Biocybernetics and Biomedical Engineering最新文献

Most real-world systems are characterised by dynamics and correlations emerging at multiple time scales, and are therefore referred to as complex systems. In this work, the complexity of time series produced by complex systems was investigated in the frame of information theory computing the entropy rate via the conditional entropy (CE) measure. A comparative investigation of several CE estimators, based on linear parametric and non-linear model-free representations of the process dynamics, was performed considering simulated linear autoregressive (AR) and mixed non-linear deterministic and linear stochastic dynamics processes, as well as physiological time series reflecting short-term cardiorespiratory dynamics. In simulations, the estimated CE values decreased when reducing the system complexity through an increase in the pole radius of the AR process or with the predominance of the deterministic behaviour in the mixed dynamics. In the application to cardiorespiratory dynamics, a reduction in physiological complexity was observed resulting from a regularization of the time series of heart rate and respiratory volume when decreasing the breathing rate. Our results evidence how simple and fast approaches based on linear parametric or permutation-based model-free estimators allow efficient discrimination of complexity changes in the short-term evolution of complex dynamic systems. However, in the presence of non-linear dynamics, the superiority of the more general but computationally expensive nearest-neighbour method is highlighted. These findings have implications for the assessment of complex dynamics both in clinical settings and in physiological monitoring.

Bypass surgery is a commonly employed method for treating coronary artery diseases, involving the use of grafts to bypass occluded arteries. However, graft occlusion remains a concern due to mechanical disparities between the grafts and native arteries. This study aims to compare the mechanical properties of three frequently used grafts in coronary bypass surgeries: human saphenous veins, mammary arteries, and radial arteries. Stress-relaxation tests were conducted on samples obtained from these vessels, and their mechanical properties were characterized. The stress–strain curves of each sample were fitted using the quasi-linear viscoelastic (QLV) model, with MATLAB software used to extract the model's constants. Additionally, fluid–structure simulations were performed employing the extracted viscoelastic mechanical properties of the vessels. The analysis revealed that the saphenous vein exhibited the highest elastic coefficient (0.5247) and non-linearity coefficient (0.8135) among the studied grafts. The mammary artery demonstrated nearly seven times greater viscoelasticity compared to the other graft options. Furthermore, the examination of shear stress distribution indicated lower shear stress regions in the radial and mammary artery specimens compared to the saphenous specimens. Notably, the lower wall of the host artery exhibited the greatest oscillatory shear index (OSI), with the radial specimen displaying the highest oscillation in this region compared to the other two specimens. The mechanical characterization results presented in this study hold potential applications in pathogenic and clinical investigations of heart diseases, aiding in the development of appropriate treatment approaches.

Objective

Breathing elevated oxygen partial pressures (PO₂) prior to SCUBA diving increases the risk of developing central nervous system oxygen toxicity (CNS-OT), which could impair performance or result in seizure and subsequent drowning. We aimed to study the dynamics of electrodermal activity (EDA) while breathing elevated PO₂ in the hyperbaric environment (HBO₂) as a possible means to predict impending CNS-OT. To this end, we used machine learning to automatically detect and predict the onset of symptoms associated with CNS-OT in humans by using features derived from EDA in both time and frequency domains.

Methods

We collected electrodermal activity (EDA) data from forty-nine exposures to HBO₂ while subjects were undergoing cognitive load and exercise in a hyperbaric oxygen chamber. Four independent experts were present during the experiment to monitor and classify any symptoms associated with hyperbaric oxygen toxicity. We computed a highly sensitive time varying spectral EDA index, named TVSymp, and extracted informative features from skin conductance responses (SCRs). Machine learning algorithms were trained and validated for classifying features from SCRs and TVSymp as CNS-OT related or non-CNS-OT related. Machine learning models were validated using a subject-independent leave one subject out (LOSO) validation scheme.

