Objective. Pendelluft is the movement of air between lung regions, which results in an apparent phase-shift of the aeration curve between lung regions. Electrical impedance tomography (EIT) can be used to detect and quantify pendelluft at the bedside. A common method to select the functional lung space (FLS)-i.e. the pixels associated with ventilation-is applying a threshold to the pixel tidal impedance variation (TIV). Due to the apparent phase shift, pixel TIV is lower in regions associated with pendelluft, resulting in removal of those pixels for further analysis.Approach. We developed a novel method for FLS selection using the established watershed segmentation method. Watershed regions are segmented based on the pixel amplitude map and local peaks in that map. Watershed regions whose local peaks fall inside the threshold-based TIV FLS are included. Pixels with an amplitude above the threshold that are inside the included watershed regions form the Watershed FLS.Main results. We evaluated the algorithm in 11 patients switching from controlled mechanical ventilation (CMV) to assisted mechanical ventilation (AMV). No significant differences were found between TIV FLS and Watershed FLS during CMV. Switching from CMV to AMV lead to a significant decrease of the TIV FLS (p= 0.043), but not the Watershed FLS. As a result, TIV FLS was significantly smaller than Watershed FLS during AMV (p⩽ 0.001). Pendelluft magnitude was higher using the Watershed FLS compared to the TIV FLS during AMV (p⩽ 0.001).Significance. The common TIV-based FLS can result in the unintended removal of pendelluft-associated pixels for further analysis. The Watershed FLS includes these pixels, potentially improving the quality of EIT-analysis in patients with spontaneous breathing efforts.

Objective. This paper describes a new method for measuring human bone density based on the measurement of bone electrical conductivity (BEC) using the well-known technique: electrical impedance tomography (EIT).Approach. The hypothesis is that BEC is directly related to bone mineral density. The proposed method consists of measuring the EIT, and then obtaining the conductivity values of the bones, and take this value to associate this one to a level of porosity. A model of skin, muscle, and bone was developed using computer simulation to vary the porosity in bones and obtain an inverse model in the form of an equation to measure three levels of porosity.Main results. The behavior of different porosity levels was simulated, and impedance tomography was applied; it is shown that electrical conductivity varies according to bone porosity. A relationship was subsequently obtained between the measurement of conductivity and bone density, creating a new non-invasive method for possible application to early detection of osteoporosis.Significance. This is a novel, non-invasive method for possible application on the early detection of osteoporosis at three different levels of bone porosity.

Objective.Prompt identification of haematomas is crucial for effective clinical treatment. Magnetic induction phase shift technology (MIPS), known for its portability, non-contact nature, and affordability, is limited by the weak signal induced by cerebral hemorrhage leading to poor sensitivity, which is urgent to be improved.Approach. Tracer of magnetic nanoparticles is introduced to produce robust induced magnetic field. A symmetrical gradiometer coil is used as the receiving coil to nullify the effect of primary magnetic field generated by the excitation coil, which is designed as a Helmholtz coil.Main results.In vitroexperiments showcase the remarkably improved sensitivity and stability of the detection system, with magnetic nanoparticles notably boosting the MIPS signal for hemorrhage. Moreover,in vivoexperiments employing a rabbit autologous blood cerebral hemorrhage model reveal that with a hemorrhage volume of 2 ml, the experimental group with employed magnetic nanoparticles increased the MIPS signal change by 23-fold compared to the control group without magnetic nanoparticles.Significance. The sensitivity of MIPS for hemorrhage detection is significantly improved compared to traditional method. The magnetic nanoparticle-enhanced MIPS detection technique holds promise as an optimal solution for real-time, non-invasive bedside monitoring for cerebral hemorrhage.

Objective.Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization, and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by predicting HF readmission.Methods.Seismocardiogram (SCG) signal is the low-frequency chest vibration produced by the mechanical activity of the heart. SCG signal was acquired from 101 patients with HF, including those readmitted to the hospital during the study period. SCG signals were segmented into heartbeats and clustered based on respiration phases. Features were extracted from each cluster. Several conventional machine learning (ML) models were developed using selected SCG and heart rate variability features. Furthermore, SCG signals were transformed into images using a time-frequency distribution method. Images were used to train a deep learning model. The models were able to predict the readmission status of HF patients.Results.ML algorithms achieved higher accuracy than the deep learning model in classifying the readmitted and non-readmitted HF patients. K-nearest neighbor achieved the highest classification accuracy (89.4% accuracy, 87.8% sensitivity, 90.1% specificity, 78.2% precision, and 82.7%F1-score). A detailed discussion of the extracted features was provided, correlating them with HF conditions.Conclusions. The study results suggest that SCG signals may be useful for readmission prediction of HF patients.

