Physiological measurement最新文献

Objective.In the future, thoracic electrical impedance tomography (EIT) monitoring may include continuous and simultaneous tracking of both breathing and heart activity. However, an effective way to decompose an EIT image stream into physiological processes as ventilation-related and cardiac-related signals is missing.Approach.This study analyses the potential ofMulti-dimensional Ensemble Empirical Mode Decompositionby application of theComplete Ensemble Empirical Mode Decomposition with Adaptive Noiseand a novel frequency-based combination criterion for detrending, denoising and source separation of EIT image streams, collected from nine healthy male test subjects with similar age and constitution.Main results.In this paper, a novel approach to estimate the lung, the heart and the perfused regions of an EIT image is proposed, which is based on theRoot Mean Square Errorbetween the index of maximal respiratory and cardiac variation to their surroundings. The summation of the indexes of the respective regions reveals physiologically meaningful time signals, separated into the physiological bandwidths of ventilation and heart activity at rest. Moreover, the respective regions were compared with the relative thorax movement and photoplethysmogram (PPG) signal. In linear regression analysis and in the Bland-Altman plot, the beat-to-beat time course of both the ventilation-related signal and the cardiac-related signal showed a high similarity with the respective reference signal.Significance.Analysis of the data reveals a fair separation of ventilatory and cardiac activity realizing the aimed source separation, with optional detrending and denoising. For all performed analyses, a feasible correlation of 0.587 to 0.905 was found between the cardiac-related signal and the PPG signal.

Objective.Cardiac Index (CI) is a key physiologic parameter to ensure end organ perfusion in the pediatric intensive care unit (PICU). Determination of CI requires invasive cardiac measurements and is not routinely done at the PICU bedside. To date, there is no gold standard non-invasive means to determine CI. This study aims to use a novel non-invasive methodology, based on routine continuous physiologic data, called Pulse Arrival Time (PAT) as a surrogate for CI in patients with normal Ejection Fraction (EF).Approach.Electrocardiogram (ECG) and photoplethysmogram (PPG) signals were collected from beside monitors at a sampling frequency of 250 samples per second. Continuous PAT, derived from the ECG and PPG waveforms was averaged per patient. Pearson's correlation coefficient was calculated between PAT and CI, PAT and heart rate (HR), and PAT and EF.Main Results.Twenty patients underwent right heart cardiac catheterization. The mean age of patients was 11.7 ± 5.4 years old, ranging from 11 months old to 19 years old, the median age was 13.4 years old. HR in this cohort was 93.8 ± 17.0 beats per minute. The average EF was 54.4 ± 9.6%. The average CI was 3.51 ± 0.72 l min^-1m^-2, with ranging from 2.6 to 4.77 l min^-1m^-2. The average PAT was 0.31 ± 0.12 s. Pearson correlation analysis showed a positive correlation between PAT and CI (0.57,p< 0.01). Pearson correlation between HR and CI, and correlation between EF and CI was 0.22 (p= 0.35) and 0.03 (p= 0.23) respectively. The correlation between PAT, when indexed by HR (i.e. PAT × HR), and CI minimally improved to 0.58 (p< 0.01).Significance.This pilot study demonstrates that PAT may serve as a valuable surrogate marker for CI at the bedside, as a non-invasive and continuous modality in the PICU. The use of PAT in clinical practice remains to be thoroughly investigated.

Objective.Even though the electrocardiogram (ECG) has potential to be used as a monitoring or diagnostic tool for fetuses, the use of non-invasive fetal ECG is complicated by relatively high amounts of noise and fetal movement during the measurement. Moreover, machine learning-based solutions to this problem struggle with the lack of clean reference data, which is difficult to obtain. To solve these problems, this work aims to incorporate fetal rotation correction with ECG denoising into a single unsupervised end-to-end trainable method.Approach.This method uses the vectorcardiogram (VCG), a three-dimensional representation of the ECG, as an input and extends the previously introduced Kalman-LISTA method with a Kalman filter for the estimation of fetal rotation, applying denoising to the rotation-corrected VCG.Main results.The resulting method was shown to outperform denoising auto-encoders by more than 3 dB while achieving a rotation tracking error of less than 33^∘. Furthermore, the method was shown to be robust to a difference in signal to noise ratio between electrocardiographic leads and different rotational velocities.Significance.This work presents a novel method for the denoising of non-invasive abdominal fetal ECG, which may be trained unsupervised and simultaneously incorporates fetal rotation correction. This method might prove clinically valuable due the denoised fetal ECG, but also due to the method's objective measure for fetal rotation, which in turn might have potential for early detection of fetal complications.

Objective.To determine the optimal frequency and site of stimulation for transcutaneous vagus nerve stimulation (tVNS) to induce acute changes in the autonomic profile (heart rate (HR), heart rate variability (HRV)) in healthy subjects (HS) and patients with heart failure (HF).Approach.We designed three single-blind, randomized, cross-over studies: (1) to compare the acute effect of left tVNS at 25 Hz and 10 Hz (n= 29, age 60 ± 7 years), (2) to compare the acute effect of left and right tVNS at the best frequency identified in study 1 (n= 28 age 61 ± 7 years), and (3) to compare the acute effect of the identified optimal stimulation protocol with sham stimulation in HS and HF patients (n= 30, age 59 ± 5 years, andn= 32, age 63 ± 7 years, respectively).Main results.In study 1, left tragus stimulation at 25 Hz was more effective than stimulation at 10 Hz in decreasing HR (-1.0 ± 1.2 bpm,p< 0.001 and -0.5 ± 1.6 bpm, respectively) and inducing vagal effects (significant increase in RMSSD, and HF power). In study 2, the HR reduction was greater with left than right tragus stimulation (-0.9 ± 1.5 bpm,p< 0.01 and -0.3 ± 1.4 bpm, respectively). In study 3 in HS, left tVNS at 25 Hz significantly reduced HR, whereas sham stimulation did not (-1.1 ± 1.2 bpm,p< 0.01 and -0.2 ± 2.9 bpm, respectively). In HF patients, both active and sham stimulation produced negligible effects.Significance.Left tVNS at 25 Hz is effective in acute modulation of cardiovascular autonomic control (HR, HRV) in HS but not in HF patients (NCT05789147).

Objective.The cerebral vasculature is formed of an intricate network of blood vessels over many different length scales. Changes in their structure and connection are implicated in multiple cerebrovascular and neurological disorders. In this study, we present a novel approach to the quantitative analysis of the cerebral macrovasculature using computational and mathematical tools in a large dataset.Approach.We analysed a publicly available vessel dataset from a cohort of 56 (32/24F/M) healthy subjects. This dataset includes digital reconstructions of human brain macrovasculatures. We then propose a new mathematical model to compute blood flow dynamics and pressure distributions within these 56-representative cerebral macrovasculatures and quantify the results across this cohort.Main results.Statistical analysis showed that the steady state level of cerebrovascular resistance (CVR) gradually increases with age in both men and women. These age-related changes in CVR are in good agreement with previously reported values. All subjects were found to have only small phase angles (<6°) between blood pressure and blood flow at the cardiac frequency.Significance.These results showed that the dynamic component of blood flow adds very little phase shift at the cardiac frequency, which implies that the cerebral macrocirculation can be regarded as close to steady state in its behaviour, at least in healthy populations, irrespective of age or sex. This implies that the phase shift observed in measurements of blood flow in cerebral vessels is caused by behaviour further down the vascular bed. This behaviour is important for future statistical models of the dynamic maintenance of oxygen and nutrient supply to the brain.

Objective.Electrical impedance tomography (EIT) has been used to determine regional lung ventilation distribution in humans for decades, however, the effect of biological sex on the findings has hardly ever been examined. The aim of our study was to determine if the spatial distribution of ventilation assessed by EIT during quiet breathing was influenced by biological sex.Approach.219 adults with no known acute or chronic lung disease were examined in sitting position with the EIT electrodes placed around the lower chest (6th intercostal space). EIT data were recorded at 33 images/s during quiet breathing for 60 s. Regional tidal impedance variation was calculated in all EIT image pixels and the spatial distribution of the values was determined using the established EIT measures of centre of ventilation in ventrodorsal (CoV_vd) and right-to-left direction (CoV_rl), the dorsal and right fraction of ventilation, and ventilation defect score.Main results.After exclusion of one subject due to insufficient electrode contact, 218 data sets were analysed (120 men, 98 women) (age: 53 ± 18 vs 50 ± 16 yr (p= 0.2607), body mass index: 26.4 ± 4.0 vs 26.4 ± 6.6 kg m^-2(p= 0.9158), mean ± SD). Highly significant differences in ventilation distribution were identified between men and women between the right and left chest sides (CoV_rl: 47.0 ± 2.9 vs 48.8 ± 3.3% of chest diameter (p< 0.0001), right fraction of ventilation: 0.573 ± 0.067 vs 0.539 ± 0.071 (p= 0.0004)) and less significant in the ventrodorsal direction (CoV_vd: 55.6 ± 4.2 vs 54.5 ± 3.6% of chest diameter (p= 0.0364), dorsal fraction of ventilation: 0.650 ± 0.121 vs 0.625 ± 0.104 (p= 0.1155)). Ventilation defect score higher than one was found in 42.5% of men but only in 16.6% of women.Significance.Biological sex needs to be considered when EIT findings acquired in upright subjects in a rather caudal examination plane are interpreted. Sex differences in chest anatomy and thoracoabdominal mechanics may explain the results.

Objective. Physiological signals based emotion recognition is a prominent research domain in the field of human-computer interaction. Previous studies predominantly focused on unimodal data, giving limited attention to the interplay among multiple modalities. Within the scope of multimodal emotion recognition, integrating the information from diverse modalities and leveraging the complementary information are the two essential issues to obtain the robust representations.Approach. Thus, we propose a intermediate fusion strategy for combining low-rank tensor fusion with the cross-modal attention to enhance the fusion of electroencephalogram, electrooculogram, electromyography, and galvanic skin response. Firstly, handcrafted features from distinct modalities are individually fed to corresponding feature extractors to obtain latent features. Subsequently, low-rank tensor is fused to integrate the information by the modality interaction representation. Finally, a cross-modal attention module is employed to explore the potential relationships between the distinct latent features and modality interaction representation, and recalibrate the weights of different modalities. And the resultant representation is adopted for emotion recognition.Main results. Furthermore, to validate the effectiveness of the proposed method, we execute subject-independent experiments within the DEAP dataset. The proposed method has achieved the accuracies of 73.82% and 74.55% for valence and arousal classification.Significance. The results of extensive experiments verify the outstanding performance of the proposed method.

Objective.This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning method, the focus is on accurately identifying minority classes in conditions characterized by significant imbalances in ECG data.Approach.We propose a feature fusion neural network enhanced by a dynamic minority-biased batch weighting loss function. This network comprises three specialized branches: the complete ECG data branch for a comprehensive view of ECG signals, the local QRS wave branch for detailed features of the QRS complex, and theRwave information branch to analyzeRwave characteristics. This structure is designed to extract diverse aspects of ECG data. The dynamic loss function prioritizes minority classes while maintaining the recognition of majority classes, adjusting the network's learning focus without altering the original data distribution. Together, this fusion structure and adaptive loss function significantly improve the network's ability to distinguish between various heartbeat classes, enhancing the accuracy of minority class identification.Main results.The proposed method demonstrated balanced performance within the MIT-BIH dataset, especially for minority classes. Under the intra-patient paradigm, the accuracy, sensitivity, specificity, and positive predictive value for Supraventricular ectopic beat were 99.63%, 93.62%, 99.81%, and 92.98%, respectively, and for Fusion beat were 99.76%, 85.56%, 99.87%, and 84.16%, respectively. Under the inter-patient paradigm, these metrics were 96.56%, 89.16%, 96.84%, and 51.99%for Supraventricular ectopic beat, and 96.10%, 77.06%, 96.25%, and 13.92%for Fusion beat, respectively.Significance.This method effectively addresses the class imbalance in ECG datasets. By leveraging diverse ECG signal information and a novel loss function, this approach offers a promising tool for aiding in the diagnosis and treatment of cardiac conditions.

