decided on each axis, and the totals were finally summed to calculate sleep efficiency (SE), regarded as sleep quality in general. The sleep experiment was carried out for performance evaluation of the proposed method, and 14 subjects participated. An experimental protocol was designed for comparative analysis. The activity during sleep was recorded not only by the proposed method but also by well-known commercial applications simultaneously; moreover, activity was recorded on different mattresses and locations to verify the reliability in practical use. Every calculated SE was compared with the SE of a clinically certified medical device, the Philips (Amsterdam, The Netherlands) Actiwatch. RESULTS: In these experiments, the proposed method proved its reliability in quantifying sleep quality. Compared with the Actiwatch, accuracy and average bias error of SE calculated by the proposed method were 96.50% and -1.91%, respectively. CONCLUSIONS: The proposed method was vastly superior to other comparative applications with at least 11.41% in average accuracy and at least 6.10% in average bias; average accuracy and average absolute bias error of comparative applications were 76.33% and 17.52%, respectively.

Preliminary study for the personal handheld device based snoring detection in ordinary sleep situation.

Snoring is one of the representative phenomena of the sleep disorder and detection of snoring is quite important for improving quality of daily human life. The purpose of this research is to define the noises of the ordinary sleep situation and to find its characteristics as a preliminary research of snoring detection. Differently from previous snoring researches, we use a built-in sound recording system of Smartphone for practical use in ordinary sleep condition, and recording was carried out in a general private bedroom. Especially, we designed the experimental protocol, including the various noises could be frequently occurred during sleep such as cough, music, talking, alarm, door open/close, fan, radio and footstep to make closer to the actual sleep circumstance. The sound data set was recorded during actual sleep from 10 normal subjects. Totally 44 snoring data set and 75-noise dataset is acquired and analyzed.

Electron-transfer mediator for a NAD-glucose dehydrogenase-based glucose sensor.

A new electron-transfer mediator, 5-[2,5-di (thiophen-2-yl)-1H-pyrrol-1-yl]-1,10-phenanthroline iron(III) chloride (FePhenTPy) oriented to the nicotinamide adenine dinucleotide-dependent-glucose dehydrogenase (NAD-GDH) system was synthesized through a Paal-Knorr condensation reaction. The structure of the mediator was confirmed by Fourier-transform infrared spectroscopy, proton and carbon nucler magnetic resonance spectroscopy, and mass spectroscopy, and its electron-transfer characteristic for a glucose sensor was investigated using voltammetry and impedance spectroscopy. A disposable amperometric glucose sensor with NAD-GDH was constructed with FePhenTPy as an electron-transfer mediator on a screen printed carbon electrode (SPCE) and its performance was evaluated, where the addition of reduces graphene oxide (RGO) to the mediator showed the enhanced sensor performance. The experimental parameters to affect the analytical performance and the stability of the proposed glucose sensor were optimized, and the sensor exhibited a dynamic range between 30 mg/dL and 600 mg/dL with the detection limit of 12.02 ± 0.6 mg/dL. In the real sample experiments, the interference effects by acetaminophen, ascorbic acid, dopamine, uric acid, caffeine, and other monosaccharides (fructose, lactose, mannose, and xylose) were completely avoided through coating the sensor surface with the Nafion film containing lead(IV) acetate. The reliability of proposed glucose sensor was evaluated by the determination of glucose in artificial blood and human whole blood samples.

Activation of pluripotency genes by a nanotube-mediated protein delivery system.

The overexpression of cell reprogramming factors (Oct4, Sox2, Klf4, Nanog, and c-Myc) allows differentiated cells to revertto an earlier developmental stage. Differentiated cells can also be reprogrammed by directly delivering reprogramming proteins tagged with cell-penetrating peptides, which allow the proteins to pass through the cell membrane and into the cytoplasm-although this method has been an inefficient process. Here, we describe a novel technique for delivering reprogramming proteins into cells using titanium oxide (TiO2 ) nanotubes, which show no cytotoxic effects and do not affect cell proliferation. TiO2 nanotubes successfully transferred the above-mentioned reprogramming factors into differentiated somatic cells. After 3 weeks of treatment with protein-conjugated nanotubes, the somatic cells adopted an embryonic stem cell-like morphology and expressed activated Oct4-green fluorescent protein, a pluripotency biomarker. Our results indicate that TiO2 nanotubes can be used to directly deliver reprogramming factors into somatic cells to induce pluripotency.

Detection of subjects with higher self-reporting stress scores using heart rate variability patterns during the day.

Heart rate variability (HRV) has been well established to measure instantaneous levels of mental stress. Circadian patterns of HRV features have been reported but their use to estimate levels of mental stress were not studied thoroughly. In this study, we investigated time dependent variations of HRV features to detect subjects under chronic mental stress. Sixty eight subjects were divided into high (n=10) and low stress group (n=43) depending on their self-reporting stress scores. HRV features were calculated during three different time periods of the day. High stress group showed decreased patterns of HRV features compared to low stress group. When logistic regression analysis was performed with raw multiple HRV features, the classification was 63.2% accurate. A new % deviance score reflecting the degree of difference from normal reference patterns increased the accuracy to 66.1%. Our data suggested that HRV patterns obtained at multiple time points of the day could provide useful data to monitor subjects under chronic stress.

Ultra short term analysis of heart rate variability for monitoring mental stress in mobile settings.

Heart rate variability (HRV) analysis is commonly used as a quantitative marker depicting the activity of autonomous nervous system (ANS) that may be related to mental stress. For mobile applications, short term ECG measurement may be used for HRV analysis since the conventional five minute long recordings might be inadequately long. Short term analysis of HRV features has been investigated mostly in ECG data from normal and cardiac patients. Thus, short term HRV features may not have any relevance on the assessment of acute mental stress. In this study, we obtained ultra short term HRV features from 24 subjects during baseline stage and Stroop color word test. We validated these HRV features by showing significant differences in HRV features existed between the two stages. Our results indicated that ultra short term analysis of heart rate and RR intervals within 10 s, RMSSD and PNN50 within 30 s, HF within 40 s, LF/HF, normalized LF, and normalized HF within 50 s could be reliably performed for monitoring mental stress in mobile settings.