Search Results (146)

Background: Obstructive sleep apnea syndrome (OSAS) is a prevalent sleep-related breathing disorder associated with significant systemic complications and reduced quality of life. Mandibular advancement devices (MADs) represent an established alternative therapy for patients who cannot tolerate continuous positive airway pressure (CPAP). However, concerns remain regarding their potential effects on temporomandibular disorders (TMD). Materials and Methods: This retrospective exploratory study analyzed clinical records of 26 patients (mean age 55.4 ± 5.8 years) with polysomnography-confirmed OSAS and baseline TMD-related symptoms treated with a custom-made monobloc MAD. Clinical parameters were evaluated at baseline (T0) and after approximately 6 months of therapy (T1). Outcomes included apnea–hypopnea index (AHI), Epworth Sleepiness Scale (ESS), Fonseca Anamnestic Index, and health-related quality of life assessed using the SF-36 questionnaire. Repeated measures ANOVA and linear regression analyses were performed. Results: After six months of MAD therapy, a significant reduction in AHI was observed (30 ± 13.76 vs. 10.87 ± 3.9; p < 0.00001). Daytime sleepiness significantly decreased (ESS: 9.31 ± 3.53 vs. 3.38 ± 1.77; p < 0.00001). TMD symptom severity also decreased significantly according to the Fonseca Index (33.85 ± 17.74 vs. 10.00 ± 8.94; p < 0.00001). Quality of life scores improved significantly (SF-36: 41.15 ± 9.52 vs. 65.38 ± 5.82; p < 0.00001). Linear regression analysis showed no significant association between changes in AHI and changes in TMD symptoms, ESS scores, or quality of life. Conclusions: Within the limitations of this retrospective study, MAD therapy was not associated with symptom aggravation of temporomandibular disorders in patients with pre-existing TMD symptoms. Significant improvements in respiratory parameters, daytime sleepiness, and quality of life were observed after six months of therapy. Full article

Precise respiration assessment is crucial for heart rate variability (HRV) interpretation as respiratory components—particularly respiratory sinus arrhythmia (RSA)—provide essential information on vagally mediated regulation. Conventional single-lead electrocardiogram-derived respiration (EDR) methods measure the amplitude modulation of the QRS-waveform caused by respiratory chest movements. This causes a displacement of the electrical heart axis in relation to the ECG lead axis, typically within the 2D frontal plane of the Einthoven electrode montage. Another approach is based on heartbeat acceleration and deceleration during respective inspiration and expiration causing RR interval modulation. However, interval-based methods depend on the complexity of sympathovagal factors that affect RSA. The present feasibility study accounts for the 3D rotational movement of the electrical heart axis during the respiratory cycle and avoids non-respiratory neuromodulatory confounds. The beat-to-beat cardiac rotation was extracted from Frank-XYZ coordinates reconstructed via a four-electrode EASI device. In a pilot study with data from 19 healthy adults performing acoustically paced breathing (6–18 bpm), three surrogates (${\mathrm{RR-Interval}}_{\mathrm{EDR}}$, ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$) were compared using a unified Python 3.11.13 pipeline (3D VCG R-peak detection, multivariate Mahalanobis artifact correction, wavelet-based analysis) against a synthetic reference derived from the instructed breathing schedule. The results demonstrated a consistently lower estimation error and higher reference-based signal-to-noise ratio (refSNR), measuring spectral alignment with the paced-breathing trajectory for ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and achieving a mean refSNR of 6.01 dB (vs. 4.62 dB for ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ and 3.20 dB for ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$) and a mean absolute estimation error of 0.016 Hz (vs. 0.050 Hz and 0.032 Hz, respectively). Notably, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ and ${\mathrm{R-Amplitude}}_{\mathrm{EDR}}$ performance slightly improved at higher heart rates, consistent with the interpretation that higher cardiac sampling density benefits spectral resolution for chest movement-based methods, whereas ${\mathrm{RR-Interval}}_{\mathrm{EDR}}$ showed no significant heart rate dependence. Furthermore, ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ was compared with the EDR results obtained by applying the Kubios-HRV Premium software (version 3.5.0). Kubios-EDR yielded higher precision at elevated breathing frequencies, whereas ${\mathrm{Heartmovement}}_{\mathrm{EDR}}$ outperformed Kubios-EDR at breathing rates below 10 bpm—a range that is particularly relevant for vagally activating slow breathing protocols or treatments. Future work should validate this method using a direct respiration measurement under spontaneous natural breathing conditions. Full article

