Hz Sampling Rate Research Articles

The Impact of Optical Undersampling on the Ca2+ Signal Resolution in Ca2+ Imaging of Spontaneous Neuronal Activity.

In neuroscience, Ca2+ imaging is a prevalent technique used to infer neuronal electrical activity, often relying on optical signals recorded at low sampling rates (3 to 30 Hz) across multiple neurons simultaneously. This study investigated whether increasing the sampling rate preserves critical information that may be missed at slower acquisition speeds. Primary neuronal cultures were prepared from the cortex of newborn pups. Neurons were loaded with Oregon Green BAPTA-1 AM (OGB1-AM) fluorescent indicator. Spontaneous neuronal activity was recorded at low (14 Hz) and high (500 Hz) sampling rates, and the same neurons (n = 269) were analyzed under both conditions. We compared optical signal amplitude, duration, and frequency. Although recurring Ca2+ transients appeared visually similar at 14 Hz and 500 Hz, quantitative analysis revealed significantly faster rise times and shorter durations (half-widths) at the higher sampling rate. Small-amplitude Ca2+ transients, undetectable at 14 Hz, became evident at 500 Hz, particularly in the neuropil (putative dendrites and axons), but not in nearby cell bodies. Large Ca2+ transients exhibited greater amplitudes and faster temporal dynamics in dendrites compared with somas, potentially due to the higher surface-to-volume ratio of dendrites. In neurons bulk-loaded with OGB1-AM, cell nucleus-mediated signal distortions were observed in every neuron examined (n = 57). Specifically, two regions of interest (ROIs) on different segments of the same cell body displayed significantly different signal amplitudes and durations due to dye accumulation in the nucleus. Our findings reveal that Ca2+ signal undersampling leads to three types of information loss: (1) distortion of rise times and durations for large-amplitude transients, (2) failure to detect small-amplitude transients in cell bodies, and (3) omission of small-amplitude transients in the neuropil.

Experimental validation of a wave-height sensor based on a MEMS piezoresistive cantilever and a waterproof film

Abstract This paper presents a barometric pressure-sensing wave-height sensor that employs a microelectromechanical system (MEMS) piezoresistive cantilever, an air chamber, and a micromesh waterproof film. The developed sensor has a height resolution of less than 5 mm in the 0.01–10 Hz frequency band and a high time resolution with 100 Hz sampling rate. We conducted wave height measurements at sea using the developed sensor and compared its performance with that of a commercial GPS buoy-type wave-height sensor.

A Hybrid Photoplethysmography (PPG) Sensor System Design for Heart Rate Monitoring.

A photoplethysmography (PPG) sensor is a cost-effective and efficacious way of measuring health conditions such as heart rate, oxygen saturation, and respiration rate. In this work, we present a hybrid PPG sensor system working in a reflective mode with an optoelectronic module, i.e., the combination of an inorganic light-emitting diode (LED) and a circular-shaped organic photodetector (OPD) surrounding the LED for efficient light harvest followed by the proper driving circuit for accurate PPG signal acquisition. The performance of the hybrid sensor system was confirmed by the heart rate detection process from the PPG using fast Fourier transform analysis. The PPG signal obtained with a 50% LED duty cycle and 250 Hz sampling rate resulted in accurate heart rate monitoring with an acceptable range of error. The effects of the LED duty cycle and the LED luminous intensity were found to be crucial to the heart rate accuracy and to the power consumption, i.e., indispensable factors for the hybrid sensor.

Barefoot vs shod walking and jogging on the electromyographic activity of the medial and lateral gastrocnemius

