Physiological measurement最新文献

Background.Non-invasive continuous blood pressure (BP) monitoring is of longstanding interest in various cardiovascular scenarios. In this context, pulse arrival time (PAT), i.e., a surrogate parameter for systolic BP (change), became very popular recently, especially in the context of cuffless BP measurement and dedicated lifestyle interventions. Nevertheless, there is also understandable doubt on its reliability in uncontrolled and mobile settings.Objective.The aim of this work is therefore the investigation whether PAT follows oscillometric systolic BP readings during moderate interventions by physical or mental activity using a medical grade handheld device for non-invasive PAT assessment.Approach.A study was conducted featuring an experimental group performing a physical and a mental task, and a control group. Oscillometric BP and PAT were assessed at baseline and after each intervention. Interventions were selected randomly but then performed sequentially in a counterbalanced order. Multivariate analyses of variance were used to test within-subject and between-subject effects for the dependent variables, followed by univariate analyses for post-hoc testing. Furthermore, correlation analysis was performed to assess the association of intervention effects between BP and PAT.Mainresults.The study included 51 subjects (31 females). Multivariate analysis of variances showed that effects in BP, heart rate, PAT and pulse wave parameters were consistent and significantly different between experimental and control groups. After physical activity, heart rate and systolic BP increased significantly whereas PAT decreased significantly. Mental activity leads to a decrease in systolic BP at stable heart rate. Pulse wave parameters follow accordingly by an increase of PAT and mainly unchanged pulse wave analysis features due to constant heart rate. Finally, also the control group behaviour was accurately registered by the PAT method compared to oscillometric cuff. Correlation analyses revealed significant negative associations between changes of systolic BP and changes of PAT from baseline to the physical task (-0.33 [-0.63, 0.01],p< 0.048), and from physical to mental task (-0.51 [-0.77, -0.14],p= 0.001), but not for baseline to mental task (-0.12 [-0,43,0,20],p= 0.50) in the experimental group.Significance.PAT and the used digital, handheld device proved to register changes in BP and heart rate reliably compared to oscillometric measurements during intervention. Therefore, it might add benefit to future mobile health solutions to support BP management by tracking relative, not absolute, BP changes during non-pharmacological interventions.

Objective. Pressure-volume loop analysis, traditionally performed by invasive pressure and volume measurements, is the optimal method for assessing ventricular function, while cardiac magnetic resonance (CMR) imaging is the gold standard for ventricular volume estimation. The aim of this study was to investigate the agreement between the assessment of end-systolic elastance (Ees) assessed with combined CMR and simultaneous pressure catheter measurements compared with admittance catheters in a porcine model.Approach. Seven healthy pigs underwent admittance-based pressure-volume loop evaluation followed by a second assessment with CMR during simultaneous pressure measurements.Main results. Admittance overestimated end-diastolic volume for both the left ventricle (LV) and the right ventricle (RV) compared with CMR. Further, there was an underestimation of RV end-systolic volume with admittance. For the RV, however, Ees was systematically higher when assessed with CMR plus simultaneous pressure measurements compared with admittance whereas there was no systematic difference in Ees but large differences between admittance and CMR-based methods for the LV.Significance. LV and RV Ees can be obtained from both admittance and CMR based techniques. There were discrepancies in volume estimates between admittance and CMR based methods, especially for the RV. RV Ees was higher when estimated by CMR with simultaneous pressure measurements compared with admittance.

Objective.This study aimed to examine differences in heart rate variability (HRV) across accelerometer-derived position, self-reported sleep, and different summary measures (sleep, 24 h HRV) in free-living settings using open-source methodology.Approach.HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate (HR) and accelerometry data were collected using single-lead electrocardiography and trunk- and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files were processed and analysed in the RHRV R package. Start time and duration spent in physical behaviours were extracted, and time and frequency analysis for each episode was performed. Differences in HRV estimates across activities were compared using linear mixed models adjusted for age and sex with subject ID as random effect. Next, repeated-measures Bland-Altman analysis was used to compare 24 h RMSSD estimates to HRV during self-reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the approach of employing accelerometer-determined episodes to examine activity-independent HRV was described.Main results.HRV was estimated for 31 289 episodes in 160 individuals (53.1% female) at a mean age of 41.4 years. Significant differences in HR and most markers of HRV were found across positions [Mean differences RMSSD: Sitting (Reference) - Standing (-2.63 ms) or Lying (4.53 ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor agreement between 24 h estimates compared to sleep HRV was detected. Sensitivity analyses confirmed that removing the first and last 30 s of accelerometry-determined HR episodes was an accurate strategy to account for orthostatic effects.Significance.Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep. The proposed approach for free-living HRV analysis may be an effective strategy to remove confounding by physical activity when the aim is to monitor general autonomic stress.

