Search Results (1,009)

Background: Delayed emergence after general anesthesia in pediatric patients is uncommon and requires prompt evaluation of pharmacological, metabolic, and neurological causes. Residual neuromuscular blockade remains an important reversible cause, particularly when short acting neuromuscular blocking agents dependent on plasma cholinesterase metabolism are used. Case report: We report the case of a previously healthy three-year-old boy who underwent elective adenoidectomy, tonsillotomy, and tympanostomy under general anesthesia with fentanyl, propofol, sevoflurane, and mivacurium. Following the procedure, the patient failed to regain spontaneous breathing, purposeful movements, or response to painful stimuli. In response, neostigmine was administered to reverse possible residual neuromuscular blockade; however, no immediate clinical improvement was observed, and mechanical ventilation was continued. Quantitative neuromuscular monitoring was unavailable. Therefore, residual blockade could not be objectively assessed, and the depth of blockade at the time of neostigmine administration remained unknown. The patient was transferred to a tertiary pediatric center for further evaluation. Several hours later, spontaneous breathing and motor activity recovered, and the patient was successfully extubated. Retrospective family history revealed prolonged recovery after anesthesia in the patient’s grandfather. Subsequent laboratory testing demonstrated markedly reduced plasma butyrylcholinesterase activity (2493 U/L; reference range 7000–19,000 U/L), raising a strong suspicion of inherited butyrylcholinesterase deficiency. Conclusions: This case highlights the importance of considering residual neuromuscular blockade in delayed emergence after pediatric anesthesia, maintaining airway protection, and obtaining relevant family history to guide future management. Full article

Background/Objectives: Monitoring and evaluating respiratory rate (RR) plays a critical role during emergency transport. Conventional RR measurement methods have primarily relied on electrocardiogram-based approaches, which are susceptible to large errors caused by patient body movement and impose a burden on patients due to their contact-based nature. This study aimed to develop a non-contact RR measurement method using RGB and thermal cameras that can be applied inside a moving ambulance. Methods: One of the key challenges in non-contact RR measurement inside an ambulance is accommodating changes in patient posture. We addressed this by patient posture-adaptive automatic image registration between RGB and thermal cameras and developing a nostril detection method robust to posture changes. Results: The image registration error of the proposed method was evaluated at various distances from the camera, and the error was confirmed to remain within 1 pixel. RR measurement experiments simulating various respiratory conditions were then conducted inside a moving ambulance. The average of mean absolute error (MAE) was 0.95, 0.59, and 2.80 breaths per minute (breaths/min) for bradypnea, normal, and tachypnea, respectively, in the supine position, and 1.08, 1.90, and 4.38 breaths/min, respectively, in the semi-sitting position. Evaluation of the proposed nostril detection method further confirmed improved RR estimation accuracy compared with the conventional method. Conclusions: This study achieved patient posture-adaptive automatic image registration and robust nostril detection and successfully conducted experiments under realistic and challenging conditions, representing a substantial step toward the real-world implementation of a non-contact RR measurement system in ambulances. Full article

A 77-GHz radar can detect small chest movements induced by cardiac activity and respiration; however, typical systems transmit the acquired signals to an external computer for processing. This limits the development of autonomous and energy-efficient monitoring devices. This study presents a system in which radar signal processing, signal quality assessment, and heart rate (HR) and respiratory rate (RR) estimation are performed directly on the AWR1843 radar device. A Tiny 1D CNN used a 10-s observation window for HR and a 30-s window for RR, producing updated estimates every 1 s. The system was validated with 30 participants aged 18–72 years. The mean absolute error was 2.09 beats/min for HR and 1.05 breaths/min for RR. Compared with the implemented short-window FFT-based spectral estimator, the errors were reduced by 45.3% and 37.5%, respectively. INT8 quantization reduced the model weight size from 19.2 to 4.8 kB, the inference time from 4.6 to 1.9 ms, and the incremental energy per network execution from 2.7 to 0.9 mJ. However, this inference-level improvement did not produce a proportional reduction in whole-node power, which remained approximately 2.38 W because the continuously active radar front end and frame-level signal processing dominated the total power consumption. The system transmits only the estimated parameters and their associated reliability indicators. Consequently, the radar can operate as a standalone measurement node rather than continuously streaming raw data to an external computer. Full article