Objective.Sodium (Na) overconsumption has been associated with hypertension risk and progression. Human soft tissue and bone are recognized as quickly and slowly exchangeable compartments for sodium storage. How such a distribution regulates blood pressure remains unknown. This study performed in vivo Na measurements on human subjects who underwent dietary intervention, utilizing a compact neutron generator-based neutron activation analysis system. It aimed to evaluate the performance of this innovative system for body Na assessment.Approach.Participants were provided with low and high sodium diets. Baseline measurements were taken before each intervention feeding period, and follow-up measurements were conducted afterwards. The human hands were irradiated for 20 min, followed by 2 cycles of Na gamma ray collection. A biokinetic model was used to calculate sodium concentrations in soft tissue and bone, reflecting sodium accumulation in the two compartments.Main results.For soft tissue, Na levels after low Na diet decreased from baseline in half of the subjects, with reductions ranging from 8% to 55%. The other half of participants exhibited relatively stable Na content. Among participants consuming high Na diet, all participants had elevated Na in soft tissue compared to those on low Na diet. By contrast, Na in bone showed no significant changes from baseline and follow-up for either dietary intervention. Bone Na concentrations ranged from approximately 1000–2000 ppm.Significance.For the first time, Na in soft tissue and bone was measured in humans using neutron activation analysis in response to dietary interventions. This study demonstrates that in vivo neutron activation analysis can be used to measure Na concentration in both soft tissue and bone. It successfully detects Na alteration in soft tissue and explores the biokinetics of Na retention following dietary interventions. Measuring soft tissue and bone sodium content is a potentially useful approach to study diet and disease links affected by sodium.

Objective.Integration of cerebral blood macrocirculation and microcirculation form a crucial aspect of global cerebral blood flow. Our study aimed to investigate time delay between pulse oscillations of cerebral blood flow velocity (FV) and total hemoglobin concentration (tHb) acquired via transcranial Doppler ultrasonography and functional near-infrared spectroscopy, respectively. Additionally, we compared time-related characteristics with cerebral arterial time constant (τ).Approach.The study involved monitoring of FV, tHb, arterial blood pressure and end-tidal CO2(EtCO2) during 5 min of rest (normocapnia) and 5 min of controlled hypercapnia in 36 healthy subjects (age: 24 years,Q1-Q3: 21-27 years, 15 females). Onset time delay (onset TD) was defined as the time offset between onsets of FV and tHb pulses, and time to tHb max (TTM) as a time difference between the FV pulse onset and tHb pulse maximum.τwas calculated as a product of cerebrovascular compliance and resistance. Onset TD and TTM were compared between normocapnia and hypercapnia, and their associations withτwere assessed.Main results.Onset TD was consistently positive (0.075 s,Q1-Q3: 0.065-0.108 s). Onset TD andτwere significantly shorter in hypercapnia (0.075 s vs 0.071 s,p< 0.001; 0.107 s vs 0.099 s,p< 0.001). Changes in onset TD andτbetween normo- and hypercapnia were significantly correlated (RS= 0.452,p< 0.01). TTM was correlated withτin normocapnia (RS= 0.364,p= 0.03), hypercapnia (RS= 0.407,p= 0.01) and in terms of relative changes (RS= 0.421,p= 0.01).Significance.There is a time delay between cerebral macro- and microcirculation, which becomes shorter as the cerebral vasculature dilates in hypercapnia.

Objectives.This study aimed to develop and validate a load cell-based device for measuring isometric forearm rotation torque and to determine its test-retest reliability.Approach.The custom-built device was calibrated using known weights and validated against a high-precision torque transducer. For reliability assessment, 35 physically active participants (20 males, 15 females; age 30 ± 7 years) were tested for isometric forearm pronation and supination strength 5-7 d apart.Main results.The custom device demonstrated excellent validity (intraclass correlation coefficient (ICC), absolute agreement = 1.00;r2= 1.00,p< 0.001; mean difference = -1.26-1.44%,p< 0.001). Test-retest reliability was excellent for absolute pronation and supination torque (ICC = 0.88-0.97; coefficient of variation percentage (CV%) = 4.1-5.6; minimal detectable change (MDC) at 90% confidence level = 13.1-19.9%), good to excellent for supination:pronation ratios (ICC = 0.60-0.88; CV% = 7.0-8.6; MDC = 0.10-0.13), and fair to good for dominant:non-dominant ratios (ICC = 0.42-0.66; CV% = 6.1-7.6; MDC = 0.07-0.10). Sex significantly influenced absolute torque values, with males demonstrating consistently higher torque, although reliability metrics were similar for both sexes.Significance.The device is valid, and the test is reliable. It is suitable for clinical assessments, rehabilitation monitoring, and performance evaluation, facilitating an improved understanding of factors affecting elbow overloading and injuries. Limb ratio metrics should be interpreted with caution due to their lower reliability.