Falls among older adults are a major public health concern, yet collecting large-scale real fall data for radar-based detection is ethically and practically difficult. This study presents a controlled simulation-to-real feasibility study for trip-fall detection using continuous-wave (CW) Doppler radar. The method couples a physics-informed kinematic trip-fall model with a CW radar observation model to synthesize I/Q signals and Doppler spectrograms, while domain randomization varies body size, fall direction, initial velocity, sensor placement, aspect angle, amplitude, and noise. Synthetic walking and respiration data were also generated for controlled three-class classification among trip fall, walking, and seated quiet breathing. In Experiment I, the simulated spectrograms reproduced the dominant time–frequency characteristics of measured enacted trip-fall signals acquired with a 24 GHz CW radar; quantitative similarity analysis yielded a mean SSIM of 0.782 and a Doppler-ridge MAE of 24.6 Hz across five fall directions. In Experiment II, a ResNet-18 classifier trained only on simulated spectrograms achieved a macro-F1 score of 0.912 [95% CI: 0.883–0.936] on measured data from ten participants, three start locations, and eight directions. Under the present controlled evaluation, this exceeded the available real-data-trained baseline of 0.748 [95% CI: 0.691–0.805] (paired subject-level permutation test, $p=0.006$). These findings suggest that physics-informed simulation with domain randomization can reduce dependence on real trip-fall samples under limited-data conditions. The results do not establish robustness to other fall morphologies, fall-like activities of daily living, different environments, different radar devices, or embedded deployment. Full article

Disinfection is essential to ensure safe drinking water and hygienic conditions in environmental, industrial, and clinical settings. However, conventional methods for monitoring free residual chlorine are often laboratory-based and not suited for decentralized analysis. Here, we report a novel paper-based colorimetric biosensing platform that translates the ISO 7393-2 standard, a method based on the reaction of chlorine with N,N-diethyl-p-phenylenediamine (DPD), into a portable and user-friendly format. The proposed device integrates the DPD chemistry within a paper architecture, enabling reagent-free operation at the point of need. The sensor provides a rapid visual readout that is detectable by the naked eye, while quantitative analysis is achieved within 3 min through smartphone-based image acquisition. This work constitutes the first implementation of the ISO standard in a portable paper-based format suitable for both environmental and clinical matrices. The sensor provided a detection limit of 12 μM for sodium hypochlorite and was successfully validated in real samples, including bottled water and exhaled breath condensate, with satisfactory recoveries. Furthermore, the stability of the paper-based sensor was assessed under storage conditions of 4 °C and room temperature (23 °C), demonstrating excellent performance over 30 days in both cases, indicating that refrigeration is not required for maintaining sensor performance. Full article

Background: The aim of this study was to assess in vitro nebulized drug delivery during invasive and non-invasive ventilation, comparing jet nebulizers (JN) and vibrating mesh nebulizers (VMN) across various pediatric ventilation models. Methods: Drug delivery performance was compared between a continuous output JN (Aquineb) and VMN (Aerogen Solo A-VMN). The non-invasive model simulated a spontaneously breathing 9-month-old child using an anatomically correct upper airway model and breathing simulator. The invasive model used a mechanical ventilator with heated humidifier in a pediatric breathing circuit with an endotracheal tube. Nebulizers were driven with supplemental oxygen at manufacturer-recommended rates and positioned at approved locations. Absolute inhaled dose, delivery rate and residual volume were assessed using face mask, mechanical ventilation, high-flow nasal therapy and blow-by delivery methods. Dose was quantified using spectrophotometric analysis. Results: During spontaneous breathing, A-VMN delivered almost double the dose of the evaluated JN (p < 0.001), with a significantly faster delivery rate (p < 0.001) and lower residual volume (p < 0.0001). During mechanical ventilation, A-VMN demonstrated a greater than 3-fold increase in delivered dose (p < 0.0001) and faster delivery (p < 0.0001), with reduced residual volume (p < 0.001). During high-flow nasal therapy, delivery via nasal cannula was affected by gas flow rate for both devices, with A-VMN consistently delivering greater doses. A-VMN delivered significantly greater salbutamol doses during blow-by delivery. Conclusions: VMN demonstrated significantly superior dose delivery, faster delivery rates and reduced residual volumes compared to the evaluated JN across all tested pediatric respiratory support modalities. These in vitro findings provide important performance data for evidence-based device selection and warrant clinical investigation to determine potential therapeutic benefits in pediatric populations requiring aerosol therapy during respiratory support. Full article