Monopterus albus (Asian swamp eel) is a commercially important, air-breathing freshwater fish harvested throughout East and Southeast Asia. As a protogynous species, it begins life as a female, passes through a transitional phase, and ultimately becomes male, making sexual-state classification difficult and potentially affecting management in capture fisheries and aquaculture. We examined sex–size relationships in market-purchased, reportedly wild-caught M. albus from northern and northeastern Thailand using gonadal histology to determine sexual state, together with body length (BL), body mass (BM), and a separate otolith-aged sample. Among 199 histologically examined individuals, no females were observed above approximately 70 cm BL or 270 g BM. Across much of the intermediate size range, female, transitional, and male individuals overlapped in size. Transitional individuals reached 78 cm and 424 g, showing that the upper values represent female-exclusion boundaries rather than thresholds identifying males. No male was smaller than 55 cm BL or 200 g BM, although sampling below these values was sparse. Otolith-derived age estimates from a separate sample of 70 eels provided local size-at-age context but could not establish age at sex transition because sexual state was not determined in those individuals. Comparisons with published studies showed substantial regional variation in size–sex relationships, indicating that size-based criteria developed in one region may not transfer reliably elsewhere. The boundaries identified here provide practical guidance for fisheries monitoring and broodstock selection in northern and northeastern Thailand while emphasizing the need for locally validated criteria elsewhere. Full article

Respiration is a critical vital sign for assessing the health status of infants during the first year of life. Conventional video-based respiratory monitoring methods often rely on manually defined regions of interest (ROIs) targeting specific anatomical regions, such as the chest or abdomen, or employ pretrained object detection models to localize body regions before extracting respiratory signals. However, in unconstrained sleep environments, infants may adopt diverse sleep postures, making it difficult to assume that respiration-induced movements will consistently appear in the same anatomical region or image location. To address this limitation, we propose an anchor-based respiratory-region localization framework that identifies spatial regions exhibiting prominent respiratory periodicity across the entire video frame without relying on predefined anatomical ROIs or separate body-region localization models. For each grid cell, time-series signals are constructed from inter-frame color/intensity variations and optical-flow-derived motion changes. The magnitude spectra of these signals are then multiplied element-wise to generate a respiratory periodicity map representing the spatial distribution of respiration-related periodicity across the frame. The grid cell with the highest Periodicity Score is selected as the Anchor Grid Cell and used as the primary respiratory region for respiratory-rate estimation. The proposed method was evaluated using 6-fold subject-wise cross-validation on 374 video clips from 15 infants. The single-Anchor Grid Cell approach achieved a mean absolute error (MAE) of 1.9953 breaths per minute (BPM), a root mean square error (RMSE) of 3.9681 BPM, and a Pearson correlation coefficient (PCC) of 0.7287. Moreover, MAEs below 2.5 BPM were obtained for the supine, side-lying, and prone postures, indicating consistent estimation performance across the evaluated sleep postures. These findings suggest that respiration-related information in infants is not necessarily confined to predefined anatomical regions and may instead be expressed at different spatial locations depending on sleep posture. Full article

