Real-time Pulse Research Articles

Association of Wearable Derived Pulse Rate Variability With Pediatric Appendectomy Complications

IntroductionPostoperative recovery of children is difficult to gauge by parents after hospital discharge. Consumer wearable devices (CWD) generate valid and near real-time pulse rate data, integer pulse rate variability (PRVi), that can serve as digital biomarkers for the onset of complications during post-discharge recovery. This study sought to explore whether pediatric patients with surgical complications after appendectomy exhibited a CWD-derived PRVi trajectory that differs from the normative PRVi recovery trajectory. MethodsIn this retrospective analysis, children aged 3–17 undergoing appendectomy for complicated appendicitis was invited to participate. Participants wore a Fitbit device for 21 days postoperatively. PRVi was calculated as the variance, defined as the sum of the squared differences of each value from the mean, of the heart rate in beats per minute measured over 5 consecutive minutes. The mean daily PRVi was calculated for patients recovering with and without complications. ResultsNinety-eight patients were enrolled in the study; 60 (61 %) were ages 3–11, 54 (55 %) were female, and 57 (58 %) were Caucasian. Eleven (11 %) developed surgical complications. Those with complications experienced a slower increase in the mean PRVi over the 21-day period. ConclusionsIn this prospective observational study, it was demonstrated that PRVi derived from CWD data can be used as an objective digital biomarker to profile surgical complications. This finding provides clinicians with an additional tool for monitoring children during the postoperative period, enhancing their ability to proactively detect and address complications. Level of EvidenceIV.

Physics-Informed Graph Neural Networks to solve 1-D equations of blood flow

Background and Objective:Computational models of hemodynamics can contribute to optimizing surgical plans, and improve our understanding of cardiovascular diseases. Recently, machine learning methods have become essential to reduce the computational cost of these models. In this study, we propose a method that integrates 1-D blood flow equations with Physics-Informed Graph Neural Networks (PIGNNs) to estimate the propagation of blood flow velocity and lumen area pulse waves along arteries. Methods:Our methodology involves the creation of a graph based on arterial topology, where each 1-D line represents edges and nodes in the blood flow analysis. The innovation lies in decoding the mathematical data connecting the nodes, where each node has velocity and lumen area pulse waveform outputs. The training protocol for PIGNNs involves measurement data, specifically velocity waves measured from inlet and outlet vessels and diastolic lumen area measurements from each vessel. To optimize the learning process, our approach incorporates fundamental physical principles directly into the loss function. This comprehensive training strategy not only harnesses the power of machine learning but also ensures that PIGNNs respect fundamental laws governing fluid dynamics. Results:The accuracy was validated in silico with different arterial networks, where PIGNNs achieved a coefficient of determination (R2) consistently above 0.99, comparable to numerical methods like the discontinuous Galerkin scheme. Moreover, with in vivo data, the prediction reached R2 values greater than 0.80, demonstrating the method’s effectiveness in predicting flow and lumen dynamics using minimal data. Conclusions:This study showcased the ability to calculate lumen area and blood flow rate in blood vessels within a given topology by seamlessly integrating 1-D blood flow with PIGNNs, using only blood flow velocity measurements. Moreover, this study is the first to compare the PIGNNs method with other classic Physics-Informed Neural Network (PINNs) approaches for blood flow simulation. Our findings highlight the potential to use this cost-effective and proficient tool to estimate real-time arterial pulse waves.