Background: Peripheral venous cannulation is one of the most common clinical procedures, yet it often causes significant pain, anxiety, and discomfort for patients. While pharmacological methods exist, non-pharmacological interventions offer a low-cost, low-risk alternative that eliminates waiting times for anesthetic onset. The aim of this review is to synthesize the various nonpharmacological interventions for procedural pain reduction during PIVC in adults, covering interventions ranging from psychological distraction to advanced procedural support technologies. Methods: A systematic review was conducted following PRISMA 2020 guidelines and the Joanna Briggs Institute (JBI) framework. Databases including PubMed, CINAHL, Web of Science, and Scopus were searched for studies published between 2015 and 2025. Inclusion criteria focused on randomized controlled trials (RCTs) and quasi-experimental studies involving adult patients undergoing PIVC. Results: Thirty studies (29 randomized controlled trials and one experimental study) were included in the final analysis. The interventions were categorized into three primary groups: distraction techniques, physical methods, and behavioral techniques. The application of virtual reality (VR), optical illusion cards, and music therapy significantly reduced pain scores and enhanced patient satisfaction. Similarly, physical methods, such as thermomechanical stimulation (e.g., the Buzzy^® device), local heat application, and vibration, were found to be effective in lowering pain intensity compared to standard care. Behavioral techniques, including the “cough trick,” diaphragmatic breathing, and the Valsalva maneuver, consistently demonstrated efficacy in reducing both procedural pain and anxiety. Notably, while most interventions successfully reduced pain, certain methods—such as near-infrared (NIR) vein visualization—improved procedural success rates without significantly altering the subjective perception of pain. Conclusions: Findings from this review suggest that non-pharmacological interventions may serve as effective, safe, and feasible adjuncts for pain management during peripheral venous cannulation. Techniques such as the cough trick and vibration-based devices are particularly recommended due to their ease of integration into routine nursing practice, potentially improving patient comfort and clinical outcomes. Full article

Swallowing is a critical marker of neurological and emotional health. The ability to monitor it continuously and non-invasively, especially through smart ear-worn devices, holds significant promise for clinical applications. Despite this potential, no public audio datasets currently support reliable swallowing sound detection. Existing datasets focus primarily on speech and breathing, offering limited coverage and lacking detailed annotations for swallowing events. To address this gap, we introduce an in-ear audio dataset specifically designed to capture a wide range of verbal and non-verbal sounds. It includes comprehensive labeling focused on swallowing. The dataset was collected from 34 healthy adults (14 females and 20 males) between the ages of 20 and 29. Each participant performed a series of predefined tasks involving both non-verbal and verbal events. Non-verbal tasks included swallowing, clicking, forceful blinking, touching the scalp, and physical movements such as squatting or walking in place. Verbal tasks consisted of speaking (e.g., describing an image). Recordings were conducted in both quiet and noisy environments to better reflect real-world conditions. Data were captured using a combination of in-/outer-ear microphones, a chest belt to record electrocardiogram (ECG), respiration and acceleration signals, and an ultrasound probe to track tongue movement, which served as a reference for swallowing annotation. All signals were precisely synchronized. To ensure high data quality, the recordings were reviewed using both algorithmic analysis and manual inspection. Swallowing events were identified based on ultrasound signals and validated by an expert to guarantee accurate labeling. As a proof of concept that in-ear audio supports swallow classification, we fine-tune a fully connected neural network on YAMNet embeddings plus zero-crossing rate (ZCR) features. Across the completed folds, the model reaches an F1 score of 0.875 ± 0.013. Full article