Infectious Laryngotracheitis (ILT), caused by an alphaherpesvirus (ILTV), is an acute respiratory disease that inflicts significant economic and welfare impacts on poultry through increased mortality, reduced weight gain, and declining egg production. Current surveillance depends on clinical observation followed by labor-intensive laboratory diagnostics, delaying early intervention. Clinical and behavioral signs, including eye rubbing, head bobbing, mouth breathing, huddling, and reduced feeding and drinking, typically precede severe outcomes, making automated early detection critical for disease control. This pilot study deployed vision-based object detection to identify ILT-associated signs, comparing YOLO11-obb and YOLO26-obb models. We divided 14 SPF Leghorn hens into ILTV-infected (n = 7) and uninfected control (n = 7) groups. We recorded high-definition videos over 8 h daily for seven days post-infection, yielding 1214 annotated images split into training (70%), validation (20%), and testing (10%) sets. Models were evaluated using precision, recall, mAP@0.50, and mAP@0.50–0.95. YOLO26x-obb achieved the highest overall performance, while feeding and drinking behaviors were detected with a precision of 0.93 to 0.98 across both model variants. Detection of clinical signs ranged from 0.36 to 1.0, reflecting visual overlap between behaviors. These findings establish a scalable, non-invasive framework for real-time ILT monitoring, enabling earlier management intervention before flock-level disease spread. Full article

Purpose: This pilot study evaluated the real-world accuracy of a novel wearable morphic sensor system for unobtrusive, continuous heart rate (HR) and respiratory rate (RR) monitoring in critically ill patients within an intensive care unit (ICU). Methods: A hybrid piezoresistive/piezoelectric disk-shaped sensor was placed on patients’ backs to capture cardiorespiratory activity via forcecardiographic principles. Data were acquired from 15 ICU patients using a custom wireless recorder. The piezoelectric channel was processed using discrete wavelet transform and an adaptive peak detection algorithm. Derived HR and RR were compared against reference measurements from clinical patient monitors and a ventilator. Results: Bland–Altman analysis showed a mean bias of −0.39 ± 1.99 beats per minute for HR and 0.06 ± 0.90 breaths per minute for RR. Linear regression revealed strong correlation with clinical monitors (HR R-squared = 0.982; RR R-squared = 0.966). The device’s RR performance matched the agreement between two independent clinical reference devices. Conclusion: The proposed system met the pre-specified bias and standard deviation criteria for HR and RR monitoring in bed-bound ICU patients. Its skin-adhesive-free design enables unobtrusive, continuous vital sign surveillance without direct skin contact. These findings support ICU monitoring. Step-down ward and ambulatory applications remain to be validated in future studies. Full article

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. Identifying patients with early COPD remains challenging; however, even at this stage, substantial biological disease progression and irreversible lung injury may already be present. Considerable effort has therefore been directed toward identifying parameters that detect early pathophysiological changes before airflow limitation develops. The importance of this stage has recently been recognized and is described using terms such as “pre-COPD” and “early COPD”. Exhaled breath temperature (EBT) is a non-invasive method for detecting and monitoring respiratory inflammation. Most EBT studies have been conducted in asthma and have demonstrated the utility of this approach for assessing changes in airway inflammation. In patients with COPD, the number of airways and their vasculature is reduced, and EBT decreases proportionally with the degree of structural destruction; however, EBT may still increase during COPD exacerbations. In recent studies, the change in EBT after cigarette smoking in individuals without a COPD diagnosis significantly predicted disease progression after 2 years. Early interventions based on these findings should be tested for efficacy in preventing the development of overt COPD. Full article

Sleep-related breathing disorders, particularly the obstructive sleep apnea syndrome, lead to an urgent demand for continuous, comfortable, and accurate monitoring technologies. However, conventional polysomnography is limited by complex procedures, poor wearing comfort, and restricted suitability for long-term monitoring. Owing to their skin-like mechanical performance, high biocompatibility, high ionic conductivity, and excellent tissue compatibility, hydrogels have emerged as promising materials for flexible bioelectronic devices. Recent multimodal hydrogel-based sensors enable simultaneous acquisition of mechanical, thermohygrometric, and electrophysiological signals, providing new opportunities for comprehensive sleep respiratory monitoring. This review systematically discusses the mechanisms for acquiring and analyzing multimodal physiological signals, including respiratory motion, respiratory airflow, auxiliary physiological signals, and AI-assisted sleep respiratory state recognition. Furthermore, structural design strategies, including single-layer, multilayer, biomimetic, and integrated architectures, are reviewed to clarify how structural configurations facilitate multimodal signal acquisition, signal interference suppression, and sensing performance optimization. This review provides valuable references for the future development of high-performance wearable multimodal hydrogel-based sensors toward sleep respiratory monitoring. Full article