Online MR-guided proton and ion beam radiotherapy: investigation of image quality

Objective.Magnetic resonance (MR) images free of artefacts are of pivotal importance for MR-guided ion radiotherapy. This study investigates MR image quality for simultaneous irradiation in an experimental setup using phantom imaging as well asin-vivoimaging. Observed artefacts are described within the study and their cause is investigated with the goal to find conclusions and solutions for potential future hybrid devices.Approach.An open MR scanner with a field strength of 0.25 T has been installed in front of an ion beamline. Simultaneous magnetic resonance imaging and irradiation using raster scanning were performed to analyze image quality in dedicated phantoms. Magnetic field measurements were performed to assist the explanation of observed artifacts. In addition,in-vivoimages were acquired by operating the magnets for beam scanning without transporting a beam.Main Results.The additional frequency component within the isocenter caused by the fringe field of the horizontal beam scanning magnet correlates with the amplitude and frequency of the scanning magnet steering and can cause ghosting artifacts in the images. These are amplified with high currents and fast operating of the scanning magnet. Applying a real-time capable pulse sequencein-vivorevealed no ghosting artifacts despite a continuously changing current pattern and a clinical treatment plan activation scheme, suggesting that the use of fast imaging is beneficial for the aim of creating high quality in-beam MR images. This result suggests, that the influence of the scanning magnets on the MR acquisition might be of negligible importance and does not need further measures like extensive magnetic shielding of the scanning magnets.Significance.Our study delimited artefacts observed in MR images acquired during simultaneous raster scanning ion beam irradiation. The application of a fast pulse sequence showed no image artefacts and holds the potential that online MR imaging in future hybrid devices might be feasible.

Personalized whole-brain activity patterns predict human corticospinal tract activation in real-time.

Transcranial magnetic stimulation (TMS) interventions could feasibly treat stroke-related motor impairments, but their effects are highly variable. Brain state-dependent TMS approaches are a promising solution to this problem, but inter-individual variation in lesion location and oscillatory dynamics can make translating them to the poststroke brain challenging. Personalized brain state-dependent approaches specifically designed to address these challenges are therefore needed. As a first step towards this goal, we tested a novel machine learning-based EEG-TMS system that identifies personalized brain activity patterns reflecting strong and weak corticospinal tract (CST) output (strong and weak CST states) in healthy adults in real-time. Participants completed a single-session study that included the acquisition of a TMS-EEG-EMG training dataset, personalized classifier training, and real-time EEG-informed single pulse TMS during classifier-predicted personalized CST states. MEP amplitudes elicited in real-time during personalized strong CST states were significantly larger than those elicited during personalized weak and random CST states. MEP amplitudes elicited in real-time during personalized strong CST states were also significantly less variable than those elicited during personalized weak CST states. Personalized CST states lasted for ~1-2 seconds at a time and ~1 second elapsed between consecutive similar states. Individual participants exhibited unique differences in spectro-spatial EEG patterns between personalized strong and weak CST states. Our results show for the first time that personalized whole-brain EEG activity patterns predict CST activation in real-time in healthy humans. These findings represent a pivotal step towards using personalized brain state-dependent TMS interventions to promote poststroke CST function.

Revolutionizing Chronic Heart Disease Management: The Role of IoT-Based Ambulatory Blood Pressure Monitoring System.

Chronic heart disease (CHD) is a widespread and persistent health challenge that demands immediate attention. Early detection and accurate diagnosis are essential for effective treatment and management of this condition. To overcome this difficulty, we created a state-of-the-art IoT-Based Ambulatory Blood Pressure Monitoring System that provides real-time blood pressure readings, systolic, diastolic, and pulse rates at predefined intervals. This unique technology comes with a module that forecasts CHD's early warning score. Various machine learning algorithms employed comprise Naïve Bayes, K-Nearest Neighbors (K-NN), random forest, decision tree, and Support Vector Machine (SVM). Using Naïve Bayes, the proposed model has achieved an impressive 99.44% accuracy in predicting blood pressure, a vital aspect of real-time intensive care for CHD. This IoT-based ambulatory blood pressure monitoring (IABPM) system will provide some advancement in the field of healthcare. The system overcomes the limitations of earlier BP monitoring devices, significantly reduces healthcare costs, and efficiently detects irregularities in chronic heart diseases. By implementing this system, we can take a significant step forward in improving patient outcomes and reducing the global burden of CHD. The system's advanced features provide an accurate and reliable diagnosis that is essential for treating and managing CHD. Overall, this IoT-based ambulatory blood pressure monitoring system is an important tool for the early identification and treatment of CHD in the field of healthcare.