Gastrocnemius weakness is associated with Achilles tendinopathies and muscle strains, with the medial gastrocnemius (MG) more commonly injured than the lateral gastrocnemius (LG). Walking and jogging are common in daily activities and sports, and biomechanical differences between shod and barefoot exercise may influence MG and LG activation. Understanding these activation patterns could help optimize training programs for injury prevention and/or rehabilitation. The aim was to compare MG and LG electromyographic activity during walking and jogging, both shod and barefoot. Twenty-nine participants (25.28 ± 4.53 years, 171.31 ± 0.76 cm, 72.68 ± 6.36 kg) completed a warm-up followed by 1 min of walking (80–99 steps/min) and jogging (130–150 steps/min) in both conditions (barefoot and shod, random order). Electromyographic signals were recorded using wearable devices (mDurance Solutions S.L., Granada, Spain; 1024 Hz sampling rate). We measured the root-mean-square (RMS) amplitudes for an entire stride cycle and digitally filtered the signals. For analysis, we normalized electromyographic values to the average peak values obtained during two sprints. We analyzed differences with a repeated-measures analysis of variance. Significant effects of condition (barefoot-shod) and gastrocnemius (MG-LG) were observed (all p ≤ 0.023, ƞp2 = 0.17–0.39), with higher MG activation compared to LG in the barefoot conditions (p = 0.004–0.027, d = 0.72–0.83), and nonsignificant differences between muscles in the shod conditions (p > 0.05). Shod exercise compared to barefoot resulted in lower MG activation (p = 0.001–0.003, d = 0.62–0.63) and non-significant differences in LG activation. These results indicate that barefoot walking and jogging increase MG activation compared to shod conditions, with no differences in LG activation. Additionally, footwear reduces differences between MG and LG.

Performance and safety of volume guarantee ventilation in neonates using the fabian ventilator, a multicenter study.

To evaluate the performance (i.e., agreement between set and measured parameters) and safety (adverse events, device malfunctions, and ventilator alarms) of the fabian HFOi neonatal ventilator in volume guaranteed (VG) mode during conventional ventilation. To analyze the impact of leakage around the endotracheal tube and the set maximum allowed inflating pressure (Pmax). Prospective multicenter observational study. Clinical and ventilator data were collected from 71 infants receiving VG ventilation for ≥12 h in four neonatal intensive care units (NICUs). Ventilator settings, parameters, and alarms were downloaded with 0.5 Hz sampling rate. Data from 4,341 h of ventilation were analyzed. The median (interquartile range, IQR) of the absolute difference between the target and measured expired tidal volume was 0.76 (0.51-1.16) mL/kg. It was less when leak was <50% (median 0.36, IQR: 0.25-0.64 mL/kg, p < .001) and even less when the required peak inflating pressure (PIP) was also below Pmax (median: 0.09 mL/kg, IQR: 0.00-0.16 mL/kg, p < .001). On NICUs setting Pmax higher, tidal volume was maintained significantly closer to target. In 56 patients VG was continued until extubation. Two ventilator malfunctions were reported, none of them resulting in patient harm. "Tidal volume not reached" alarm occurred 32 times hourly, usually lasting for <10 s. The fabian HFOi ventilator maintains tidal volume close to its target, particularly when leak is <50% and when PIP is below Pmax. In most patients VG can be continued until extubation. Despite frequent ventilator alarms, ventilator malfunctions occur very rarely.

Citizen science: Development of a low-cost magnetometer system for a coordinated space weather monitoring

As part of Ham Radio Science Citizen Investigation (HamSCI) Personal Space Weather Station (PSWS) project, a low-cost, commercial off-the-shelf magnetometer has been developed to provide quantitative and qualitative measurements of the geospace environment from the ground for both scientific and operational purposes at a cost that will allow for crowd-sourced data contributions. The PSWS magnetometers employ a magneto-inductive sensor technology to record three-axis magnetic field variations with a field resolution of ∼3 nT at a 1 Hz sample rate. The measurement range of the sensor is ±1.1×106 nT) and is valid over a temperature range of −40 °C to +85 °C. Data from the PSWS network will combine these magnetometer measurements with high frequency (HF, 3–30 MHz) radio observations to monitor large-scale current systems and ionospheric disturbances due to drivers from both space and the atmosphere. A densely-spaced magnetometer array, once established, will demonstrate their space weather monitoring capability to an unprecedented spatial extent. Magnetic field data obtained by the magnetometers installed at various locations in the US are presented and compared with the existing magnetometers nearby, demonstrating that the performance is very adequate for scientific investigations.

Seismoacoustic Wavefield at Popocatépetl Volcano, Mexico, Captured by a Temporary Broadband Network from 2021 to 2022

Abstract Popocatépetl is a highly active stratovolcano in central Mexico with recurrent activity of Vulcanian-type explosions and frequent degassing. The proximity of Popocatépetl volcano to Mexico City, one of the most populated cities in the world, demands continuous monitoring to achieve an adequate volcano risk assessment. We present an overview of the first high-dynamic-range and high-broadband (0.01–200 Hz; 400 Hz sampling rate) seismoacoustic network (PoPiNet), which we operated around Popocatépetl volcano from August 2021 to May 2022. Here, we show preliminary results of the explosions recorded in September 2021. We deployed five seismoacoustic stations within 4–25 km horizontal distance (range) from the vent. We identify infrasonic waveforms associated with tremor and explosions, with pressures ranging from 16 to 134 Pa and dominant frequencies between 0.2 and 5.0 Hz. The frequency content of the recorded signals at the closest stations to the volcano spans the sub-bass (20–60 Hz) and bass (60–250 Hz) ranges. The associated seismic signals of moderate explosions exhibit air-to-ground coupled waves with maximum coherence values at frequencies up to 5 and 25 Hz for the farthest and closest stations to the volcano, respectively. Conversely, we observe infrasound signal amplitudes from relatively small explosions reaching maximum pressures of 10 Pa that do not couple into the ground, even at the closest stations. These infrasound signals are associated with type-I long-period events as reported in previous investigations. The waveform consistency suggests repetitive and nondestructive sources beneath the volcano.

Evaluation of the single-frequency variometric approach based on low-cost GNSS observations and different satellite combinations for detecting short-term dynamic behaviors

This study presents the capability of the single-frequency (SF) variometric approach (VA) technique with low-cost GNSS observations to detect short-term dynamic behaviors. Harmonic oscillations with amplitudes between 5 and 20 mm and frequencies between 0.3 and 5.0 Hz were generated employing a single-axis shake table to investigate the performance of the SF-VA technique in the structural health monitoring (SHM) system. Besides, a Mw 6.9 Kobe, 1995 earthquake simulation was generated using the shake table to analyze the SF-VA performance for the earthquake early warning (EEW) system. A low-cost u-blox ZED-F9P GNSS receiver and ANN-MB-00 patch antenna were used to collect GNSS observations at a 20 Hz sampling rate during the experiments. The observations were processed using the MATLAB-based open-source PPPH-VA software in real-time (RT) mode, considering eight different satellite combinations. The capability of the SF-VA technique to detect horizontal dynamic behaviors in RT mode was investigated in the frequency and time domains, accepting the displacements from the linear variable differential transformer sensor as a reference. The results in the frequency domain demonstrate that the SF-VA technique with low-cost GNSS observations can successfully detect the peak frequency value of short-term harmonic oscillations up to 5 Hz. Moreover, time domain findings emphasize that the short-time dynamic oscillations can be determined with the SF-VA technique with an accuracy ranging from 0.8 to 6.4 mm. Earthquake simulation experiment results demonstrate that the strong ground motions caused by mega earthquakes can be determined at mm-level by the SF-VA method. The results of both experiments show that multi-GNSS observations contribute to the SF-VA technique considerably. Overall, the findings reveal that the SHM and EEW systems can be operated with low-cost GNSS receivers, and the natural frequency of the man-made structures and accurate displacement values of seismic waveforms can be determined in RT with the SF-VA technique.

High Spatiotemporal Resolution Radial Encoding Single-Vessel fMRI.

High-field preclinical functional MRI (fMRI) is enabled the high spatial resolution mapping of vessel-specific hemodynamic responses, that is single-vessel fMRI. In contrast to investigating the neuronal sources of the fMRI signal, single-vessel fMRI focuses on elucidating its vascular origin, which can be readily implemented to identify vascular changes relevant to vascular dementia or cognitive impairment. However, the limited spatial and temporal resolution of fMRI is hindered hemodynamic mapping of intracortical microvessels. Here, the radial encoding MRI scheme is implemented to measure BOLD signals of individual vessels penetrating the rat somatosensory cortex. Radial encoding MRI is employed to map cortical activation with a focal field of view (FOV), allowing vessel-specific functional mapping with 50×50 µm2 in-plane resolution at a 1 to 2 Hz sampling rate. Besides detecting refined hemodynamic responses of intracortical micro-venules, the radial encoding-based single-vessel fMRI enables the distinction of fMRI signals from vessel and peri-vessel voxels due to the different contribution of intravascular and extravascular effects.

Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study

ObjectiveTo predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data. Study designThis is an observational study with prospective recordings of oxygen saturation (SpO2) and ventilator data from infants < 30 weeks’ gestation age (GA). Research pulse oximeters collected SpO2 (1 Hz sampling rate) to quantify intermittent hypoxemia (IH). Continuous ventilator metrics were collected (4-5 min sampling) from bedside ventilators. Data modeling was completed using unbiased machine learning algorithms. Three model sets were created using the following data source combinations: [1] IH and ventilator (IH + SIMV), [2] IH, and [3] ventilator (SIMV). Infants were also analyzed separated by postnatal age (infants < 2 or ≥ 2 weeks of age). Models were compared by area under the ROC curve (AUC). ResultsA total of 110 extubation events from 110 preterm infants were analyzed. Infants had a median GA and birth weight of 26 weeks and 825 grams, respectively. Of the three models presented, the IH + SIMV model achieved the highest AUC of 0.77 for all infants. Separating infants by postnatal age increased accuracy further achieving AUC of 0.94 for < 2 weeks of age group and AUC of 0.83 for ≥ 2 weeks group. ConclusionsMachine learning analysis has the potential to enhance prediction accuracy of extubation readiness in preterm infants while utilizing readily available data streams from bedside pulse oximeters and ventilators.

Wind Turbine Blade Damage Evaluation under Multiple Operating Conditions and Based on 10-Min SCADA Data

The present paper aims to enable the assessment of the fatigue damage of wind turbine blades over a long duration (e.g., several months/years) in conjunction with different operating regimes and based on two information sources: the 10-min SCADA data and an interpolation using response surfaces identified using the FAST aeroelastic numerical tool. To assess blade damage, prior studies highlighted the need for a high-frequency (&gt;1 Hz) sampling rate. Because of data availability and computation resource limitations, such methods limit the duration of the analysis period, making the direct use of such an approach based on a 1 Hz wind speed signal in current wind farms impractical. The present work investigates the possibility of overcoming these issues by estimating the equivalent damage using a 1 Hz wind speed for each 10-min sample stored in the SCADA data. In the literature, the influence of operating regimes is not considered in fatigue damage estimation, and for the first time, the present project takes a pioneering approach by considering these operating regimes.

Efficient similar waveform search using short binary codes obtained through a deep hashing technique

Summary A similar waveform search plays a crucial role in seismology for detecting seismic events, such as small earthquakes and low-frequency events. However, the high computational costs associated with waveform cross-correlation calculations represent bottlenecks during the analysis of long, continuous records obtained from numerous stations. In this study, we developed a deep-learning network to obtain 64-bit hash codes containing information on seismic waveforms. Using this network, we performed a similar waveform search for ∼35 million moving windows developed for the 30 min waveforms recorded continuously at 10 MHz sampling rates using 16 acoustic emission transducers during a laboratory hydraulic fracturing experiment. The sampling points of each channel corresponded to those of the 5.8-year records obtained from typical seismic observations at 100 Hz sampling rates. Of the 35 million windows, we searched for windows with small average Hamming distances among the hash codes of 16 channel waveforms against template hash codes of 6057 events that were catalogued using conventional autoprocessing techniques. The calculation of average Hamming distances is 1430–1530 times faster than that of the corresponding network correlation. This hashing-based template matching enabled the detection of 23,462 additional events. We also demonstrated the feasibility of the hashing-based autocorrelation analysis, where similar event pairs were extracted without templates, by calculating the average Hamming distances for all possible pairs of the ∼35 million windows. This calculation required only 15.5 h under 120 thread parallelisation. This deep hashing approach significantly reduced the required memory compared with locality-sensitive hashing approaches based on random permutations, enabling similar waveform searching on a large-scale dataset.