In view of the demand for wearable physiological monitoring, porous PDMS/MWCNTs composite films were fabricated via the sacrificial template method in this work. Output performance tests were carried out on films with different sacrificial particle sizes, film thicknesses, and multi-walled carbon nanotube (MWCNT) mass fractions. Scanning electron microscopy (SEM) was employed for structural morphology characterization. The optimal particle size, thickness, and doping ratio were confirmed to be 63 μm, 3 mm, and 9 wt%, respectively, and flexible sensor arrays based on triboelectric nanogenerators were further fabricated. In this paper, a complete signal conditioning system consisting of a flexible sensor array and a signal acquisition circuit (including a power supply unit, reset circuit, signal conditioning module, and core control chip) was designed to realize data transmission and upper computer display. Pulse signals from four volunteers were collected in five groups, respectively, for comparative analysis. Experimental results reveal that multiple sets of pulse data from the same subject show high consistency. Slight deviations within 0.38 V are merely caused by breathing and other subtle interference factors. These results verify that the prepared sensor possesses favorable sensitivity and repeatability. Full article

Background and Objective: Obstructive sleep apnea (OSA) is a fatal widespread sleep-related breathing disorder and a major risk factor for cardiovascular and cerebrovascular diseases, significantly impacting older adults’ health worldwide. Due to the risk of such complications, timely and accurate identification of OSA is crucial. Polysomnography is considered the most accurate technique for detecting OSA; however, it is limited by its complexity and multi-channel requirements. A promising alternative is electrocardiogram (ECG)-based diagnosis, which continuously monitors heart rhythm and captures subtle cardiac changes associated with OSA. Nevertheless, existing ECG-based approaches still face challenges related to complex feature engineering, limited capture of complementary temporal–spectral information and global dependencies, along with inadequate feature recalibration and fusion, which can restrict OSA detection. Thus, further improvements are still required to achieve clinically reliable performance. Methods: To address these challenges, this study proposes SApneaNet, a novel advanced deep learning method for detecting OSA events using ECG signals. The proposed approach employs the continuous wavelet transform (CWT) to convert ECG signals into RGB log-scalograms, enabling the simultaneous analysis of temporal and frequency-domain features. The generated RGB log-scalograms are then fed into a deep CNN encoder with adaptive squeeze-and-excitation (ASE), followed by a transformer and an adaptive gated feature fusion (AGFF) architecture. In this framework, to improve OSA detection performance, the CNN extracts rich local features, the ASE module performs channel-wise recalibration to enhance feature representations, the transformer performs data-parallel processing and captures global contextual dependencies, and the AGFF mechanism adaptively emphasizes informative features while suppressing less relevant ones. Results: The experimental results on the Apnea-ECG dataset showed that the model achieved a sensitivity of 94.7%, specificity of 95.2%, F1-score of 93.5%, accuracy of 95.1%, Cohen’s kappa of 89.4%, and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 for per-segment classification. Furthermore, for per-recording classification, the model achieved an accuracy of 100.0%, a mean absolute error (MAE) of 2.025, and a Pearson correlation coefficient (PCC) of 0.992. Overall, the experimental results demonstrated that the proposed model achieved excellent and competitive performance compared with other advanced state-of-the-art methods for OSA classification. Conclusions: The proposed model demonstrates strong efficacy in OSA detection, providing a novel and robust alternative to conventional diagnostic methods. The model’s reliable and consistent diagnostic performance highlights its potential for integration into practical OSA diagnostic systems, including home-based health monitoring devices and clinical decision-support tools. Full article

