Pulse rate variability (PRV) analysis appears as the first alternative to heart rate variability analysis for wearable devices; however, there is a constraint on computational load and energy consumption for the limited system resources available to the devices. Considering that adjustment of the sampling frequency is one of the strategies for reducing computational load and power consumption, this study aimed to investigate the influence of sampling frequency (f ) on PRV analysis and to find the minimum sampling frequency while maintaining reliability. We generated 5000, 2500, 1000, 500, 250, 100, 50, 25, 20, 15, 10, 5 Hz down-sampled photoplethysmograms from 10 kHz-sampled PPGs and derived time- and frequency-domain variables of the PRV. These included AVNN, SDNN, SDSD, RMSSD, NN50, pNN50, total power, VLF, LF, HF, LF/HF, nLF and nHF for each down-sampled signal. Derived variables were compared with heart rate variability of the 10 kHz-sampled electrocardiograms, and then statistically investigated using one-way ANOVA test and Bland-Altman analysis. As a result, significant differences (P< 0.05) were found for SDNN, SDSD, RMSSD, NN50, pNN50, TP, HF, LF/HF, nLF and nHF, but not for AVNN, VLF and LF. Based on the post hoc tests, it was found that the NN50 and pNN50, SDSD and RMSSD, LF/HF and nHF, SDNN, TP and nLF analysis had significant differences at f ⩽  20 Hz, f ⩽  15 Hz, f ⩽10 Hz; f =  5 Hz, respectively. In other words, a significant difference was not seen for any variable if the f was greater than 25 Hz. Consequently, our pilot study suggests that analysis of variability in the time and frequency domain from pulse rate obtained through PPG may be potentially as reliable as that derived from the analysis of the electrocardiogram, provided that f ⩾25 Hz sampling frequency is used.

The purpose of this study is to quantitatively analyze the effect of an ectopic beat on heart rate variability (HRV) in the time domain, frequency domain, and in a non-linear analysis. A quantitative analysis was carried out by generating artificial ectopic beats that probabilistically contained a missed beat or a false-detected beat, and the statistical significance was evaluated though a comparison with an ectopic-free HRV by increasing the ratio of the ectopic beat in 0.1% increments from 0 to 50%. The effect of the interpolation on the ectopic HRV was also investigated by applying nearest-neighbor interpolation, linear interpolation, and cubic spline interpolation. The results confirmed a statistically significant difference (P < 0.05) even in the less-than-1% ectopic HRV in every domain. When interpolation was applied, there were differences according to the interpolation method used, but statistical significance was secured for an ectopic beat ratio from 1 to 2% to several tens of a percent. In the effect, linear interpolation, and spline interpolation were confirmed to have a higher effect on the high-frequency related HRV variables, and nearest-neighbor interpolation had a higher effect on low-frequency related variables.

This study developed a novel and simple method for fabricating high-temperature ultralight, fast responsive, flexible transparent Ni-based heaters using the laser digital patterning (LDP) process of a nonstoichiometric solution-processed NiOx...

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.