Wireless Pulse Wave Monitoring System Based on Reflective Flexible Probe and AFE4490

Pulse rate and blood oxygen levels are crucial physiological parameters that reflect physiological and pathological information within the human body. The system designs a wireless pulse wave monitoring system utilizing a flexible reflective probe and the AFE4490, which is capable of monitoring pulse wave and blood oxygen levels on the human forehead. The system is predominantly based on a reflective flexible probe, the AFE4490, a power supply module, a control microcontroller unit (MCU), and a Wi-Fi module. Post-processing by a slave computer, the collected pulse wave data is wirelessly transmitted to a smartphone. The real-time pulse waveform, pulse rate, and blood oxygen levels are displayed on an application. Following relevant tests and verifications, the system can accurately detect pulse wave signals, meet the requirements for wearable technology, and possesses significant market application potential.

Development and feasibility assessment of a virtual reality-based aerobic exercise program with real-time pulse rate monitoring on hemodynamic and arterial stiffness in healthy people: a pilot study.

Virtual reality (VR) exercises are reportedly beneficial as a physical activity tool for health promotion and rehabilitation, and can also help individuals exercise under professional supervision. We developed and investigated the potential feasibility of a VR-based aerobic exercise program using the XBOX ONE console and Kinect sensor with real-time pulse rate monitoring. The VR setting consisted of two-dimensional (2D) environments via computer, laptop, or television screens. In addition, the study investigated the potential feasibility of the VR-based exercise program on hemodynamic response and arterial stiffness in healthy participants of various ages. Healthy participants (n = 30) aged > 18 years were enrolled in the VR exercise-based program. All participants were required to wear a polar heart rate (HR) monitor set for moderate-intensity exercise, targeting 40%-59% of their HR reserve. Hemodynamic and arterial stiffness (pulse wave velocity) were noninvasively measured. The Borg scale rate of perceived exertion (RPE) was also assessed. Following a VR-guided exercise routine, all participants performed moderate-intensity exercise with no adverse health outcomes during or after the exercise. The effects of VR-based aerobic exercise extended beyond enhanced central hemodynamic and arterial stiffness. However, neither hemodynamic nor arterial stiffness showed significant differences before and after the VR exercise, except for a higher RPE response following the exercise program. VR-based aerobic exercise with pulse rate monitoring is a promising physical activity tool to induce physiological changes and impact dyspnea scales and is also feasible for administration to healthy populations.

S-method based on modified group delay for time-frequency analysis

A noise robust time-frequency representation based on the S-method (SM) and modified group delay (MGD) (S-MGD) is proposed in this article. The SM is free from cross-terms as it is the sum of individual Wigner distributions of different signal components and provides high-energy concentration both in time and frequency. The MGD reduces the Gibbs ripple and spurious spectral peaks because of noise, without using any frequency smoothing, and thus maintains the best frequency resolution as that of rectangular window. Hence,a combination of SM and MGD provides the desirable features of both the methods. The proposed method is tested for different signals like time varying random process, frequency shift keying, synthesized car engine sound/pressure signals using a solo combustion and a real-time echolocation pulse emitted by a big brown bat (Eptesicus fuscus) which indicate that the new S-MGD has improved noise immunity and frequency resolution compared to the method that uses frequency smoothing to suppress noise and Gibbs effects.