Background: Asthma during pregnancy is associated with adverse maternal and neonatal outcomes. Nursing education, self-management support, and mobile health (mHealth) technologies may support asthma management during pregnancy. Aim: This study evaluated the effect of a structured nursing intervention supported by an mHealth application on asthma control, treatment adherence, and self-monitoring, and maternal and neonatal outcomes among pregnant women with asthma. Design and Setting: A quasi-experimental pretest–posttest design with a control group was conducted at two Maternal and Child Health Centers in Shebin El-Kom, Menoufia Governorate, Egypt. Participants: A convenience sample of 140 pregnant women with physician-diagnosed asthma was recruited and allocated non-randomly according to the center attended to a study group (n = 70) and a control group (n = 70). The sample size was calculated a priori based on a previous study using a 5% significance level and 80% statistical power, with a 1:1 allocation ratio, resulting in a required sample of 70 participants per group. All recruited participants completed the study, with no withdrawals or loss to follow-up. Intervention: The study group received routine antenatal care plus an eight-week structured nursing intervention consisting of four individualized weekly educational sessions (60–90 min each) addressing asthma management, medication adherence, self-monitoring, trigger avoidance, warning signs, inhaler technique, lifestyle modification, and rhythmic breathing exercises. Participants were trained to use the Airlyn mHealth application for guided breathing exercises, practiced 2–3 times daily, and received telephone/WhatsApp follow-up during weeks 6–8. The control group received routine antenatal care according to usual MCH center procedures. Measures: Asthma control was assessed using the Asthma Control Test (ACT). Treatment adherence and self-monitoring were assessed using the Asthma Adherence and Self-Monitoring Questionnaire. Maternal outcomes included hypertensive disorders, gestational diabetes, infections, preterm labor/PPROM, hemorrhage, and mode of delivery. Neonatal outcomes included birth weight, Apgar scores, respiratory distress and respiratory support, NICU admission and length of stay, and condition at discharge. The researcher-developed instruments underwent expert assessment of content validity and assessment of internal consistency before use. Results: Baseline ACT scores were comparable between the study and control groups (14.5 ± 4.2 vs. 14.1 ± 4.3; mean difference = 0.4; p = 0.580). Following the intervention, ACT scores were higher in the study group than in the control group (19.7 ± 3.6 vs. 15.2 ± 4.2; mean difference = 4.5; 95% CI: 3.189–5.811; p < 0.001; Cohen’s d = 1.147). Well-controlled asthma was observed in 75.7% of the study group compared with 17.1% of the control group (p < 0.001). Treatment adherence and self-monitoring scores were also better in the study group (5.6 ± 2.7 vs. 9.1 ± 3.1; mean difference = −3.5; 95% CI: −4.476 to −2.524; p < 0.001; Cohen’s d = 1.199). The overall maternal outcome score was more favorable in the study group (3.5 ± 1.7 vs. 6.1 ± 2.6; mean difference = −2.6; 95% CI: −3.340 to −1.860; p < 0.001; Cohen’s d = 1.174), as was the overall neonatal outcome score (7.7 ± 2.2 vs. 5.5 ± 2.1; mean difference = 2.2; 95% CI: 1.480–2.920; p < 0.001; Cohen’s d = 1.022). Significant between-group differences were also observed in birth weight, Apgar scores, respiratory distress, and NICU admission. Conclusions: Participants who received the structured nursing intervention supported by the Airlyn mHealth application were associated with improved asthma control, treatment adherence, and self-monitoring, as well as more favorable maternal and neonatal outcomes, compared with those receiving routine antenatal care. However, the quasi-experimental design, convenience sampling, and non-randomized allocation limit causal interpretation and the generalizability of the findings. Therefore, the findings should be interpreted cautiously. Recommendations: Larger randomized controlled trials with standardized protocols, adequate sample sizes to evaluate maternal and neonatal outcomes, and longer follow-up periods are recommended to confirm these findings and further assess their clinical relevance. Full article

