Sulfur hexafluoride (SF) multiple-breath washout (MBW) assesses ventilation inhomogeneity, as an early marker of obstructive respiratory diseases. Primary outcomes are customarily washout-derived, and it is unclear whether the preceding SF-washin can provide similar estimates. We aimed to assess comparability of primary SF-MBW outcomes between washin and washout phases of infant SF-MBW data measured with the WBreath (ndd Medizintechnik AG, Zurich, Switzerland) and Spiroware (Eco Medics AG, Duernten, Switzerland) MBW-setups, respectively.We assessed mean relative differences in lung clearance index (LCI) and functional residual capacity (FRC) between the washin and washout of existing SF-MBW data from healthy infants and infants with cystic fibrosis (CF). We assessed whether these differences exceeded the mean relative within-test between-trial differences of washout-derived outcomes, which can be attributed to natural variability. We also explored non-physiological factors using a pediatric lung simulator.LCI and FRC from washin and washout were not comparable, for both setups. The mean difference (SD) in LCI between washin and washout was 2.3(10.8)% for WBreath and -9.7(8.0)% for Spiroware, while in FRC it was -4.7(8.4)% for WBreath and -2.3(9.7)% for Spiroware. These differences exceeded the within-test between-trial differences in washout-derived outcomes. Outcomes from washin and washout were also not comparable in a pediatric lung simulator.Outcomes of the washin and washout were not comparable due to an interplay of physiological and non-physiological factors, and cannot be used interchangeably.

. Pulse pressure waves contain information about human physiology. There is a need for a simple, accurate way to know cardiovascular health in the clinic, so as to realize the implementation of convenient and effective early health monitoring for patients with arteriosclerosis.. This study proposes an arteriosclerosis assessment method based on fitting a lognormal function, along with improving a conventional electronic sphygmomanometer. During the deflation phase of blood pressure measurement, the cuff pressure was kept constant (40 mmHg) and an additional 10 s of pulse signal was acquired. To derive the pulse pressure waveforms for a single cycle, the acquired pulse data of 101 cases were preprocessed in this study, including filtering for noise removal, onset point identification, removal of baseline drift, and normalization. In this study, an improved pulse resolution algorithm is proposed for the multimodal problem of the pulse wave, combining waveform matching and threshold setting, and finally obtaining the resolution parameters of the lognormal function with an average error less than 1.5%.. According to the correlation analysis, the resolved parameters,,,, andwere significantly correlated with brachial-ankle Pulse Wave Velocity, and the absolute correlation range in 0.17-0.53, which can be used as a reference index for arteriosclerosis. An arteriosclerosis assessment model was constructed based on the support vector mechanism, and the prediction accuracy was 91.1%.. This study provides a new solution idea for the arteriosclerosis assessment method as well as the pulse resolution algorithm, which has a greater reference value.

Obstructive sleep apnea (OSA) is associated with deficits in vigilance. This work explored the temporal patterns of OSA-related events during sleep and vigilance levels measured by the psychomotor vigilance test (PVT) in patients undergoing polysomnography (PSG) for suspected OSA.The PVT was conducted prior to in-laboratory PSG for 80 patients suspected of having OSA. Three groups were formed based on PVT-RT-outcomes and participants were randomly allocated into Training (= 55) and Test (= 25) samples. Sleep epochs of non-rapid-eye movement (NREM) electroencephalographic (EEG) asynchrony data, and REM and NREM data for respiratory, arousal, limb movement and desaturation events were analysed. The data were segmented by sleep stage, by sleep blocks (SB) of stable Stage N2, Stage N3, mixed-stage NREM sleep (NXL), and, by Time of Night (TN) across sleep. Models associating this data with PVT groups were developed and tested.model using NREM EEG asynchrony data segmented by SB and TN achieved 81.9% accuracy in the Test Cohort. Models based on interhemispheric asynchrony SB data and OSA data segmented by TN achieved 80.6% and 79.5% respectively.Novel data segmentation methods via blocks of NXL and TN have improved our understanding of the relationship between sleep, OSA and vigilance.