Single-point curved fiber optic pulse sensor for physiological signal prediction based on the genetic algorithm-support vector regression model

For hypertensive patients, a real-time human physiological signal monitoring system helps to track blood pressure status and provides valuable data to clinicians for early diagnosis and timely intervention. Optical fiber pulse sensing has superior conditions such as resistance to electromagnetic interference and richness of measured pulse characteristics. In this study, a monitoring system based on a single-point curved fiber pulse sensor (CFPS) was used to collect the pulse wave signal of a human radial artery. The features of the pulse wave signal were used to estimate the pulse wave transit time (PTT) and Blood Pressure—systolic blood pressure (SBP) and diastolic blood pressure (DBP)—based on support vector regression (SVR) optimized by a genetic algorithm (GA) (GA-SVR model). The results show that the root mean square error (RMSE) of SBP, DBP and PTT were 1.571 mmHg, 3.250 mmHg and 5.719 ms, respectively, and the results met the Advancement of Medical Instrumentation (AAMI) criteria. Therefore, a real-time pulse wave monitoring system based on a single-point CFPS can well predict human physiological signals such as PTT, SBP and DBP.

Toward real-time digital pulse process algorithms for CsI(Tl) detector array at external target facility in HIRFL-CSR

Toward real-time digital pulse process algorithms for CsI(Tl) detector array at external target facility in HIRFL-CSR

High-speed real-time periodic weak pulse signal detection with simplified phase-weighted stacking.

Phase-weighted stacking (PWS) is an efficient noise reduction technique widely used in exploration seismology. It uses the coherence of the instantaneous phase to enhance signals by reducing incoherent noise. However, the high computational cost makes it difficult to apply PWS for the real-time detection of weak signals with high repetition frequency. This paper proposes a novel simplified PWS method with low computational complexity. The complex plane is divided into four quadrants, and the instantaneous phases in the same quadrant are simplified to the same phase. Based on the proposed method combined with a one-bit analog-to-digital converter, a novel field-programmable gate array-based high-speed real-time periodic weak pulse signal detection technique is presented. A prototype is implemented to verify the proposed technique. For weak pulse signals with an input signal-to-noise ratio (SNR) of -21 dB to -15 dB for 2500 cycles, the results obtained show that the simplified PWS algorithm can improve the SNR of the coherently integrated signals by about 10-15 dB with a latency of about 2 µs.

Real-time evolution dynamics during transitions between different dissipative soliton states in a single fiber laser.

Various dissipative soliton solutions exist in the parameter space of mode-locked fiber lasers, including both coherent and incoherent pulses. Novel ultrafast laser designs can lead to distinctive dissipative soliton solutions formed by unique pulse shaping dynamics in the same cavity. However, transitionary states in between steady-state mode-locked regimes remain largely unexplored. Here, we investigate the intermediate transition dynamics in a versatile Tm-doped fiber laser capable of emitting both dissipative solitons with anomalous-dispersion and normal-dispersion pulse-shaping mechanisms by adjusting an intracavity polarization controller. Real-time pulse dynamics during mode-locking transitions are analyzed with a modified dispersive Fourier transform setup, illustrating characteristic pulse shaping mechanisms typically reserved for different dispersion regimes. Combined with a spectral intensity correlation analysis, the coherence evolution between two distinct mode-locked states is fully resolved for the first time.

Hybrid Domain Efficient Modulation-Based Deceptive Jamming Algorithm for Nonlinear-Trajectory Synthetic Aperture Radar

Deception jamming of synthetic aperture radar (SAR) has attracted extensive attention due to its low power consumption and high fidelity advantages. However, existing SAR deception jamming algorithms assume that SAR operates on a linear trajectory. In practice, SAR trajectories often become nonlinear due to factors such as atmospheric turbulence, which results in the jamming signals lacking the two-dimensional spatial variability of nonlinear-trajectory SAR echo signal and affects the imaging quality of deception jamming. This paper proposes a new algorithm for nonlinear-trajectory airborne SAR deception jamming based on hybrid domain efficient (HDE) modulation. This algorithm derives the jamming frequency response (JFR) with SAR trajectory deviation in the azimuth time–frequency hybrid domain. Based on the hybrid domain modulation, the jammer calculates the JFR of the linear trajectory in the azimuth frequency domain and constructs for the real-time trajectory deviation pulse by pulse at each azimuth moment. The real-time modulation process of the algorithm only involves range domain Fourier transform and complex multiplication, combining computational efficiency and modulation flexibility. The validity constraints of the algorithm have been analyzed to ensure the focusing ability of the jamming signal. Simulation and computational complexity analysis validate the excellent performance of the algorithm in imaging quality and efficiency.