Background: Asthma is a chronic respiratory disease shaped by environmental, meteorological, and behavioral factors. Few approaches combine population-level surveillance with individual-level monitoring within a single analytical framework. Methods: This work developed a dual machine learning framework. Study 1 modeled the daily count of respiratory admissions (chapter X of the ICD-10) in São Paulo, Brazil, from 2017 to 2022 as a nowcasting task, combining ElasticNet, residual CatBoost, direct CatBoost, and adaptive blending, validated by walk-forward over 30 bimonthly windows. Study 2 applied an XGBoost and Random Forest pipeline to 913 diaphragmatic-breathing sessions from 17 patients aged 9 to 16 years in the Respire Bem system, with Asthma Control Test and salivary cortisol represented using evidence-based synthetic simulation. Results: Study 1 achieved a mean MAE of 18.22, RMSE of 23.99, and R² of 0.675, exceeding the seasonal baseline by 41.5%, with a significant advantage over all three baselines (Wilcoxon and Diebold–Mariano, p ≤ 0.038). Ablation showed each single-component configuration to be significantly worse than the full hybrid, but removing the environmental block cost only 0.27 admissions per day, an effect indistinguishable from zero. SHAP rankings were stable across windows (Kendall W = 0.640), led by NO₂, PM2.5, and temperature. In Study 2 the pipeline ran end-to-end on real behavioral data, but because the outcomes were simulated, no predictive-accuracy metric is reported. Conclusions: Study 1 delivers a validated population-level nowcasting model whose accuracy rests mainly on the temporal structure of the series. Study 2 contributes a real behavioral dataset and a reproducible pipeline; the clinical validity of the digital biomarkers remains open and requires prospective work with directly measured outcomes. Full article

Railway pre-duty assessments require rapid, objective measurements that do not disrupt operational workflows. This study evaluated the system-level integration and participant-level measurement performance of a gate-based Through-Pass platform for railway pre-duty screening. The platform combines radio-frequency identification, infrared temperature sensing, camera-based remote photoplethysmography, breath-alcohol sensing, signal-quality control, edge processing, and rule-based screening. Participant-level method comparison was performed in 300 adults using paired Through-Pass and reference measurements of body temperature, heart rate, systolic and diastolic blood pressure, and blood alcohol concentration. Agreement was assessed using Bland–Altman analysis, concordance statistics, error metrics, Deming regression, and multivariable models of measurement error. Mean biases were close to zero for all five outcomes, and Lin’s concordance correlation coefficients ranged from 0.938 to 0.995. After multiple-testing correction, measurement error showed no consistent associations with age, sex, body mass index, grouped Fitzpatrick skin type, or eyeglass use, whereas the reference-value magnitude was associated with error for diastolic blood pressure and blood alcohol concentration. These findings support the feasibility of integrating established sensing components into a unified pre-duty measurement platform under controlled indoor conditions; field and screening-classification validation remain necessary before operational implementation. Full article

Mental stress has become a significant health issue because it can disrupt autonomic balance and alter breathing patterns. Breathing techniques, especially when monitored by the HeartMath coherence device, are popular for promoting balanced heart–mind interaction. In this project, we developed an open-source Arduino-based photoplethysmography (PPG) system capable of recording heart pulse signals and displaying them on a specialized Python interface. The heart pulse amplitudes are displayed to allow observation of breathing modulation as an envelope of the PPG amplitude train. Simultaneously, the increase in heart rate during inhalation and the decrease during exhalation are displayed. The Python interface enables real-time monitoring of how 0.1 Hz rhythmic breathing exercises synchronize the heart-rate waveform with the PPG amplitude envelope. The proposed platform can serve as a basis for future studies on breathing techniques to foster a harmonious state, enhancing productivity, creativity, and inner peace. Full article