Physiological measurement最新文献

Objective. Start hesitation is a key issue for individuals with Parkinson's disease (PD) during gait initiation. Visual cues have proven effective in enhancing gait initiation. When applied to laser-light shoes, swing-limb detection efficiently activates the laser on the side of the stance limb, prompting the opposite swing limb to initiate stepping.Approach. This paper presents the development of two models for this purpose: a convolutional neural network that predicts the swing limb's side using center of pressure data, and a swing onset detection model based on sequential hypothesis test using foot pressure data.Main results. Our findings demonstrate an accuracy rate of 85.4% in predicting the swing limb's side, with 82.4% of swing onsets correctly detected within 0.05 s.Significance. This study demonstrates the efficiency of swing-limb detection based on foot pressures. Future research aims to comprehensively assess the impact of this method on improving gait initiation in individuals with PD.

Objective.Continuous monitoring of the hemodynamic coherence between macro and microcirculation is difficult at the bedside. We tested the role of photoplethysmography (PPG) to real-time assessment of microcirculation during extreme manipulation of macrohemodynamics induced by the cardiopulmonary bypass (CPB).Approach.We analyzed the alternating (AC) and direct (DC) components of the finger PPG in 12 patients undergoing cardiac surgery with CPB at five moments: (1) before-CPB; (2) CPB-start, at the transition from pulsatile to non-pulsatile blood flow; (3) CPB-aortic clamping, at a sudden decrease in pump blood flow and volemia.; (4) CPB-weaning, during step-wise 20% decreases in pump blood flow and opposite proportional increases in native pulsatile blood flow; and (5) after-CPB.Main results.Nine Caucasian men and three women were included for analysis. Macrohemodynamic changes during CPB had an immediate impact on the PPG at all studied moments. Before-CPB the AC signal amplitude showed a median and IQR values of 0.0023(0.0013). The AC signal completely disappeared at CPB-start and at CPB-aortic clamping. During CPB weaning its amplitude progressively increased but remained lower than before CPB, at 80% [0.0008 (0.0005);p< 0.001], 60% [0.0010(0.0006);p< 0.001], and 40% [0.0013(0.0009);p= 0.011] of CPB flow. The AC amplitude returned close to Before-CPB values at 20% of CPB flow [0.0015(0.0008);p= 0.081], when CPB was completely stopped [0.0019 (0.0009);p= 0.348], and at after-CPB [0.0021(0.0009);p= 0.687]. The DC signal Before-CPB [0.95(0.02)] did not differ statistically from CPB-start, CPB-weaning and After-CPB. However, at CPB-aortic clamping, at no flow and a sudden drop in volemia, the DC signal decreased from [0.96(0.01)] to [0.94(0.02);p= 0.002].Significance.The macrohemodynamic alterations brought on by CPB were consistent with changes in the finger's microcirculation. PPG described local pulsatile blood flow (AC) as well as non-pulsatile blood flow and volemia (DC) in the finger. These findings provide plausibility to the use of PPG in ongoing hemodynamic coherence monitoring.

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 one-dimensional viscoelastic representation of the arterial system, and is coupled with zero-dimensional 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.

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 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: Phonocardiography has recently gained popularity in low-cost and remote monitoring, including passive fetal heart monitoring. The development of methods which analyse phonocardiographic data tries to capitalize on this opportunity, and in recent years a multitude of such algorithms and models have been published. In these approaches there is little to no standardization and multiple parts of these models have to be reimplemented on a case-by-case basis. Datasets containing heart sound recordings also lack standardization in both data storage and labeling, especially in fetal phonocardiography.

Approach: We are presenting a toolbox that can serve as a basis for a future standard framework for heart sound analysis. This toolbox contains some of the most widely used processing steps and with these, complex analysis pipelines can be created. These functions can be tested individually.

Main results: Due to the interdependence of the steps, we validated the current segmentation stage using two phonocardiogram datasets, a fetal dataset comprising 50 one-minute abdominal PCG recordings, which include 6758 S1 and 6729 S2 labels and a filtered version of the dataset used in the 2022 PhysioNet Challenge, containing 413 records with 9795 S1 and 9761 S2 labels. Our results were compared to other common and publicly available segmentation methods, such as peak detection with the Neurokit2 library, and the Hidden Semi-Markov Model by Springer et al. Our best model achieved a 96.1% F1 score and 11.7 ms mean absolute error for fetal S1 detection, and 81.3% F1 score and 50.5 ms mean absolute error for PhysioNet S1 detection.

Significance: Our detection method outperformed all other tested methods on the fetal dataset and achieved results comparable to the state of the art on the PhysioNet dataset. Accurate segmentation of signals is critical for the calculation of accurate statistical measures and the creation of classification models. Our toolbox contains functions for both feature extraction and calculation of statistics which are compatible with the previous steps. All of our methods can be fine tuned for specific datasets. pyPCG is available on https://pypcg-toolbox.readthedocs.io/en/latest/.

Increasing temperatures pose new challenges for track workers (TWs), who endure prolonged exposure to extreme heat and humidity. New methods are critically needed to assess their performance and heat tolerance, aiming to mitigate workplace accidents and long-term health consequences. This study aimed to investigate the physiological effects of heat exposure on TWs, using wearable sensors to monitor key physiological parameters under controlled environmental conditions. Nineteen TWs participated in the study, which included two experimental sessions simulating different thermal environments: a typical Swiss summer night and a hot summer day. Participants' core body temperature, heart rate (HR), and skin temperature were monitored using wearable sensors, and physiological indexes were computed. In addition, perceptual strain index (PeSI) and psychomotor vigilance task (PVT) response times were recorded. Statistically significant increases in physiological parameters were observed under hotter conditions. The study identified statistically significant correlations between the PeSI and the physiological strain index and between PeSI and HR. Perceptual scores were consistently higher than the values derived from physiological measurements, suggesting a greater subjective experience of heat strain. The PVT response times were higher on the hotter day, reflecting increased cognitive strain due to heat exposure. The study highlights the critical impact of heat stress on TWs, with statistically significant increases in physiological and cognitive strain under higher temperatures. Future research should focus on real-world applications of heat strain monitoring.

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.

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 (AMSA_1m-cpr) exhibited the best sensitivity and specificity for classification of successful and unsuccessful defibrillation. While the optimal AMSA_1m-cprthresholds 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 AMSA_1m-cprdid 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, AMSA_1m-cprdemonstrated 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.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.