Respiration rate (RR) is a key physiological indicator of health, stress, and thermoregulatory load in dairy cattle, yet continuous RR monitoring under commercial farm conditions remains challenging. In this Technical Note, we present a non-invasive clip-on nose ring device for continuous respiration monitoring based on acoustic recording directly at the nostril. The device integrates a MEMS microphone, embedded electronics, battery, and removable storage in a sealed, mechanically robust housing suitable for real-world barn environments. The system was deployed on five dairy cows under commercial farm conditions, enabling repeated multi-day recordings over several weeks. The respiration rate was extracted offline from raw audio using a deterministic signal-processing pipeline based on multiscale periodicity detection. Algorithm-derived RR estimates were evaluated against manually annotated breath events. Using 10-min rolling median values, the algorithm achieved a mean absolute error (MAE) of 1.47 breaths per minute (bpm), a root mean square error (RMSE) of 1.92 bpm, and a high correlation with reference values (r = 0.98, R² = 0.96). In addition to short-term accuracy, the system enabled stable multi-day monitoring. Group-level analysis across all five animals revealed a clear diurnal respiration pattern over multiple consecutive days, with lower RR during nighttime and higher RR during daytime summer conditions, without signs of a baseline drift. These results demonstrate the feasibility of continuous, long-term respiration monitoring in dairy cattle using an audio-based clip-on nose ring device and provide a practical foundation for longitudinal (multi-day, within-animal) RR assessment under commercial farm conditions, with potential for future extensions towards advanced respiratory health monitoring. While the system demonstrated stable performance under summer farm conditions, validation under extreme heat-stress environments and larger animal cohorts is required for comprehensive population-level assessment. Full article

The interpretation and diagnosis of central sleep apnea in pediatrics by nocturnal polysomnography is challenging due to its technical complexity, which involves the simultaneous recording of multiple physiological parameters related to sleep and wakefulness. Furthermore, the unfamiliar environment of a sleep laboratory can hinder sleep evaluation, and diagnostic backlogs are common due to restricted capacity at specialist tertiary centers. The ability to undertake home sleep studies in a familiar environment using simple, robust, and low-cost technology is attractive. The potential to repurpose the PneumoWave biosensor, a UKCA Class 1 device, registered as an accelerometer-based monitoring device that is intended to capture and store chest motion data continuously over a period of time for retrospective analysis, was explored in an in vitro model of central sleep apnea. The PneumoWave system contains a biosensor (PW010), which was able to record simulated apnea episodes of 5 to 20 s across physiologically relevant pediatric breathing rates using an in vitro manikin model and manual annotation. The findings confirm that the PneumoWave biosensor could be a useful technology to support home sleep apnea testing and warrant further exploration. Full article

Background: Sleep-disordered breathing (SDB) and intracerebral hemorrhage (ICH) share a bidirectional relationship: SDB may increase ICH risk, while ICH can induce or exacerbate SDB. However, the prevalence and characteristics of post-ICH SDB remain poorly defined. Objective: To estimate the prevalence of SDB among ICH survivors and examine associated clinical factors, including the relative burden of obstructive (OSA) versus central sleep apnea (CSA). Methods: A systematic review and meta-analysis were performed across PubMed, Scopus, CINAHL, and ClinicalTrials.gov. Studies assessing SDB in adults with ICH using American Academy of Sleep Medicine (AASM) category 1–4 diagnostic devices were included. Random-effects models estimated pooled prevalence at varying apnea–hypopnea index (AHI) thresholds, with subgroup analyses by setting, timing, geography, and diagnostic factors. Results: Seventeen studies met inclusion criteria. Pooled SDB prevalence was 85% (95% CI: 80–91%) at AHI > 5, with 49% (95% CI: 42–57%) experiencing moderate SDB (AHI > 15), and 21% (95% CI: 15–27%) experiencing severe SDB (AHI > 30). The prevalence of OSA predominated 73% (95% CI: 64% to 82%),while CSA occurred in 5% (95% CI: 2–9%), corresponding to a pooled RR of 7.44 and OR of 53.08 for OSA versus CSA. Conclusions: SDB—primarily OSA—is highly prevalent following ICH, underscoring the need for early, routine screening and intervention to improve neurological and cardiovascular outcomes. Full article