Results

Our machine learning model was able to classify EDA dynamics related to CNS-OT with 100 % sensitivity and 84 % specificity via LOSO validation. Moreover, the median prediction time for CNS-OT symptoms was ∼ 250 s preceding the occurrence of actual symptoms.

Significance

This study shows that EDA can potentially be used for early prediction of CNS-OT in divers with a high sensitivity and sufficient prediction time for countermeasures. While the study results are promising, independent validation datasets are warranted to confirm the findings. However, the current results are well corroborated in an animal study, which consistently showed seizure prediction time of 2 min prior to seizure.

In this paper, an adaptive controller is designed to regulate the blood glucose level of type 1 diabetes mellitus while not all states of the system are measurable and also its parameters are unknown. The main goal in the control of diabetes is to preserve blood glucose level within a safe rang by a suitable injecting insulin rate to the patient. Herein, it is achieved by measuring the blood glucose level and proposed an observer based adaptive control system. In the proposed method, firstly, the dynamic equations of nonlinear Bergman minimal model (BMM) are transformed into a companion form. Then an adaptive observer is presented to simultaneously estimate the state variables and the system’s parameters. Afterward, based on the designed observer and using a new meal simulation model, an adaptive control is presented to bring back the blood glucose level to its safe range. The overall stability of the developed adaptive control is established using the Lyapunov direct method. Simulation results have been performed to verify the effectiveness of the proposed approach in tracking the desired blood glucose.

Thrombosis is a major and serious complication in patients with artificial heart pump assist device (HPAD). There is an urgent need for an efficient and safe method to solve the clinical challenge. We have developed a new type of ultrasound integrated heart pump assist device (uHPAD) with a pair of ultrasonic transducer rings installed around the pump. Based on the in-vitro experiments, the sonothrombolysis protocol was determined. Then, in-vivo experiments were performed on sheep to evaluate the efficacy and safety of the novel uHPAD. It is found that the ultrasound assisted thrombolysis with the drug-loaded microbubbles can accelerate the dissolution of the thrombus in the pump, while have no significant negative effect on blood cell components, coagulation-hemolysis system, and the structure and function of main organs. The ultrasound assisted thrombolysis is demonstrated to be a promising method to solve the clinical problem of thrombosis in the HPAD.

Synergies were demonstrated to exist in the kinematic, force and muscular domains, and their task-specificity and subject-specificity was also highlighted in literature. Despite that, no works have extracted synergies on specific grasp classes to analyze task-specific synergistic patterns. Moreover, only few studies focused on the combined analysis of kinematic, force and muscle synergies.

The aim of this work was to (i) identify the grasp classes on which to extract task-specific synergies; (ii) extract subject-specific and task-specific synergies in the three domains and (iii) calculate the similarity of the extracted synergies among subjects and define average generalized synergies.

8 subjects were recruited to perform 21 reach-to-grasp tasks and the kinematics, contact forces and muscular activation of the hand were acquired. A LDA classifier allowed distinguishing power and precision grasp classes with an average accuracy of 89% considering kinematic data alone and combined kinematic, muscle and force data. Subject and task-specific synergies were therefore extracted on these two classes. Kinematic and force synergies were distinctive for the two classes, and highly similar among subjects, thus suggesting the possibility of adopting generalized synergies to describe grasp strategies. Conversely, muscle synergies did not differ particularly for the two classes. The combined analysis of force and kinematic data suggested that the hand posture may be somehow modulated by the optimal distribution of contact forces to perform stable grasps. Simulations with a virtual hand confirmed that stability significantly increased when grasps were generated by activating combined kinematic and force synergies rather than kinematic synergies only.

Independent component analysis (ICA) is widely used to separate maternal and fetal electrocardiograms. However, it has become less effective due to the efforts to reduce the number of recording electrodes. To address this issue, we propose an extension of ICA that can extract the fetal electrocardiogram from only two maternal abdominal electric signals. We solve this problem by increasing the dimension of the observed signals using the method of delays, followed by spatio-spectral filtering to separate the source signals. By iteratively applying this approach, we can extract signals that are not separable using the original observations alone. These signals are then clustered to create signal subspaces corresponding to different sources, allowing for a rough reconstruction of signal components produced by these sources. This initial decomposition can then be refined by using the reconstructed components as new observations, extending the original ones, and applying ICA to this extended signal representation.

