Physiological measurement最新文献

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.

Objective.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.Approach.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.Main results.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.Significance.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.

Objective.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.Approach.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.Main results.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).Significance.CIMAPtoolbox offers scientists a standardized method for analyzing muscle activation patterns during cyclical movements.

Objective.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).Approach.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.Main Results.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 (r= 0.65, 95% CI: 0.62, 0.69;p< 0.001), as did NRS and mYPAS (r= 0.39, 95% CI: 0.34, 0.44;p< 0.001). The correlation between HR and CI was stronger with NRS (r= 0.24, 95% CI: 0.19, 0.29;p< 0.001; andr= 0.13, 95% CI: 0.07, 0.19;p< 0.001, respectively) than with mYPAS (r= 0.06, 95% CI: 0.00, 0.11;p= 0.046; andr= 0.08, 95% CI: 0.02, 0.14;p= 0.006, respectively). The correlation with mYPAS for both HR and CI varied significantly in both direction and magnitude across the different time points.Significance.A modest yet discernable correlation exists between objective measures (HR and CI) and established subjective anxiety assessments.

Objective.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.Approach.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.Main Results.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.Significance.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.

Objective. 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.Approach.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.Main results.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 cmH₂O depending on the chosen ROI selection method.Significance.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.

Objective: 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.

Approach: We investigated the relationship between PTT and systolic BP (SBP) at different HR levels by introducing the heterogeneous treatment effects (HTE) as a quantitative indicator. Compared to the average HR calculated using traditional indicators (e. g. regression coefficient, correlation coefficient), the HTE calculation method can compute the relationship between PTT and SBP at different HR levels, and reduce the influence of confounding factors.

Main results: We analyzed the HTE of PTT and SBP of 47 subjects who are resting healthy young people with varying levels of HR. The results showed that the strength of the HTE of PTT and SBP varied with HR, indicating that the strength of the causal relationship between PTT and SBP is subject to HR levels. Whereas the correlation between SBP and PTT was individual dependent; either the strength or the direction of the correlation can vary with HR. We further investigated the group in which PTT and SBP exhibited a negative correlation, and found that about 50% of the subjects showed enhanced strength of HTE in with an increase in HR and the remaining showed the opposite.

Significance: This study means that HR needs to be considered when PTT is used as an indicator of SBP.

Objective.Accurate prediction of unmeasured muscle excitations can reduce the required wearable surface electromyography (sEMG) sensors, which is a critical factor in the study of physiological measurement. Synergy extrapolation uses synergy excitations as building blocks to reconstruct muscle excitations. However, the practical application of synergy extrapolation is still limited as the extrapolation process utilizes unmeasured muscle excitations it seeks to reconstruct. This paper aims to propose and derive methods to provide an avenue for the practical application of synergy extrapolation with non-negative matrix factorization (NMF) methods.Approach.Specifically, a tunable Gaussian-Laplacian mixture distribution NMF (GLD-NMF) method and related multiplicative update rules are derived to yield appropriate synergy excitations for extrapolation. Furthermore, a template-based extrapolation structure (TBES) is proposed to extrapolate unmeasured muscle excitations based on synergy weighting matrix templates totally extracted from measured sEMG datasets, improving the extrapolation performance. Moreover, we applied the proposed GLD-NMF method and TBES to selected muscle excitations acquired from a series of single-leg stance tests, walking tests and upper limb reaching tests.Main results.Experimental results show that the proposed GLD-NMF and TBES could extrapolate unmeasured muscle excitations accurately. Moreover, introducing synergy weighting matrix templates could decrease the number of sEMG sensors in a series of experiments. In addition, verification results demonstrate the feasibility of applying synergy extrapolation with NMF methods.Significance.With the TBES method, synergy extrapolation could play a significant role in reducing data dimensions of sEMG sensors, which will improve the portability of sEMG sensors-based systems and promotes applications of sEMG signals in human-machine interfaces scenarios.

Objective: 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. .

Objective.This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads.Approach.We utilized experimental data from two distinct centers. Langendorff-perfused pig (n= 2) and dog (n= 2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI.Main results.The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended.Significance.This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance.