Physiological measurement最新文献

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.

Objective. This study evaluated the predictive performance of a deep learning approach to predict stroke volume variation (SVV) from central venous pressure (CVP) waveforms.Approach. Long short-term memory (LSTM) and the feed-forward neural network were sequenced to predict SVV using CVP waveforms obtained from the VitalDB database, an open-source registry. The input for the LSTM consisted of 10 s CVP waveforms sampled at 2 s intervals throughout the anesthesia duration. Inputs of the feed-forward network were the outputs of LSTM and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the SVV. The performance of SVV predicted by the deep learning model was compared to SVV estimated derived from arterial pulse waveform analysis using a commercialized model, EV1000.Main results. The model hyperparameters consisted of 12 memory cells in the LSTM layer and 32 nodes in the hidden layer of the feed-forward network. A total of 224 cases comprising 1717 978 CVP waveforms and EV1000/SVV data were used to construct and test the deep learning models. The concordance correlation coefficient between estimated SVV from the deep learning model were 0.993 (95% confidence interval, 0.992-0.993) for SVV measured by EV1000.Significance. Using a deep learning approach, CVP waveforms can accurately approximate SVV values close to those estimated using commercial arterial pulse waveform analysis.

Objective.This study aims to use recurrence quantification analysis (RQA) of uterine vectormyometriogram (VMG) created from the slow wave (SW) and high wave (HW) bands of electrohysterogram (EHG) signals and assess the directionality of the EHG activity (horizontal orX, vertical orY) in normal-weight (NW) and overweight (OW) women during the first stage of labor.Approach. The study involved 41 parturient women (NW = 21 and OW = 20) during the first stage of labor, all of whom were attended at the Gynecology and Obstetrics Hospital of the Maternal and Child Institute of the State of Mexico in Toluca, Mexico. Twenty-minute EHG signals were analyzed in horizontal and vertical directions. Linear and nonlinear indices such as dominant frequency (Dom), Sample Entropy (SampEn), and RQA measures of VMG were computed for SW and HW bands.Main results. Significant differences in SampEn and Dom were observed in the SW band between NW and OW in bothXandYdirections, indicating more regular dynamics of electrical uterine activity and a higher Dom in NW parturient women compared to OW women. Additionally, the RQA indices calculated from the VMG of SW were consistent and revealed that NW women exhibit more regular dynamics compared to OW women.Significance. The study demonstrates that RQA of VMG signals and EHG directionality differentiate uterine activity between NW and OW women during the first stage of labor. These findings suggest that the uterine vector may become more periodic, predictable, and stable in NW women compared to OW women. This highlights the importance of tailored clinical strategies for managing labor in OW women to improve maternal and infant outcomes.

Objective.We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.Approach.Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.Main results.Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.Significance.The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.

Objective.The present study investigated how breathing stimuli affect both non-linear and linear metrics of the autonomic nervous system (ANS).Approach.The analysed dataset consisted of 70 young, healthy volunteers, in whom arterial blood pressure (ABP) was measured noninvasively during 5 min sessions of controlled breathing at three different frequencies: 6, 10 and 15 breaths min^-1. CO₂concentration and respiratory rate were continuously monitored throughout the controlled breathing sessions. The ANS was characterized using non-linear methods, including phase-rectified signal averaging (PRSA) for estimating heart acceleration and deceleration capacity (AC, DC), multiscale entropy, approximate entropy, sample entropy, and fuzzy entropy, as well as time and frequency-domain measures (low frequency, LF; high-frequency, HF; total power, TP) of heart rate variability (HRV).Main results.Higher breathing rates resulted in a significant decrease in end-tidal CO₂concentration (p< 0.001), accompanied by increases in both ABP (p <0.001) and heart rate (HR,p <0.001). A strong, linear decline in AC and DC (p <0.001 for both) was observed with increasing breathing rate. All entropy metrics increased with breathing frequency (p <0.001). In the time-domain, HRV metrics significantly decreased with breathing frequency (p <0.01 for all). In the frequency-domain, HRV LF and HRV HF decreased (p= 0.038 andp= 0.040, respectively), although these changes were modest. There was no significant change in HRV TP with breathing frequencies.Significance.Alterations in CO₂levels, a potent chemoreceptor trigger, and changes in HR most likely modulate ANS metrics. Non-linear PRSA and entropy appear to be more sensitive to breathing stimuli compared to frequency-dependent HRV metrics. Further research involving a larger cohort of healthy subjects is needed to validate our observations.

Objective. Time-frequency (T-F) analysis of electroencephalographic (EEG) is a common technique to characterise spectral changes in neural activity. This study explores the limitations of utilizing conventional spectral techniques in examining cyclic event-related cortical activities due to challenges, including high inter-trial variability.Approach. Introducing the cycle-frequency (C-F) analysis, we aim to enhance the evaluation of cycle-locked respiratory events. For synthetic EEG that mimicked cycle-locked pre-motor activity, C-F had more accurate frequency and time localization compared to conventional T-F analysis, even for a significantly reduced number of trials and a variability of breathing rhythm.Main results. Preliminary validations using real EEG data during both unloaded breathing and loaded breathing (that evokes pre-motor activity) suggest potential benefits of using the C-F method, particularly in normalizing time units to cyclic activity phases and refining baseline placement and duration.Significance. The proposed approach could provide new insights for the study of rhythmic neural activities, complementing T-F analysis.