Amplitude Spectrum Area (AMSA) of the electrocardiogram (ECG) waveform during ventricular fibrillation (VF) has shown promise as a predictor of defibrillation success during cardiopulmonary resuscitation (CPR). However, AMSA relies on the magnitude of the ECG waveform, raising concerns about reproducibility across different settings that may introduce magnitude bias. This study aimed to evaluate different AMSA normalization approaches and their impact on removing bias while preserving predictive value.

This study evaluates the effectiveness of four machine Learning algorithms in classifying physiological stress using Heart Rate Variability (HRV) and Pulse Rate Variability (PRV) time series, comparing an automatic feature selection based on Akaike's criterion to a physiologically-based feature selection approach.

The accurate temporal analysis of muscle activations is of great importance in several research areas spanning from the assessment of altered muscle activation patterns in orthopaedic and neurological patients to the monitoring of their motor rehabilitation. Several studies have highlighted the challenge of understanding and interpreting muscle activation patterns due to the high cycle-by-cycle variability of the sEMG data. This makes it difficult to interpret results and to use sEMG signals in clinical practice. To overcome this limitation, this study aims at presenting a toolbox to help scientists easily characterize and assess muscle activation patterns during cyclical movements.CIMAP(Clustering for the Identification of Muscle Activation Patterns) is an open-source Python toolbox based on agglomerative hierarchical clustering that aims at characterizing muscle activation patterns during cyclical movements by grouping movement cycles showing similar muscle activity.From muscle activation intervals to the graphical representation of the agglomerative hierarchical clustering dendrograms, the proposed toolbox offers a complete analysis framework for enabling the assessment of muscle activation patterns. The toolbox can be flexibly modified to comply with the necessities of the scientist.CIMAPis addressed to scientists of any programming skill level working in different research areas such as biomedical engineering, robotics, sports, clinics, biomechanics, and neuroscience. CIMAP is freely available on GitHub (https://github.com/Biolab-PoliTO/CIMAP).CIMAPtoolbox offers scientists a standardized method for analyzing muscle activation patterns during cyclical movements.

Pulse transit time (PTT) is a popular indicator of blood pressure (BP) changes. However, the relationship between PTT and BP is somehow individual dependent, resulting in the inaccuracy of PTT-based BP estimation. Confounding factors, e.g., heart rate (HR), of PTT and BP could be the primary cause. In this study we attempt to explore the impact of HR as a window to look at the influence of confounding factors on the relationship between PTT and BP.

The radial artery, one of the terminal branches of the forearm, is utilized for vascular access and in various non-invasive measurement method, providing crucial medical insights. Various sensor technologies have been developed, each suited to specific characterization requirements. The work presented in this paper is based on a systematic literature review of the main publications relating to this topic. Analysis of the forearm vascular system complex array of anatomical structures shows that the radial artery can be characterized by its size, position, elasticity, tissue evaluation, blood flow and blood composition. The survey of medical procedures for patient monitoring, diagnosis and pre-operative validation shows the use of measures for pulse wave, blood pressure, heart rate, skin temperature, tissue response,…By exploring sensor technologies used for artery characterization, we produce a synthesis of measurement principles, measured phenomena and measurement accuracy for capacitive, piezoresistive, bioimpedance, thermography, fiber optic based, piezoelectric and photoacoustic sensors. A comparative study is conducted for sensor technologies by considering the metrics of the information to be collected and the associated accuracy as well as the portability, the complexity of the processing, the cost and the mode of contact with the arm. Finally, a comprehensive framework is proposed to facilitate informed decisions in the development of medical devices tailored to specific characterization needs.