This paper proposes a low-cost measurement system that can be used to perform simultaneous blood pressure (BP) and breathing (BR) measurements. Regarding BP measurements, the main parameters that are accessed include systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure blood pressure (MAP), and heartbeat rate (HR). Concerning BR measurements, the main parameters that are accessed include the inspiration period and amplitude (IPA), the expiration period and amplitude (EPA), and the breathing rate (BR), as well as the statistical and standard deviation of all these parameters. The dual measurement capability of the proposed measurement system is very important since blood pressure and breathing parameters are not statistically independent and it is possible to obtain additional and valuable clinical information from the information provided by both biomedical variables when measured simultaneously. The analysis of the correlation between these variables is particularly important after performing intensive physical exercises, since it enables cardiac rehabilitation assessment, pre-surgical risk evaluation, detection of silent ischemia, and monitoring of chronic diseases recovery, among others. Regarding the performance evaluation of the proposed biomedical device, a prototype of the measurement system was developed, tested, and calibrated. Several experimental tests were carried out to evaluate the performance of the proposed measurement system and to obtain the correlation coefficients between different blood pressure and breathing parameters. The tests were based on a statistically significant number of measurements that were performed with a population that integrated twenty students in two groups with different habits of physical exercise practice but subjected to a set of common physical exercises, with graduated intensity levels. Full article

Current gene-based PCR diagnostics involving reverse-transcription polymerase chain reaction (RT-PCR) require at least several hours, expensive tools, and complicated sample collection methods to obtain results. A test for detecting volatile organic compounds (VOCs) in exhaled breath is advantageous as a simple, non-invasive, and fast screening method. In this study, a VOC detection system of array sensors was applied for the classification of breath control and COVID-19 virus infection. The ability to classify VOCs in the breath with COVID-19 virus infection has been studied with two metal-oxide (MOX) gas sensor arrays, commercially available sensors, and in-house sensors. The dataset of gas response signals from the array-type semiconductive gas sensors of the VOC detection system was analyzed using machine learning; principal component analysis (PCA) was used as a dimensionality-reduction method, and random forest (RF) and a convolutional neural network (CNN) were used as classification methods for the VOC concentration patterns in each breath. For the RF model, the accuracy results for the classification by two gas sensor arrays was 0.917 and this was improved by CO₂ calibration to 0.967, and the feature importance analysis revealed the importance of specific gas sensors. For the CNN, an input layer of a transformed gray-scale image with the shape of 12 data points × 8 sensors was used, and its accuracy reached 100% within a relatively small number of epochs, demonstrating a short training time, which is beneficial for breath detectors or e-nose devices. Full article

