Physiological measurement最新文献

Objective. Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with significant health ramifications, including an elevated susceptibility to ischemic stroke, heart disease, and heightened mortality. Photoplethysmography (PPG) has emerged as a promising technology for continuous AF monitoring for its cost-effectiveness and widespread integration into wearable devices. Our team previously conducted an exhaustive review on PPG-based AF detection before June 2019. However, since then, more advanced technologies have emerged in this field.Approach. This paper offers a comprehensive review of the latest advancements in PPG-based AF detection, utilizing digital health and artificial intelligence (AI) solutions, within the timeframe spanning from July 2019 to December 2022. Through extensive exploration of scientific databases, we have identified 57 pertinent studies.Significance. Our comprehensive review encompasses an in-depth assessment of the statistical methodologies, traditional machine learning techniques, and deep learning approaches employed in these studies. In addition, we address the challenges encountered in the domain of PPG-based AF detection. Furthermore, we maintain a dedicated website to curate the latest research in this area, with regular updates on a regular basis.

Objective. This study aims to explore the possibility of using electrical impedance tomography (EIT) to assess pursed lips breathing (PLB) performance of patients with chronic obstructive pulmonary disease (COPD).Methods. 32 patients with COPD were assigned equally to either the conventional group or the EIT guided group. All patients were taught to perform PLB by a physiotherapist without EIT in the conventional group or with EIT in the EIT guided group for 10 min. The ventilation of all patients in the final test were continuously monitored using EIT and the PLB performances were rated by another physiotherapist before and after reviewing EIT. The global and regional ventilation between two groups as well as between quite breathing (QB) and PLB were compared and rating scores with and without EIT were also compared.Results.For global ventilation, the inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB was significantly larger than those during QB for both group (P< 0.001). The inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB in the EIT guided group were higher compared to those in the conventional group (P< 0.001), as well as expiratory flow expiratory uniformity and respiratory stability were better (P< 0.001). For regional ventilation, center of ventilation significantly decreased during PLB (P< 0.05). The expiratory time constant during PLB in the EIT guided group was greater than that in the conventional group (P< 0.001). Additionally, Bland-Altman plots analysis suggested a high concordance between subjective rating and rating with the help of EIT, but the score rated after EIT observation significantly lower than that rated subjectively in both groups (score drop of -2.68 ± 1.1 in the conventional group and -1.19 ± 0.72 in the EIT guided group,P< 0.01).Conclusion.EIT could capture the details of PLB maneuver, which might be a potential tool to quantitatively evaluate PLB performance and thus assist physiotherapists to teach PLB maneuver to patients.

Increasing interest in measuring key components of the 24 h activity cycle (24-HAC) [sleep, sedentary behavior (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA)] has led to a need for better methods. Single wrist-worn accelerometers and different self-report instruments can assess the 24-HAC but may not accurately classify time spent in the different components or be subject to recall errors.Objective. To overcome these limitations, the current study harmonized output from multiple complimentary research grade accelerometers and assessed the feasibility and logistical challenges of this approach.Approach. Participants (n= 108) wore an: (a) ActiGraph GT9X on the wrist, (b) activPAL3 on the thigh, and (c) ActiGraph GT3X+ on the hip for 7-10 d to capture the 24-HAC. Participant compliance with the measurement protocol was compared across devices and an algorithm was developed to harmonize data from the accelerometers. The resulting 24-HAC estimates were described within and across days.Main results. Usable data for each device was obtained from 94.3% to 96.7% of participants and 89.4% provided usable data from all three devices. Compliance with wear instructions ranged from 70.7% of days for the GT3X+ to 93.2% of days for the activPAL3. Harmonized estimates indicated that, on average, university students spent 34% of the 24 h day sleeping, 41% sedentary, 21% in LPA, and 4% in MVPA. These behaviors varied substantially by time of day and day of the week.Significance. It is feasible to use three accelerometers in combination to derive a harmonized estimate the 24-HAC. The use of multiple accelerometers can minimize gaps in 24-HAC data however, factors such as additional research costs, and higher participant and investigator burden, should also be considered.

Objective. Surface mechanomyography (sMMG) can measure oscillations of the activated muscle fibers in three axes (i.e.X,Y, andZ-axes) and has been used to describe motor unit activation patterns (X-axis). The application of blood flow restriction (BFR) is common in exercise studies, but the cuff may restrict muscle fiber oscillations. Therefore, the purpose of this investigation was to examine the acute effects of submaximal, fatiguing exercise with and without BFR on sMMG amplitude in theX,Y, andZ-axes among female participants.Approach. Sixteen females (21 ± 1 years) performed two separate exercise bouts to volitional exhaustion that consisted of unilateral, submaximal (50% maximal voluntary isometric contraction [MVIC]) intermittent, isometric, leg extensions with and without BFR. sMMG was recorded and examined across percent time to exhaustion (%TTE) in 20% increments. Separate 2-way repeated measures ANOVA models were constructed: (condition [BFR, non-BFR]) × (time [20, 40, 60, 80, and 100% TTE]) to examine absolute (m·s^-2) and normalized (% of pretest MVIC) sMMG amplitude in theX-(sMMG-X),Y-(sMMG-Y), andZ-(sMMG-Z) axes.Main results. The absolute sMMG-X amplitude responses were attenuated with the application of BFR (mean ± SD = 0.236 ± 0.138 m·s^-2) relative to non-BFR (0.366 ± 0.199 m·s^-2, collapsed across time) and for sMMG-Y amplitude at 60%-100% of TTE (BFR range = 0.213-0.232 m·s^-2versus non-BFR = 0.313-0.445 m·s^-2). Normalizing sMMG to pretest MVIC removed most, but not all the attenuation which was still evident for sMMG-Y amplitude at 100% of TTE between BFR (72.9 ± 47.2%) and non-BFR (98.9 ± 53.1%). Interestingly, sMMG-Z amplitude was not affected by the application of BFR and progressively decreased across %TTE (0.332 ± 0.167 m·s^-2to 0.219 ± 0.104 m·s^-2, collapsed across condition.)Significance. The application of BFR attenuated sMMG-X and sMMG-Y amplitude, although normalizing sMMG removed most of this attenuation. Unlike theXandY-axes, sMMG-Z amplitude was not affected by BFR and progressively decreased across each exercise bout potentially tracking the development of muscle fatigue.