Pediatric patients undergoing medical procedures often grapple with preoperative anxiety, which can impact postoperative outcomes. While healthcare providers subjectively assess anxiety, objective quantification tools remain limited. This study aimed to evaluate two objective measures-cardiac index (CI) and heart rate (HR) in comparison with validated subjective assessments, the modified Yale Preoperative Anxiety Scale (mYPAS) and the numeric rating scale (NRS).In this prospective, observational cohort study, children ages 5-17 undergoing ambulatory endoscopy under general anesthesia underwent simultaneous measurement of objective and subjective measures at various time points: baseline, intravenous placement, two-minutes post-IV placement, when departing the preoperative bay, and one-minute prior to anesthesia induction.Of the 86 enrolled patients, 77 had analyzable CI data and were included in the analysis. The median age was 15 years (interquartile range 13, 16), 55% were female, and most were American Society of Anesthesiologists (ASA) Physical Status 2 (64%), and had previous endoscopies (53%). HR and CI correlated overall (= 0.65, 95% CI: 0.62, 0.69;< 0.001), as did NRS and mYPAS (= 0.39, 95% CI: 0.34, 0.44;< 0.001). The correlation between HR and CI was stronger with NRS (= 0.24, 95% CI: 0.19, 0.29;< 0.001; and= 0.13, 95% CI: 0.07, 0.19;< 0.001, respectively) than with mYPAS (= 0.06, 95% CI: 0.00, 0.11;= 0.046; and= 0.08, 95% CI: 0.02, 0.14;= 0.006, respectively). The correlation with mYPAS for both HR and CI varied significantly in both direction and magnitude across the different time points.A modest yet discernable correlation exists between objective measures (HR and CI) and established subjective anxiety assessments.

. This study provides an adaptive threshold algorithm for burst detection in electroencephalograms (EEG) of preterm infantes and evaluates its performance using clinical real-world EEG data.. We developed an adaptive threshold algorithm for burst detection in EEG recordings from preterm infants. To assess its applicability in the real-world, we tested the algorithm on a dataset of 30 clinical EEG recordings which were not preselected for good quality, to ensure a real-world scenario.. Interrater agreement was substantial at a kappa of 0.73 (0.68-0.79 inter-quantile range). The performance of the algorithm showed a similar agreement with one clinical expert of 0.73 (0.67-0.76) and a sensitivity and specificity of 0.90 (0.82-0.94) and 0.95 (0.93-0.97), respectively.. The adaptive threshold algorithm demonstrated robust performance in detecting burst patterns in clinical EEG data from preterm infants, highlighting its practical utility. The fine-tuned algorithm achieved similar performance to human raters. The algorithm proves to be a valuable tool for automated burst detection in the EEG of preterm infants.

. Geometrical region of interest (ROI) selection in electrical impedance tomography (EIT) monitoring may lack sensitivity to subtle changes in ventilation distribution. Therefore, we demonstrate a new physiological method for ROI definition. This is relevant when using ROIs to compute subsequent EIT-parameters, such as the ventral-to-dorsal ratio during a positive end-expiratory pressure (PEEP) trial.Our physiological approach divides an EIT image to ensure exactly 50% tidal impedance variation in the ventral and dorsal region. To demonstrate the effects of our new method, EIT measurements during a decremental PEEP trial in 49 mechanically ventilated ICU-patients were used. We compared the center of ventilation (CoV), a robust parameter for changes in ventro-dorsal ventilation distribution, to our physiological ROI selection method and different commonly used ROI selection methods. Moreover, we determined the impact of different ROI selection methods on the PEEP level corresponding to a ventral-to-dorsal ratio closest to 1.The division line separating the ventral and dorsal ROI was closer to the CoV for our new physiological method for ROI selection compared to geometrical ROI definition. Moreover, the PEEP level corresponding to a ventral-to-dorsal ratio of 1 is strongly influenced by the chosen ROI selection method, which could have a profound clinical impact; the within-subject range of PEEP level was 6.2 cmHO depending on the chosen ROI selection method.Our novel physiological method for ROI definition is sensitive to subtle ventilation-induced changes in regional impedance (i.e. due to (de)recruitment) during mechanical ventilation, similar to the CoV.