Objective. Snoring is the most typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) that can be used to develop a non-invasive approach for automatically detecting OSAHS patients.Approach. In this work, a model based on transfer learning and model fusion was applied to classify simple snorers and OSAHS patients. Three kinds of basic models were constructed based on pretrained Visual Geometry Group-16 (VGG16), pretrained audio neural networks (PANN), and Mel-frequency cepstral coefficient (MFCC). The XGBoost was used to select features based on feature importance, the majority voting strategy was applied to fuse these basic models and leave-one-subject-out cross validation was used to evaluate the proposed model.Main results. The results show that the fused model embedded with top-5 VGG16 features, top-5 PANN features, and MFCC feature can correctly identify OSAHS patients (AHI > 5) with 100% accuracy.Significance. The proposed fused model provides a good classification performance with lower computational cost and higher robustness that makes detecting OSAHS patients at home possible.

Objective.Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters.Approach.Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients.Main result.Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data.Significance.Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.

Objective.Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, cardiac magnetic resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities.Approach.This study introduces a deep model called ELRL-MD that combines ensemble learning and reinforcement learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the artificial bee colony (ABC) algorithm to enhance the starting point for learning. An array of convolutional neural networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process.Main results.ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2% and a geometric mean of 90.6%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs.Significance.The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges.

Objectives.The purpose of this study is to investigate the age dependence of bilateral frontal electroencephalogram (EEG) coupling characteristics, and find potential age-independent depth of anesthesia monitoring indicators for the elderlies.Approach.We recorded bilateral forehead EEG data from 41 patients (ranged in 19-82 years old), and separated into three age groups: 18-40 years (n= 12); 40-65 years (n= 14), >65 years (n= 15). All these patients underwent desflurane maintained general anesthesia (GA). We analyzed the age-related EEG spectra, phase amplitude coupling (PAC), coherence and phase lag index (PLI) of EEG data in the states of awake, GA, and recovery.Main results.The frontal alpha power shows age dependence in the state of GA maintained by desflurane. Modulation index in slow oscillation-alpha and delta-alpha bands showed age dependence and state dependence in varying degrees, the PAC pattern also became less pronounced with increasing age. In the awake state, the coherence in delta, theta and alpha frequency bands were all significantly higher in the >65 years age group than in the 18-40 years age group (p< 0.05 for three frequency bands). The coherence in alpha-band was significantly enhanced in all age groups in GA (p< 0.01) and then decreased in recovery state. Notably, the PLI in the alpha band was able to significantly distinguish the three states of awake, GA and recovery (p< 0.01) and the results of PLI in delta and theta frequency bands had similar changes to those of coherence.Significance.We found the EEG coupling and synchronization between bilateral forehead are age-dependent. The PAC, coherence and PLI portray this age-dependence. The PLI and coherence based on bilateral frontal EEG functional connectivity measures and PAC based on frontal single-channel are closely associated with anesthesia-induced unconsciousness.

Objective.Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts.Approach.In this work, we proposePower-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmarkPower-MFagainst three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA).Main results.Our results show thatPower-MFoutperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5% ± 0.5% F1-score, B2 Labour: 98.0% ± 3.0% F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise.Significance.Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

Objective.Intermittent hypoxia, the primary pathology of obstructive sleep apnea (OSA), causes cardiovascular responses resulting in changes in hemodynamic parameters such as stroke volume (SV), blood pressure (BP), and heart rate (HR). However, previous studies have produced very different conclusions, such as suggesting that SV increases or decreases during apnea. A key reason for drawing contrary conclusions from similar measurements may be due to ignoring the time delay in acquiring response signals. By analyzing the signals collected during hypoxia, we aim to establish criteria for determining the delay time between the onset of apnea and the onset of physiological parameter response.Approach.We monitored oxygen saturation (SpO₂), transcutaneous oxygen pressure (TcPO₂), and hemodynamic parameters SV, HR, and BP, during sleep in 66 patients with different OSA severity to observe body's response to hypoxia and determine the delay time of above parameters. Data were analyzed using the Kruskal-Wallis test, Quade test, and Spearman test.Main results.We found that simultaneous acquisition of various parameters inevitably involved varying degrees of response delay (7.12-25.60 s). The delay time of hemodynamic parameters was significantly shorter than that of SpO₂and TcPO₂(p< 0.01). OSA severity affected the response delay of SpO₂, TcPO₂, SV, mean BP, and HR (p< 0.05). SV delay time was negatively correlated with the apnea-hypopnea index (r= -0.4831,p< 0.0001).Significance.The real body response should be determined after removing the effect of delay time, which is the key to solve the problem of drawing contradictory conclusions from similar studies. The methods and important findings presented in this study provide key information for revealing the true response of the cardiovascular system during hypoxia, indicating the importance of proper signal analysis for correctly interpreting the cardiovascular hemodynamic response phenomena and exploring their physiological and pathophysiological mechanisms.