Objective. This study examines the value of ventricular repolarization using QT dynamicity for two different types of atrial fibrillation (AF) prediction.Approach. We studied the importance of QT-dynamicity (1) in the detection and (2) the onset prediction (i.e. forecasting) of paroxysmal AF episodes using gradient-boosted decision trees (GBDT), an interpretable machine learning technique. We labeled 176 paroxysmal AF onsets from 88 patients in our unselected Holter recordings database containing paroxysmal AF episodes. Raw ECG signals were delineated using a wavelet-based signal processing technique. A total of 44 ECG features related to interval and wave durations and amplitude were selected and the GBDT model was trained with a Bayesian hyperparameters selection for various windows. The dataset was split into two parts at the patient level, meaning that the recordings from each patient were only present in either the train or test set, but not both. We used 80% on the database for the training and the remaining 20% for the test of the trained model. The model was evaluated using 5-fold cross-validation.Main results.The mean age of the patients was 75.9 ± 11.9 (range 50-99), the number of episodes per patient was 2.3 ± 2.2 (range 1-11), and CHA2DS2-VASc score was 2.9 ± 1.7 (range 1-9). For the detection of AF, we obtained an area under the receiver operating curve (AUROC) of 0.99 (CI 95% 0.98-0.99) and an accuracy of 95% using a 30 s window. Features related to RR intervals were the most influential, followed by those on QT intervals. For the AF onset forecast, we obtained an AUROC of 0.739 (0.712-0.766) and an accuracy of 74% using a 120s window. R wave amplitude and QT dynamicity as assessed by Spearman's correlation of the QT-RR slope were the best predictors.Significance. The QT dynamicity can be used to accurately predict the onset of AF episodes. Ventricular repolarization, as assessed by QT dynamicity, adds information that allows for better short time prediction of AF onset, compared to relying only on RR intervals and heart rate variability. Communication between the ventricles and atria is mediated by the autonomic nervous system (ANS). The variations in intraventricular conduction and ventricular repolarization changes resulting from the influence of the ANS play a role in the initiation of AF.

Objective.Continuous monitoring of cerebrospinal compliance (CC)/cerebrospinal compensatory reserve (CCR) is crucial for timely interventions and preventing more substantial deterioration in the context of acute neural injury, as it enables the early detection of abnormalities in intracranial pressure (ICP). However, to date, the literature on continuous CC/CCR monitoring is scattered and occasionally challenging to consolidate.Approach.We subsequently conducted a systematic scoping review of the human literature to highlight the available continuous CC/CCR monitoring methods.Main results.This systematic review incorporated a total number of 76 studies, covering diverse patient types and focusing on three primary continuous CC or CCR monitoring metrics and methods-Moving Pearson's correlation between ICP pulse amplitude waveform and ICP, referred to as RAP, the Spiegelberg Compliance Monitor, changes in cerebral blood flow velocity with respect to the alternation of ICP measured through transcranial doppler (TCD), changes in centroid metric, high frequency centroid (HFC) or higher harmonics centroid (HHC), and the P2/P1 ratio which are the distinct peaks of ICP pulse wave. The majority of the studies in this review encompassed RAP metric analysis (n= 43), followed by Spiegelberg Compliance Monitor (n= 11), TCD studies (n= 9), studies on the HFC/HHC (n= 5), and studies on the P2/P1 ratio studies (n= 6). These studies predominantly involved acute traumatic neural injury (i.e. Traumatic Brain Injury) patients and those with hydrocephalus. RAP is the most extensively studied of the five focused methods and exhibits diverse applications. However, most papers lack clarification on its clinical applicability, a circumstance that is similarly observed for the other methods.Significance.Future directions involve exploring RAP patterns and identifying characteristics and artifacts, investigating neuroimaging correlations with continuous CC/CCR and integrating machine learning, holding promise for simplifying CC/CCR determination. These approaches should aim to enhance the precision and accuracy of the metric, making it applicable in clinical practice.