Continuous monitoring of blood pressure (BP) is crucial for daily healthcare. Although invasive methods provide accurate continuous BP measurements, they are not suitable for routine use. Photoplethysmography (PPG), a non-invasive technique that detects changes in blood volume within the microcirculation using light, shows promise for BP measurement. The primary goal of this study is to develop a novel cuffless method based on PPG for accurately estimating continuous BP.We introduce BP-Diff, an end-to-end method for cuffless continuous BP waveform estimation utilizing a conditional diffusion probability model combined with a U-Net architecture. This approach takes advantage of the stochastic properties of diffusion models and the strong feature representation capabilities of U-Net. It integrates the continuous BP waveform as the initial status and uses the PPG signal and its derivatives as conditions to guide the training and sampling process.BP-Diff was evaluated using both uncalibrated and calibrated schemes. The results indicate that, when uncalibrated, BP-Diff can accurately track BP dynamics, including peak and valley positions, as well as timing. After calibration, BP-Diff achieved highly accurate BP estimations. The mean absolute error of the estimated BP waveforms, along with the systolic BP, diastolic BP, and mean arterial pressure from the calibrated BP-Diff model, were 2.99 mmHg, 2.6 mmHg, 1.4 mmHg, and 1.44 mmHg, respectively. Consistency tests, including Bland-Altman analysis and Pearson correlation, confirmed its high reliability compared to reference BP. BP-Diff meets the American Association for Medical Instrumentation standards and has achieved a Grade A from the British Hypertension Society.This study utilizes PPG signals to develop a novel cuffless continuous BP measurement method, demonstrating superiority over existing approaches. The method is suitable for integration into wearable devices, providing a practical solution for continuous BP monitoring in everyday healthcare.

. Pendelluft is the movement of air between lung regions, and electrical impedance tomography (EIT) has shown an ability to detect and monitor it.In this note, we propose a functional EIT measure which quantifies the reverse airflow seen in pendelluft: the(FRIC).. FRIC measures the fraction of reverse flow in each pixel waveform (as an image) or globally (as a single parameter).. Such a measure is designed to be a more specific measure than previous approaches, to enable comparative studies of the pendelluft, and to help clarify the effect of ventilation strategies.

This study examined sex-related differences in fatigability and neuromuscular responses using surface electromyographic (sEMG) and mechanomyographic (sMMG) amplitude (AMP) and frequency (MPF) during fatiguing, maximal, bilateral isometric leg extensions.Twenty recreationally active males and females with resistance training experience performed continuous, maximal effort, bilateral isometric leg extensions until their force reduced by 50%. Linear mixed effect models analyzed patterns of force, sEMG, and sMMG AMP and MPF responses in the dominant limb. An independent samples t-test compared time-to-task failure (TTF) between sexes.There were no significant differences in TTF between males and females. However, males experienced a greater rate of force loss compared to females. Furthermore, sEMG AMP and MPF and sMMG AMP responses followed similar linear trends for both sexes, while sMMG MPF showed non-linear responses with sex-dependent differences.These data suggest that although TTF was similar, males had a higher rate of force reduction, likely due to greater absolute strength. Furthermore, despite parallel changes in sEMG AMP and MPF, as well as sMMG AMP, the divergent responses observed in sMMG MPF highlight sex-dependent differences in how males and females experience changes in the firing rates of active motor units during sustained maximal contractions.