Physics-informed neural networks (PINNs) represent a transformative approach to data models by incorporating known physical laws into neural network training, thereby improving model generalizability, reduce data dependency, and enhance interpretability. Like many other fields in engineering and science, the analysis of physiological signals has been influenced by PINNs in recent years. This manuscript provides a comprehensive overview of PINNs from various perspectives in the physiological signal analysis domain. After exploring the literature and screening the search results, more than 40 key studies in the related domain are selected and categorized based on both practically and theoretically significant perspectives, including input data types, applications, physics-informed models, and neural network architectures. While the advantages of PINNs in tackling forward and inverse problems in physiological signal contexts are highlighted, challenges such as noisy inputs, computational complexity, loss function types, and overall model configuration are discussed, providing insights into future research directions and improvements. This work can serve as a guiding resource for researchers exploring PINNs in biomedical and physiological signal processing, paving the way for more precise, data-efficient, and clinically relevant solutions.

Objective. There is growing interest in the use of physiological signals beyond electrocardiography (ECG), particularly photoplethysmography-based noninvasive arterial blood pressure (nABP), to assess autonomic nervous system (ANS) activity with minimal recording durations. This study compared heart rate variability (HRV) and pulse rate variability (PRV) derived from ECG and nABP, respectively. We investigated how signal shortening and calculation window size affect time-domain, frequency-domain, and nonlinear ANS metrics.Approach. Photoplethysmography was used to measure nABP, whereas ECG was recorded with a 3-lead device in healthy individuals (18-31 years). The HRV and PRV were analyzed using time- and frequency-domain metrics, and nonlinear indices, including entropy metrics and Poincaré plots (SD1, SD2). Agreement between signal lengths of 3 min and 5 min was assessed in 86 nABP and 70 ECG participants using intraclass correlation coefficients (ICCs). To evaluate the effect of window size, 15 min recordings from 16 participants were segmented into windows of 3 min, 5 min, and 15 min. HRV-PRV agreement was evaluated using Bland-Altman analysis.Main results. The time-domain metrics demonstrated excellent reproducibility when the signal length (ICCs ⩾ 0.96) and window size (ICCs ⩾ 0.98) were shortened, but moderate agreement between HRV and PRV. Entropy metrics were most affected by signal shortening (e.g. HRV multiscale entropy ICC (95%CI]): 0.67 (0.47-0.80); PRV approximate entropy: 0.45 (0.15-0.64)). Shorter window sizes affected selected ANS metrics, including reduced SD2 (p= 0.003 for HRV,p< 0.001 for PRV) and increased frequency-domain values (p< 0.001 for HRV and PRV).Significance. Time-domain metrics are more robust to reductions in signal length and calculation window size but demonstrate lower interchangeability between HRV and PRV. Both signal length and window size influence selected ANS metrics and should be considered, particularly when employing entropy-based indices in wearable, remote, and short-duration physiological monitoring.

Objective.Functional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statistical learning approaches to find FNC features separating patients from control. While machine learning models can improve classification accuracy, they often lack interpretability, making it difficult to understand how they arrive at their decisions.Approach.Explainable machine learning helps address this issue by identifying which features contribute most to the model’s predictions. In this study, we introduce a novel framework leveraging SHapley Additive exPlanations (SHAPs) to identify crucial FNC features distinguishing between two distinct population classes.Main results.Initially, we validate our approach using synthetic data. Subsequently, applying our framework, we ascertain FNC biomarkers distinguishing between, controls and schizophrenia (SZ) patients with accuracy of 81.04% as well as middle aged adults and old aged adults with accuracy 71.38%, respectively, employing random forest, XGBoost, and CATBoost models.Significance.Our analysis underscores the pivotal role of the cognitive control network (CCN), subcortical network (SCN), and somatomotor network in discerning individuals with SZ from controls. In addition, our platform found CCN and SCN as the most important networks separating young adults from older.