Applied to two-channel maternal abdominal signals, the proposed method was able to extract 3 source signals (two maternal and one fetal), resulting in the achievement of the goal of over-complete blind source separation (BSS). Furthermore, the method enabled the successful detection of fetal QRS (fQRS) complexes in experiments on two datasets of real-world maternal abdominal signals. For the ADFECGDB dataset, the method reached the sensitivity, positive predictivity, and F1 score of 100%, 99.97%, and 99.98%, respectively, outperforming all reference methods. For the PREGNANCY dataset, the corresponding values were 98.95%, 98.92%, and 98.93%, second only to one reference method.

The mechanical overloading of temporomandibular joint (TMJ) is generally linked to temporomandibular disorders (TMD). However, in patients with a typical combination of maxillofacial morphology and occlusal features, the reduction of joint load and treatment with general occlusal splints are often ineffective. This study investigates the biomechanical behavior of the stomatognathic system in a TMD patient with personalized splints by finite element analysis. The therapeutic position, determined based on the intercuspal position, served as the basis for designing personalized customized splints. The design of occlusal contact and splint structure was evaluated in terms of their impact on the maximum stress level in the TMJ and the biting forces on the dentition. The relationship between joint stress and biting force was further examined during treatment with different customized splints. In preoperative case, there was a significant increase in stress level and stress concentration in the medial to posterior band of the articular disc. However, in all customized splint cases, the highest stress area shifted to the intermediate zone and exhibited a decrease. Notably, the bi-splints demonstrated superior ability in relieving overloading and balancing the occlusal force on both sides of the dentition, as verified by clinical treatment. The predictable simulated results offer valuable interactive information regarding TMJ overload, aiding doctors in making better-informed clinical decisions in future.

Analyzing electroencephalographic signals (EEG) could provide valuable information about functional neural activity (FNA) during human motion. The hypothesis of this work is twofold: spatial patterns emerge in EEG signals from functional connectivity (FC) analysis during lower limb movements, and the spatial patterns are mosto robust in some frequency bands than in others. Accordingly, a set of human subjects without neuromotor pathologies participated in an experimental trial where EEG signals were recorded during lower limb movements. The FC was studied with coherence analysis (in $\delta$, $\theta$, and $\alpha$) and graph theory was proposed to study the characteristics of spatial dynamics by means a set of metrics (degree, maximum connection, and closeness centrality) and two distances (Hamming distance and Jaccard). Finally, a statistical study of the metrics by frequency band was performed to analyze the significant differences between the phases of each stage and movement, considering the proposed metrics. The results of the study indicated that the frequency bands that showed greater statistical significance in the analysis were $\delta$, $\theta$, and $\alpha$ and that the major differences in graph dynamics were shown in degree, maximum connection, and closeness centrality in $\alpha$ band. Present findings portray leading underlying neural networks, implying that discernible spatial patterns exist in FNA during lower limb movements, and such patterns can be characterized with the proposed methodology.

Spatio-Temporal Optical Coherence Tomography (STOC-T) is a novel imaging technique using light with controlled spatial and temporal coherence. Retinal images obtained using the STOC-T system maintain high resolution in all three dimensions, on a sample of about 700 μm, without the need for mechanical scanning. In the present work, we use known data processing algorithms for optical coherence tomography angiography (OCTA) and modify them to improve the rendering of the vasculature in the human retina at different depths by introducing the angio STOC-T method. The algorithms are primarily sensitive to the strong signal phase variance corresponding to the appearance of a wide Doppler band in STOC-T signals obtained for millisecond exposure times. After using STOC-T angiography, we can render high contrast images of the choroid.