Resampling Strategies and their Influence on Heart Rate Variability Features in Low Sampling Rate Electrocardiogram Data

Heart rate variability (HRV) is a parameter to measure fluctuations in the interval between heartbeats. HRV provides essential insights into the cardiovascular function and autonomic nervous system. Electrocardiograms (ECG) on wearable devices are often recorded at low sampling rates, limiting temporal resolution and information. Resampling is a technique of changing the sampling rate from a high sampling rate to a lower sampling rate and vice versa. This research aims to evaluate the effect of resampling ECG data with a low sampling rate on HRV features. ECG data consists of 50 Hz and 100 Hz sampling rates. Data with a 50 Hz sampling rate is up-sampled up to 100 Hz, while 100 Hz data is down-sampled up to 50 Hz and up-sampled up to 250 Hz using the Fast Fourier Transform Interpolation Method. Upsampling from 50 Hz to 100 Hz shows unsatisfactory results, except for some HRV features such as NN20, pNN20, and CVI. Better results were found when up sampling from 100 Hz up to 250 Hz, with some HRV features showing good concordance values. However, downsampling from 100 Hz up to 50 Hz is unsuitable for HRV feature analysis. To obtain accurate HRV analysis results in all domains, it is highly recommended to use a sampling rate above 100 Hz.

Synchronization of Separate Sensors’ Data Transferred through a Local Wi-Fi Network: A Use Case of Human-Gait Monitoring

This paper addresses the challenge of synchronizing data acquisition from independent sensor systems in a local network. The network comprises microcontroller-based systems that collect data from physical sensors used for monitoring human gait. The synchronized data are transmitted to a PC or cloud storage through a central controller. The performed research proposes a solution for effectively synchronizing the data acquisition using two alternative data-synchronization approaches. Additionally, it explores techniques to handle varying amounts of data from different sensor types. The experimental research validates the proposed solution by providing trial results and stability evaluations and comparing them to the human-gait-monitoring system requirements. The alternative data-transmission method was used to compare the data-transmission quality and data-loss rate. The developed algorithm allows data acquisition from six pressure sensors and two accelerometer/gyroscope modules, ensuring a 24.6 Hz sampling rate and 1 ms synchronization accuracy. The obtained results prove the algorithm’s suitability for human-gait monitoring under its regular activity. The paper concludes with discussions and key insights derived from the obtained results.

Rheoencephalography: A non-invasive method for neuromonitoring.

In neurocritical care, the gold standard method is intracranial pressure (ICP) monitoring for the patient's lifesaving. Since it is an invasive method, it is desirable to use an alternative, noninvasive technique. The computerized real-time invasive cerebral blood flow (CBF) autoregulation (AR) monitoring calculates the status of CBF AR, called the pressure reactivity index (PRx). Studies documented that the electrical impedance of the head (Rheoencephalography - REG) can detect the status of CBF AR (REGx) and ICP noninvasively. We aimed to test REG to reflect ICP and CBF AR. For nineteen healthy subjects we recorded bipolar bifrontal and bitemporal REG derivations and arm bioimpedance pulses with a 200 Hz sampling rate. The challenges were a 30-second breath-holding and head-down-tilt (HDT - Trendelenburg) position. Data were stored and processed offline. REG pulse wave morphology and REGx were calculated. The most relevant finding was the significant morphological change of the REG pulse waveform (2nd peak increase) during the HDT position. Breath-holding caused REG amplitude increase, but it was not significant. REGx in male and female group averages have similar trends during HDT by indicating the active status of CBF AR. The morphological change of REG pulse wave during HDT position was identical to ICP waveform change during increased ICP, reflecting decreased intracranial compliance. A correlation study between ICP and REG was initiated in neurocritical care patients. The noninvasive REG monitoring would also be useful in space research as well as in military medicine during the transport of wounded service members as well as for fighter pilots to indicate the loss of CBF and consciousness.

Neural Correlates of Infant Attention During Initial Exposure to Own- and Other-Race Faces