Objective.Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers.Approach.This work describes the creation of a standard Python toolbox, denotedpyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter.Main results.The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points.Significance.Based on these fiducial points,pyPPGengineered a set of 74 PPG biomarkers. Studying PPG time-series variability usingpyPPGcan enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models.pyPPGis available onhttps://physiozoo.com/.

Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

Objective. Since pulse wave transit time (PWTT) shortens as pulmonary artery pressure (PAP) increases it was suggested as a potential non-invasive surrogate for PAP. The state of tidal lung filling is also known to affect PWTT independently of PAP. The aim of this retrospective analysis was to test whether respiratory gating improved the correlation coefficient between PWTT and PAP.Approach. In each one of five anesthetized and mechanically ventilated pigs two high-fidelity pressure catheters were placed, one directly behind the pulmonary valve, and the second one in a distal branch of the pulmonary artery. PAP was raised using the thromboxane A2 analogue U46619 and animals were ventilated in a pressure controlled mode (I:E ratio 1:2, respiratory rate 12/min, tidal volume of 6 ml kg^-1). All signals were recorded using the multi-channel platform PowerLab^®. The arrival of the pulse wave at each catheter tip was determined using a MATLAB-based modified hyperbolic tangent algorithm and PWTT calculated as the time interval between these arrivals.Main results. Correlation coefficient for PWTT and mean PAP wasr= 0.932 for thromboxane. This correlation coefficient increased considerably when heart beats either at end-inspiration (r= 0.978) or at end-expiration (r= 0.985) were selected (=respiratory gating).Significance. The estimation of mean PAP from PWTT improved significantly when taking the respiratory cycle into account. Respiratory gating is suggested to improve for the estimation of PAP by PWTT.

Objective.Extracting discriminative spatial information from multiple electrodes is a crucial and challenging problem for electroencephalogram (EEG)-based emotion recognition. Additionally, the domain shift caused by the individual differences degrades the performance of cross-subject EEG classification.Approach.To deal with the above problems, we propose the cerebral asymmetry representation learning-based deep subdomain adaptation network (CARL-DSAN) to enhance cross-subject EEG-based emotion recognition. Specifically, the CARL module is inspired by the neuroscience findings that asymmetrical activations of the left and right brain hemispheres occur during cognitive and affective processes. In the CARL module, we introduce a novel two-step strategy for extracting discriminative features through intra-hemisphere spatial learning and asymmetry representation learning. Moreover, the transformer encoders within the CARL module can emphasize the contributive electrodes and electrode pairs. Subsequently, the DSAN module, known for its superior performance over global domain adaptation, is adopted to mitigate domain shift and further improve the cross-subject performance by aligning relevant subdomains that share the same class samples.Main Results.To validate the effectiveness of the CARL-DSAN, we conduct subject-independent experiments on the DEAP database, achieving accuracies of 68.67% and 67.11% for arousal and valence classification, respectively, and corresponding accuracies of 67.70% and 67.18% on the MAHNOB-HCI database.Significance.The results demonstrate that CARL-DSAN can achieve an outstanding cross-subject performance in both arousal and valence classification.

Objective. Data from two-plane electrical impedance tomography (EIT) can be reconstructed into various slices of functional lung images, allowing for more complete visualisation and assessment of lung physiology in health and disease. The aim of this study was to confirm the ability of 3D EIT to visualise normal lung anatomy and physiology at rest and during increased ventilation (represented by rebreathing).Approach. Two-plane EIT data, using two electrode planes 20 cm apart, were collected in 20 standing sedate horses at baseline (resting) conditions, and during rebreathing. EIT data were reconstructed into 3D EIT whereby tidal impedance variation (TIV), ventilated area, and right-left and ventral-dorsal centres of ventilation (CoV_RLand CoV_VD, respectively) were calculated in cranial, middle and caudal slices of lung, from data collected using the two planes of electrodes.Main results. There was a significant interaction of time and slice for TIV (p< 0.0001) with TIV increasing during rebreathing in both caudal and middle slices. The ratio of right to left ventilated area was higher in the cranial slice, in comparison to the caudal slice (p= 0.0002). There were significant effects of time and slice on CoV_VDwhereby the cranial slice was more ventrally distributed than the caudal slice (p< 0.0009 for the interaction).Significance. The distribution of ventilation in the three slices corresponds with topographical anatomy of the equine lung. This study confirms that 3D EIT can accurately represent lung anatomy and changes in ventilation distribution during rebreathing in standing sedate horses.