This paper tackles the challenge of accurately detecting second-degree and third-degree atrioventricular block (AVB) in electrocardiogram (ECG) signals through automated algorithms. The inaccurate detection of P-waves poses a difficulty in this process. To address this limitation, we propose a reliable method that significantly improves the performances of AVB detection by precisely localizing P-waves.Our proposed P-WaveNet utilized an attention mechanism to extract spatial and temporal features, and employs a bidirectional long short-term memory module to capture inter-temporal dependencies within the ECG signal. To overcome the scarcity of data for second-degree and third-degree AVB (2AVB,3AVB), a mathematical approach was employed to synthesize pseudo-data. By combining P-wave positions identified by the P-WaveNet with key medical features such as RR interval rhythm and PR intervals, we established a classification rule enabling automatic AVB detection.. The P-WaveNet achieved an F1 score of 93.62% and 91.42% for P-wave localization on the QT Dataset and Lobachevsky University dataset datasets, respectively. In the BUTPDB dataset, the F1 scores for P-wave localization in ECG signals with 2AVB and 3AVB were 98.29% and 62.65%, respectively. Across two independent datasets, the AVB detection algorithm achieved F1 scores of 83.33% and 84.15% for 2AVB and 3AVB, respectively.Our proposed P-WaveNet demonstrates accurate identification of P-waves in complex ECGs, significantly enhancing AVB detection efficacy. This paper's contributions stem from the fusion of medical expertise with data augmentation techniques and ECG classification. The proposed P-WaveNet demonstrates potential clinical applicability.

This study aimed to develop convolutional neural networks (CNNs) models to predict the energy expenditure (EE) of children from raw accelerometer data. Additionally, this study sought to external validation of the CNN models in addition to the linear regression (LM), random forest (RF), and full connected neural network (FcNN) models published in Steenbock(201994-102).Included in this study were 41 German children (3.0-6.99 years) for the training and internal validation who were equipped with GENEActiv, GT3X+, and activPAL accelerometers. The external validation dataset consisted of 39 Canadian children (3.0-5.99 years) that were equipped with OPAL, GT9X, GENEActiv, and GT3X+ accelerometers. EE was recorded simultaneously in both datasets using a portable metabolic unit. The protocols consisted of a semi-structured activities ranging from low to high intensities. The root mean square error (RMSE) values were calculated and used to evaluate model performances.(1) The CNNs outperformed the LM (13.17%-23.81% lower mean RMSE values), FcNN (8.13%-27.27% lower RMSE values) and the RF models (3.59%-18.84% lower RMSE values) in the internal dataset. (2) In contrast, it was found that when applied to the external Canadian dataset, the CNN models had consistently higher RMSE values compared to the LM, FcNN, and RF.Although CNNs can enhance EE prediction accuracy, their ability to generalize to new datasets and accelerometer brands/models, is more limited compared to LM, RF, and FcNN models.

The association between muscle damage and skin temperature is controversial. We hypothesize that including metrics that are more sensitive to individual responses by considering variability and regions representative of higher temperature could influence skin temperature outcomes. Here, the objective of the study was to determine whether using alternative metrics (TMAX, entropy, and pixelgraphy) leads to different results than mean, maximum, minimum, and standard deviation (SD) skin temperature when addressing muscle damage using infrared thermography.Thermal images from four previous investigations measuring skin temperature before and after muscle damage in the anterior thigh and the posterior lower leg were used. The TMAX, entropy, and pixelgraphy (percentage of pixels above 33 °C) metrics were applied.On 48 h after running a marathon or half-marathon, no differences were found in skin temperature when applying any metric. Mean, minimum, maximum, TMAX, and pixelgraphy were lower 48 h after than at basal condition following quadriceps muscle damage (< 0.05). Maximum skin temperature and pixelgraphy were lower 48 h after than the basal condition following muscle damage to the triceps sural (< 0.05). Overall, TMAX strongly correlated with mean (= 0.85) and maximum temperatures (= 0.99) and moderately with minimum (= 0.66) and pixelgraphy parameter (= 0.64). Entropy strongly correlates with SD (= 0.94) and inversely moderately with minimum temperature (= -0.53). The pixelgraphy moderately correlated with mean (= 0.68), maximum (= 0.62), minimum (= 0.58), and TMAX (= 0.64).Using alternative metrics does not change skin temperature outcomes following muscle damage of lower extremity muscle groups.