Non-Collinear Attosecond Streaking without the Time Delay Scan

Attosecond streaking provides an extremely high temporal resolution for characterizing light pulses and photoionization processes with attosecond (10−18 s) accuracy, which employs a laser as a streaking field to deflect electrons generated by photoionization. The current attosecond streaking requires a time delay scan between the attosecond pulses and streaking field with attosecond accuracy and a femtosecond range, which is difficult to realize real-time measurement. In this study, we theoretically propose a non-collinear attosecond streaking scheme without the time delay scan, enabling real-time and even the potential to perform single-shot attosecond pulse measurement. In the proposal, time-delay information is projected into longitudinal space, both horizontally and vertically, enabling attosecond pulse characterization with temporal-spatial coupling. From our calculation, down to 70 as pulses with pulse front and wavefront tilt are characterized with high accuracy. Our study not only provides a method toward real-time attosecond pulse measurement, but also an approach for attosecond pump-probe experiments without time delay scan.

Biocompatible Piezoelectric Polymer Poly(Lactic Acid)-Based Stethoscope for Wearable Health Monitoring Devices

With the development of wearable devices and the Internet of Things (IoT), there is a growing demand for portable and comfortable human health monitoring devices. Because of the advantages of biocompatibility, relatively large piezoelectric coefficients, and nonpyroelectricity, poly(L-lactic acid) (PLLA) polymers have been greatly utilized in the applications of tissue engineering, sensors, and energy harvesting devices. In this investigation, the PLLA film was fabricated for the first time for real-time wearable stethoscope and pulse wave sensor applications. First, the stretched PLLA film was glued onto a Mylar film as the active layer of the PLLA film-based stethoscope (PFS) and pulse sensor device. After the PFS device was measured in a semianechoic chamber, the device showed a sensitivity of –53.4 dB at 1 kHz and it also exhibited a stable frequency response in the range of 100 Hz–4 kHz. In addition, the PFS device showed an output voltage of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim $ </tex-math></inline-formula> 90 mV when measuring the heart rate and pulse signal of an adult volunteer. Furthermore, the PFS device was integrated a portable stethoscope system, which can detect a clear pulse signal and send it to a data collection application designed in a cellphone. The biodegradability, stable frequency response, and temperature stability indicate the great potential of PFS device for wearable human health monitoring devices.

Pulse-to-pulse field characterization at x-ray free-electron lasers using a speckle-correlation scattering matrix

In coherent diffraction experiments, knowledge of the incident field is important to extract pure sample-induced data from measured diffraction patterns. However, this becomes challenging in x-ray free-electron lasers (XFELs), as incident fields vary from pulse to pulse due to the stochastic nature of self-amplified stimulated emission. Despite various proposed field retrieval methods, single-shot characterization of each pulse remains elusive. To address this, we propose the speckle-correlation scattering matrix as a solution. Our method directly reconstructs the complex field without sample constraints or multiple acquisitions by introducing a designed diffuser before the detector. We demonstrate the robustness of our approach through successful field reconstructions in various experimental configurations. Based on the retrieved field results, pulse-to-pulse variations in pulse intensity, position, illumination angle, and shape were analyzed. We believe our method can readily serve as an on-field and real-time pulse diagnostic tool at XFELs and improve the overall quality of all experiments performed at XFELs.

Ultrathin Mo2S3 Nanowire Network for High-Sensitivity Breathable Piezoresistive Electronic Skins.

