Physiological measurement最新文献

Computational simulations are widely adopted in cardiovascular biomechanics because of their capability of producing physiological data otherwise impossible to measure with non-invasive modalities. Objective This study presents openBF, a computational library for simulating the blood dynamics in the cardiovascular system. Approach openBF adopts a 1-D viscoelastic representation of the arterial system, and is coupled with 0-D windkessel models at the outlets. Equations are solved by means of the finite-volume method and the code is written in Julia. We assess its predictions by performing a multiscale validation study on several domains available from the literature. Main results At all scales, which range from individual arteries to a population of virtual subjects, openBF's solution show excellent agreement with the solutions from existing software. For reported simulations, openBF requires low computational times. Significance openBF is easy to install, use, and deploy on multiple platforms and architectures, and gives accurate prediction of blood dynamics in short time-frames. It is actively maintained and available open-source on GitHub, which favours contributions from the biomechanical community.

Objective: Pulse Decomposition Analysis (PDA) has been proposed to extract reliable information from photoplethysmography (PPG) morphology by decomposing the signal in its physiological sub-waves. The Gaussian model has been widely used in the literature, even though it often underperforms because it is limited to symmetric morphologies. More advanced asymmetric models, such as the Gamma model, have been proposed to achieve improved accuracy. However, the physiological interpretation of the Gamma model is less effective than the Gaussian model, challenging the assessment of the clinical relevance of the outcomes. This paper aims to design an asymmetric PDA model with improved accuracy and effective physiological interpretability.

Approach: We implemented a novel PDA model called the Skewed-Gaussian model and tested it on 8000 PPG pulses from the MIMIC-III Waveform Database. The performances were compared with the reference Gamma-Gaussian model. Models' accuracies were assessed using the residual sum of squares, while Bland-Altman plots were used to evaluate biases. Lastly, the sensitivity and robustness of the models to the initial values' choice were evaluated using random initial values.

Main results: Our model achieved significantly higher accuracy than the reference model. The analysis with random initial values suggested that the model was less sensitive and consistently more robust. Finally, we highlighted the physiological interpretation of the model.

Significance: The proposed model may help to establish a link between alterations in cardiovascular functions and variations detectable in the PPG signal, as well as opening up new avenues for PPG-based remote patient monitoring.

In recent years, emotion recognition using Electroencephalogram (EEG) signals has garnered significant interest due to its non-invasive nature and high temporal resolution. We introduced a groundbreaking method that bypasses traditional manual feature engineering, emphasizing data preprocessing and leveraging the topological relationships between channels to transform EEG signals from two-dimensional time sequences into three-dimensional spatio-temporal representations. Maximizing the potential of deep learning, our approach provides a data-driven and robust method for identifying emotional states. Leveraging the synergy between Convolutional Neural Network (CNN) and attention mechanisms facilitated automatic feature extraction and dynamic learning of inter-channel dependencies. Our method showcased remarkable performance in emotion recognition tasks, confirming the effectiveness of our approach, achieving average accuracy of 98.62% for arousal and 98.47% for valence, surpassing previous state-of-the-art results of 95.76% and 95.15%. Furthermore, we conducted a series of pivotal experiments that broadened the scope of emotion recognition research, exploring further possibilities in the field of emotion recognition.

Objective.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.Approach.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 (n= 55) and Test (n= 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.Main Results.Amodel 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.Significance.Novel data segmentation methods via blocks of NXL and TN have improved our understanding of the relationship between sleep, OSA and vigilance.

Objective. 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.Approach. 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%.Main results. According to the correlation analysis, the resolved parametersA₁,W₂,C₂,W₃, andC₃were 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%.Significance. 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.

Objective.Sulfur hexafluoride (SF₆) 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 SF₆-washin can provide similar estimates. We aimed to assess comparability of primary SF₆-MBW outcomes between washin and washout phases of infant SF₆-MBW data measured with the WBreath (ndd Medizintechnik AG, Zurich, Switzerland) and Spiroware (Eco Medics AG, Duernten, Switzerland) MBW-setups, respectively.Approach.We assessed mean relative differences in lung clearance index (LCI) and functional residual capacity (FRC) between the washin and washout of existing SF₆-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.Main results.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.Significance.Outcomes of the washin and washout were not comparable due to an interplay of physiological and non-physiological factors, and cannot be used interchangeably.

Objective: 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.

Approach: ECG were recorded in 118 piglets (1-2 months old) during a model of asphyxia-associated VF cardiac arrest and CPR. An initial subset (91/118) was recorded using one device (Device 1), and the remaining piglets were recorded in the second device (Device 2). Raw AMSA and three ECG magnitude metrics were estimated to assess magnitude-related bias between devices. Five AMSA normalization approaches were assessed for their ability to remove detected bias and to classify defibrillation success.

Main results: Device 2 showed significantly lower ECG magnitude and raw AMSA compared to Device 1. CPR-based AMSA normalization approaches mitigated device-associated bias. Raw AMSA normalized by the average AMSA in the 1st minute of CPR (AMSA1m-cpr) exhibited the best sensitivity and specificity for classification of successful and unsuccessful defibrillation. While the optimal AMSA1m-cpr thresholds for balanced sensitivity and specificity were consistent across both devices, the optimal raw AMSA thresholds varied between the two devices. The area under the receiver operating characteristic curve for AMSA1m-cpr did not significantly differ from raw AMSA for both devices (Device 1: 0.74 vs. 0.88, P=0.14; Device 2: 0.56 vs. 0.59, P=0.81).

Significance: Unlike raw AMSA, AMSA1m-cpr demonstrated consistent results across different devices while maintaining predictive value for defibrillation success. This consistency has important implications for the widespread use of AMSA and the development of future guidelines on optimal AMSA thresholds for successful defibrillation. .

Objective: 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.

Approach: Linear Discriminant Analysis, Support Vector Machines, K-Nearest Neighbors and Random Forest were applied on ten HRV and PRV indices from time, frequency and information domains, selected with the two feature selection approaches. Data were collected from 127 healthy individuals during different stress conditions (rest, postural and mental stress).

Main results: Our results highlight that, while specific stress classification is feasible, distinguishing between postural and mental stress remains challenging. The used classifiers exhibited similar performance, with automatic Akaike Information Criterion-based feature selection proving overall better than the physiology-driven approach. Additionally, PRV-based features performed comparably to HRV-based ones, indicating their potential in outpatient monitoring using wearable devices.

Significance: The obtained findings help to determine the most relevant HRV/PRV features for stress classification, potentially useful to highlight different physiological mechanisms involved during both challenges accompanied by a shift in the sympathovagal balance. The proposed approach may have implications for advancing stress assessment methodologies in clinical settings and real-world contexts for well-being evaluation.

Objective. Pendelluft is the movement of air between lung regions, and electrical impedance tomography (EIT) has shown an ability to detect and monitor it.Approach.In this note, we propose a functional EIT measure which quantifies the reverse airflow seen in pendelluft: theFraction of Reverse Impedance Change(FRIC).MainResults. FRIC measures the fraction of reverse flow in each pixel waveform (as an image) or globally (as a single parameter).Significance. 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.