Past research has shown that infants raised in a racially homogenous environment demonstrate an advantage for processing own-race faces at the individual level in comparison to other-race faces (Lewkowicz, 2014; Mauer &amp; Werker, 2014; Quinn, Lee, &amp; Pascalis, 2018). This advantage for processing own-race faces develops across infancy and has been referred to as the own-race bias (e.g., Anzures, Quinn, Pascalis, Slater, Tanaka, &amp; Lee, 2013). The processes that lead to development of the own-race bias are complex and not fully understood. Recent work suggests that by 10 months of age, differences in own- and other-race face processing may be related to attentional processes (Roth &amp; Reynolds, 2022). The current study utilized event-related potentials (ERP) to examine neural correlates of infant attention during initial exposure to own- or other-race faces. Level of attentional engagement was also compared across early to late trials to determine whether infants demonstrated increased or decreased attention to a face with repeated exposure depending on race. Twenty-three infants were tested at 10 months of age. Only infants raised in a racially homogenous environment (based on parental report) were included in the dataset. Participants were familiarized with 20 repeated 1000 ms presentations of a single exemplar of either an own-race face (n = 13) or an other-race face (n = 10). Following familiarization, presentations of the familiar face were interspersed with presentations of novel faces until the infant was off task. EEG was recorded throughout testing using the EGI Geodesic EEG System (GES 400) 128-channel system with a 1000 Hz sampling rate. An offline bandpass filter was applied from 0.10 to 30.00 Hz. The analysis of the EEG focused on the Nc ERP component associated with level of attentional engagement in infancy (Reynolds &amp; Richards, 2005). Mean amplitude of the Nc component was analyzed at midline-frontal electrodes from 345-650 ms following stimulus onset using a 200 ms prestimulus baseline period. Participants contributed a minimum of 8 artifact-free trials per condition to the ERP averages. Infants familiarized with an other-race face demonstrated greater amplitude Nc in comparison to infants familiarized with an own-race face (F(1,21) = 4.65, p = 0.037, ηp2 = .191). Interestingly, infants showed increased Nc amplitude to other-race faces following familiarization and decreased Nc amplitude to own-race faces following familiarization. These findings indicate that 10-month-old infants demonstrate greater attentional engagement during initial exposure to other-race faces than during initial exposure to own-race faces. This greater amplitude Nc could reflect a novelty effect for other-race faces for infants raised in racially homogenous environments. The increase in Nc amplitude following familiarization with an other-race face may be indicative of continued attentional engagement due to incomplete processing of the face. Given the potential for early developing perceptual biases to contribute to subsequent development of social biases and prejudice (Xiao et al., 2018, 2019), these findings provide important insight into potential mechanisms associated with the own-race bias in face processing in infancy.

Monitoring of Road Irregularities Using Data Analytics

Abstract: This work presents a smartphone-based system that uses orientation and acceleration data to monitor and classify road anomalies in real time. Three severity levels—mild, moderate, and severe—are assigned to irregularities by the system, which also handles their identification, classification, and geotagging. Robust approaches are employed to acknowledge and mitigate challenges such as threshold determination, vehicle-specific vibrations, and smartphone orientation normalization. To ensure a thorough dataset, data collecting entails systematic trials for different types of irregularities. Kalman filtering for noise reduction and a consistent 200 Hz sample rate are used in further preprocessing. A variety of indicators from the Y-axis data are included in feature extraction, which produces a complex dataset for model training. The decision tree approach is used to identify road segments based on extracted data because of its interpretability and transparency

Validation of a novel cuffless photoplethysmography-based wristband for measuring blood pressure according to the regulatory standard