Background and Objective: Lennox–Gastaut syndrome (LGS) is a severe developmental and epileptic encephalopathy characterized by multiple seizure types, distinctive electroencephalography (EEG) abnormalities, and cognitive impairment. Sleep disturbances are highly prevalent in LGS and contribute substantially to reduced quality of life. However, no comprehensive analysis has yet been conducted to systematically examine key aspects of sleep—including architecture, microstructure, sleep-disordered breathing, and circadian regulation—leaving critical knowledge gaps. To address this, we conducted a scoping review to map the current evidence on sleep abnormalities in LGS and to identify priorities for future research. Method: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Embase, Ovid, and ClinicalTrials.gov from inception to October 2025 for studies evaluating sleep parameters in individuals with LGS or mixed epilepsy cohorts with ≥50% LGS cases. Eligible designs included observational and interventional studies using polysomnography, video-EEG, actigraphy, or sleep questionnaires. Data were synthesized narratively due to heterogeneity, and methodological quality was assessed using relevant Joanna Briggs Institute (JBI) checklists. Results: After screening 1242 articles, eleven studies met inclusion criteria, spanning 1986–2025 and conducted across four continents. Most were small single-center observational studies (5–16 LGS participants) using polysomnography as the primary assessment, with others employing wearable monitoring, surface and intracranial EEG, or circadian biomarker analyses. Across studies, individuals with LGS demonstrated markedly disrupted sleep architecture—notably reduced or absent rapid eye movement (REM) sleep, fragmented non-rapid eye movement (NREM) sleep, and attenuated spindles. Microstructural analysis showed elevated cyclic alternating pattern (CAP) rates, with epileptiform discharges clustering in CAP phase A. Sleep-disordered breathing (SDB) was common, particularly in adults, and associated with tonic seizures and central apneas. Circadian rhythm dysregulation, including altered melatonin and cortisol profiles, was also reported. A feasibility study demonstrated that home-based wearable devices and sleep apnea monitors were both acceptable and practical for use in children with LGS. No interventional studies have evaluated whether addressing sleep abnormalities modifies seizure or cognitive outcomes. Interpretation: Sleep in LGS is profoundly disrupted at both macrostructural and microstructural levels. These abnormalities may exacerbate seizure burden, cognitive impairment, and SUDEP risk, representing a potentially modifiable contributor to disease severity. Larger, prospective studies integrating polysomnography, wearable monitoring, and interventional approaches are needed to clarify causal mechanisms and therapeutic potential. Full article

Exhaled breath analysis is emerging as one of the most promising non-invasive strategies for the early detection of life-threatening diseases, especially lung cancer, where rapid and reliable diagnosis remains a major clinical challenge. In this study, we designed and optimized an electronic nose (e-nose) platform composed of quantum resistive vapor sensors (vQRSs) engineered by polymer-carbon nanotube nanocomposites via spray layer-by-layer assembly. Each sensor was tailored through specific polymer functionalization to tune selectivity and enhance sensitivity toward volatile organic compounds (VOCs) of medical relevance. The sensor array, combined with linear discriminant analysis (LDA), demonstrated the ability to accurately discriminate between cancer-related biomarkers in synthetic blends, even when present at trace concentrations within complex volatile backgrounds. Beyond artificial mixtures, the system successfully distinguished real exhaled breath samples collected under challenging conditions, including before and after smoking and alcohol consumption. These results not only validate the robustness and reproducibility of the vQRS-based array but also highlight its potential as a versatile diagnostic tool. Overall, this work underscores the relevance of nanocomposite chemo-resistive arrays for breathomics and paves the way for their integration into future portable e-nose devices dedicated to telemedicine, continuous monitoring, and early-stage disease diagnosis. Full article

Exhaled breath (EB) contains numerous volatile organic compounds (VOCs) that can reflect pathological metabolic processes, making breath analysis a promising non-invasive diagnostic approach. In particular, volatile aldehydes and ketones have been identified as disease biomarkers in EB. Gas sensors are expected to play a crucial role in the diagnosis of numerous diseases at an early stage. Among the various available approaches, sensors stand out as especially attractive tools for diagnosing diseases such as lung cancer (LC) and diabetes, due to their affordability and operational simplicity. There is an urgent need in the field of disease detection for the development of affordable, non-invasive, and user-friendly sensors capable of detecting various biomarkers. Devices of the new generation should also demonstrate high repeatability of measurements and extended operational stability of the employed sensors. Due to these demands, the past few years have seen significant advancements in the development and implementation of electronic noses (ENs), which are composed of an array of sensors for the determination of VOCs present in EB. To meet these requirements, the development and integration of advanced receptor coatings on sensor transducers is essential. These coatings include nanostructured materials, molecularly imprinted polymers, and bioreceptors, which collectively enhance selectivity, sensitivity, and operational stability. However, reliable biomarker detection in point-of-care (PoC) mode remains a significant challenge, constrained by several factors. This review provides a comprehensive and critical evaluation of recent studies demonstrating that the detection of VOCs using gas sensor platforms enables disease detection and can be implemented in PoC mode. Full article