Flexible piezosensing electronic skins (e-skins) have attracted considerable interest owing to their applications in real-time human-health monitoring, human-machine interactions, and soft bionic robot perception. However, the fabrication of piezosensing e-skins with high sensitivity, biological affinity, and good permeability at the same time is challenging. Herein, we designed and synthesized Mo2S3 nanowires by inserting ∞1[Mo2+S] chains between MoS2 interlayers. The resulting Mo2S3 nanowires feature high conductivity (4.9 × 104 S m-1) and a high aspect ratio (∼200). An ultrathin (∼500 nm) Mo2S3 nanowire network was fabricated using a simple liquid/liquid interface self-assembly method, showing high piezoresistive sensitivity (5.65 kPa-1), a considerably low pressure detection limit (0.08 Pa), and gratifying air permeability. Moreover, this nanowire network can be directly attached to human skin for real-time human pulse detection, finger movement monitoring, and sign language recognition, exhibiting excellent potential for health monitoring and human-machine interactions.

Real-time data acquisition and discharge pulse analysis in controlled RC circuit based Micro-EDM

Real-time data acquisition and discharge pulse analysis in controlled RC circuit based Micro-EDM

Development of Real-Time Cuffless Blood Pressure Measurement Systems with ECG Electrodes and a Microphone Using Pulse Transit Time (PTT).

Research has shown that pulse transit time (PTT), which is the time delay between the electrocardiogram (ECG) signal and the signal from a photoplethysmogram (PPG) sensor, can be used to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP) without the need for a cuff. However, the LED of the PPG sensor requires the precise adjustment of both light intensity and light absorption rates according to the contact status of the light-receiving element. This results in the need for regular calibration. In this study, we propose a cuffless blood pressure monitor that measures real-time blood pressure using a microphone instead of a PPG sensor. The blood pulse wave is measured in the radial artery of the wrist using a microphone that can directly measure the sound generated by a body rather than sending energy inside the body and receiving a returning signal. Our blood pressure monitor uses the PTT between the R-peak of the ECG signal and two feature points of the blood pulse wave in the radial artery of the wrist. ECG electrodes and circuits were fabricated, and a commercial microelectromechanical system (MEMS) microphone was used as the microphone to measure blood pulses. The peak points of the blood pulse from the microphone were clear, so the estimated SBP and DBP could be obtained from each ECG pulse in real time, and the resulting estimations were similar to those made by a commercial cuff blood pressure monitor. Since neither the ECG electrodes nor the microphone requires calibration over time, the real-time cuffless blood pressure monitor does not require calibration. Using the developed device, blood pressure was measured three times daily for five days, and the mean absolute error (MAE) and standard deviation (SD) of the SBP and DBP were found to be 2.72 ± 3.42 mmHg and 2.29 ± 3.53 mmHg, respectively. As a preliminary study for proof-of-concept, these results were obtained from one subject. The next step will be a pilot study on a large number of subjects.

Sustainable Smart Helmet For Coal Miners

The “Sustainable Smart Safety Helmet for Coal Miners” paper aims to enhance the safety and well-being of coal miners by implementing an innovative monitoring system. The paper utilizes the ESP32 microcontroller, integrated with a suite of sensors, including DHT11 for temperature and humidity measurement, MQ135 for gas detection, IR Sensor for helmet status monitoring, and a Heartbeat Sensor for real-time pulse rate tracking. A Buzzer is also integrated to provide immediate audible alerts when potential hazards are detected. The system continuously collects sensor data in real-time, which is then processed and analysed within the ESP32 microcontroller. The data is transmitted to a user-friendly mobile application developed using Blynk. Through the Blynk app, miners and relevant personnel receive critical information regarding environmental conditions and the miners' vital signs. In case of any hazardous conditions, the system generates prompt alerts, triggering the Buzzer and sending notifications to the app to ensure timely responses to potential threats. By combining advanced sensor technology, real-time data monitoring, and swift alert mechanisms, this paper endeavours to safeguard the lives of coal miners in hazardous working environments, providing them with a smart safety solution and empowering them to work with increased confidence and security.