Abstract Background Blood pressure is a key paramater in acute and chronic cardiovascular diseases (CVD) [1]. However, obtaining reliable and reproducible cuff blood pressure (BP) measurements is challenging. We demonstrate that a wristband-based PPG measurement in combination with a custom developed BP algorithm represents a possible alternative to cuff BP measurements. Purpose This study aimed to validate a novel cuffless photopletysmography (PPG)-based non-invasive wristband for continuous BP monitoring in accordance with ISO 81060-2:2019. Methods The study compared PPG-guided BP algorithm predictions with subclavian arterial reference measurements taken during cardiac catheterization. Eligible patients were consecutively included, and eligibility was screened following ISO 81060-2:2019 requirements. Reference measurements were performed using a validated invasive BP monitoring device with a sampling rate of 100Hz. PPG signals were collected using six light emission diodes and two photodiodes at a sampling rate of 128Hz. Three sequential initialization measurements were taken using a validated blood pressure cuff before the cardiac catheterization exam. These measurements, along with approximately 100 additional features (PPG-derived and based on patient demographics), were used as input for the machine learning-based BP algorithm. Correlation, mean error, and standard deviation (SD) were determined for systolic and diastolic BP measurements between the BP algorithm predictions and invasive reference measurements Results The study included 97 patients from whom 420 individual 30-second samples were obtained. The mean age, weight, and height of the analysed subjects were 67.1 (SD 11.1), 83.4 (SD 16.1), and 174.1 (SD 10.0), respectively. In 48 samples (11%), systolic BP was ≤100mmHg, while in 106 samples (25%), systolic BP was ≥160mmHg. Diastolic BP was ≤70 mmHg in 222 samples (53%) and ≥85 mmHg in 99 samples (24%). The BP algorithm predictions showed a high correlation with invasive reference measurements for systolic (r = 0.985) and diastolic (r = 0.961) BP measurements. The mean error of the BP algorithm predictions compared to the invasive reference measurements was ±3.7 mmHg (SD 4.4 mmHg) and ±2.5 mmHg (SD 3.7 mmHg) for systolic and diastolic BP, respectively. Results were similar within each gender and skin colour category (Fitzpatrick I-VI). Conclusion This study demonstrates that a wristband-based PPG measurement in combination with the developed BP algorithm can provide accurate continuous BP measurements across a wide range of BP distributions. Therefore, wristband BP monitoring may serve as a valid and less burdensome alternative to cuff BP measurements for both in-hospital and at-home BP monitoring. However, further research is necessary to evaluate the precision of the BP algorithm during movement and the stability of the predictions over time.figure of BP

Effects of Patterned Electromagnetic Fields and Light-Emitting Diodes on Cancer Cells: Impact on Cell Density and Biophoton Emission When Applied Individually vs. Simultaneously

It has been previously reported that time-varying EMFs and LEDs have the potential to modulate cellular activity and cell viability. It has also been shown that cellular activity and state can be inferred by measuring the biophoton emission derived from these same cells. To identify if the brief application (15 min) of an LED (635 nm at 3 klx) or EMF (1–3 uT) could influence cell growth and subsequent biophoton emission characteristics, B16-BL6 cells were grown to confluence and exposed to a time-varying, frequency-modulated EMF, LED, or both. Before and after EMF and LED exposure, photon emission measurements were taken for 1 min at a 50 Hz sampling rate. Following the exposure and photon emission measurements, cell viability was assessed via the use of a hemocytometer. The results demonstrated that after only 15 min of exposure to a time-varying EMF, there was a 41.6% reduction in viable cells when compared to sham controls [t(25) = 2.4, p = 0.02]. This effect approached significance in the LED alone condition [p = 0.07] but was completely absent in the condition wherein the LED and EMF were applied simultaneously [p &lt; 0.8]. Additionally, following exposure to only the LED, there was a significant increase in biophoton emission SPD values at 13 Hz from whole cell cultures [t(60) = 2.3, p = 0.021]. This biophoton emission frequency was also strongly correlated with the number of nonviable cells [r = −0.514] in the dish. Taken together, these data point to biophotons emitted from cell cultures at 13 Hz as a potential indicator of the number of nonviable cells in vitro. The summation of data here corroborates previous work demonstrating the efficacy of specific time-varying EMFs as a novel therapeutic for the inhibition of cancer cell growth. It also furthers our assertion that biophoton emission can be used as a novel detection tool for cell activity.

Predicting battery impedance spectra from 10-second pulse tests under 10 Hz sampling rate

Onboard measuring the electrochemical impedance spectroscopy (EIS) for lithium-ion batteries is a long-standing issue that limits the technologies such as portable electronics and electric vehicles. Challenges arise from not only the high sampling rate required by the Shannon Sampling Theorem but also the sophisticated real-life battery-using profiles. We here propose a fast and accurate EIS predicting system by combining the fractional-order electric circuit model-a highly nonlinear model with clear physical meanings-with a median-filtered neural network machine learning. Over 1000 load profiles collected under different state-of-charge and state-of-health are utilized for verification, and the root-mean-squared-error of our predictions could be bounded by 1.1mΩ and 2.1mΩ when using dynamic profiles last for 3min and 10s, respectively. Our method allows using size-varying input data sampled at a rate down to 10Hz and unlocks opportunities to detect the battery's internal electrochemical characteristics onboard via low-cost embedded sensors.