Despite the growing interest in understanding the role of triggers of paroxysmal atrial fibrillation (AF), solutions beyond questionnaires to identify a broader range of triggers remain lacking. This study aims to investigate the relation between triggers detected in wearable-based physiological signals and the occurrence of AF episodes.Week-long physiological signals were collected during everyday activities from 35 patients with paroxysmal AF, employing an ECG patch attached to the chest and a photoplethysmogram (PPG)-based wrist-worn device. The signals acquired by the patch were used for detecting potential triggers due to physical exertion, psychophysiological stress, lying on the left side, and sleep disturbances. To assess the relation between detected triggers and the occurrence of AF episodes, a measure of relational strength is employed accounting for pre- and post-trigger AF burden. The usefulness of ECG- and PPG-based AF detectors in determining AF burden and assessing the relational strength is also analyzed.Physical exertion emerged as the trigger associated with the largest increase in relational strength for the largest number of patients ( < 0.01). On the other hand, no significant difference was observed for psychophysiological stress and sleep disorders. The relational strength of the detected AF exhibits a moderate correlation with the relational strength of annotated AF, with = 0.66 for ECG-based AF detection and = 0.62 for PPG-based AF detection.The findings indicate a patient-specific increase in relational strength for all four types of trigger.The proposed approach has the potential to facilitate the implementation of longitudinal studies and can serve as a less biased alternative to questionnaire-based AF trigger detection.

Obstructive sleep apnoea (OSA) affects an estimated 936 million people worldwide, yet only 15% receive a definitive diagnosis. Diagnosis of OSA poses challenges due to the dynamic nature of physiological signals such as oxygen saturation (SpO) and heart rate variability (HRV). Linear analysis methods may not fully capture the irregularities present in these signals. The application of entropy of routine physiological signals offers a promising method to better measure variabilities in dynamic biological data. This review aims to explore entropy changes in physiological signals among individuals with OSA.Keyword and title searches were performed on Medline, Embase, Scopus, and CINAHL databases. Studies had to analyse physiological signals in OSA using entropy. Quality assessment used the Newcastle-Ottawa Scale. Evidence was qualitatively synthesised, considering entropy signals, entropy type, and time-series length.Twenty-two studies were included. Multiple physiological signals related to OSA, including SpO, HRV, and the oxygen desaturation index (ODI), have been investigated using entropy. Results revealed a significant decrease in HRV entropy in those with OSA compared to control groups. Conversely, SpOand ODI entropy values were increased in OSA. Despite variations in entropy types, time scales, and data extraction devices, studies using receiver operating characteristic curves demonstrated a high discriminative accuracy (>80% AUC) in distinguishing OSA patients from control groups.. This review highlights the potential of SpOentropy analysis in developing new diagnostic indices for patients with OSA. Further investigation is needed before applying this technique clinically.

Sleep arousal, a frequent interruption in sleep with complete or partial wakefulness from sleep, may indicate a breathing disorder, neurological disorder, or sleep-related disorders. These phenomena necessitate the detection of sleep arousals. Uses of deep learning methods to detect features inhibits the scope to understand the specific distinctive nature of the signals and reduces the interpretability of the model. To evade these inconsistencies and to improve the classification performance of the sleep arousal detection model, a model has been proposed in this study on the prospect of understandable features that are useful in detecting sleep arousals. Approach: Time-frequency analysis of the electroencephalogram (EEG) signals was performed using Short-Time Fourier Transform (STFT). From the STFT coefficients, the spectrogram and instantaneous properties (frequency, bandwidth, power spectrum, band energy, local maxima, and band energy ratios) were investigated. From these properties, instantaneous features were generated by statistical analysis. Additive feature sets and reduced feature sets, formed by adding features successively and reducing features using the analysis of variance test respectively, were subjected to a tri-layered neural network classifier to evaluate the capability of the features to detect sleep arousal and normal sleep segments. Main results: The reduced feature set (Set 6) has proved to be efficacious in facilitating superior classification performance metrics (accuracy, sensitivity, specificity, and AUC of 89.14%, 83.52%, 89.49%, and 93.84% respectively). Significance: This efficient model can be incorporated with an automatic sleep apnea detection system where the estimation of hypopnea requires the detection of sleep arousal. .