Biocybernetics and Biomedical Engineering最新文献

Cerebral hyperperfusion occurs in some patients after superficial temporal artery–middle cerebral artery bypass surgery. However, there is uncertainty about cerebral hyperperfusion after bypass for patients with different Circle of Willis (CoW) structures.

This study established a lumped parameter model coupled with one–dimensional model (0–1D), whilst a deep learning model for predicting pressure drop (DLM–PD) caused by stenosis and a cerebral autoregulation model (CAM) were introduced into the model. Based on this model, 9 CoW structural models before and after bypass was constructed, to investigate the effects of different CoW structures on cerebral hyperperfusion after bypass. The model and the results were further verified by clinical data.

The MSE of mean flow rates from 0–1D model calculation and from clinically measurement was 1.4%. The patients exhibited hyperperfusion in three CoW structures after bypass: missing right anterior segment of anterior cerebral artery (mRACA1) (13.96% hyperperfusion), mRACA1 and foetal-type right anterior segment of posterior cerebral artery (12.81%), and missing anterior communicating artery and missing left posterior communicating artery (112.41%). The error between the average flow ratio from the model calculations and fromclinical measurement was less than 5%.

This study demonstrated that the CoW structure had a significant impact on hyperperfusion after bypass. The general 0–1D model coupled with DLM–PD and CAM proposed in this study, could accurately simulate the hemodynamic environment of different CoW structures before and after bypass, which might help physicians identify high–risk patients with hyperperfusion before surgery, and promote the development of non-invasive diagnosis and treatment of cerebrovascular diseases.

Objective

To develop a novel algorithm for tracking acute mental stress which can infer acute mental stress state from multi-modal digital signatures of physiological parameters compatible with wearable-enabled sensing.

Methods

We derived prominent digital signatures of physiological responses to mental stress using cross-integration of multi-modal physiological signals including the electrocardiogram (ECG), photoplethysmogram (PPG), seismocardiogram (SCG), ballistocardiogram (BCG), electrodermal activity (EDA), and respiratory effort. Then, we developed an algorithm for tracking acute mental stress that can continuously classify stress vs no stress states by computing an aggregated likelihood computed with respect to a priori probability density distributions associated with the digital signatures of mental stress under stress and no stress states.

Results

Our algorithm could adequately infer mental stress state (average classification accuracy: 0.85, sensitivity: 0.85, specificity: 0.86) using a small number of prominent digital signatures derived from cross-integration of multi-modal physiological signals. The digital signatures in our work significantly outperformed the digital signatures employed in the state-of-the-art in tracking acute mental stress. Its exploitation of collective inference allowed for improved inference of mental stress state relative to naïve data mining techniques.

Conclusion

Our algorithm for tracking acute mental stress has the potential to make a leap in continuous, high-accuracy, and high-confidence inference of mental stress via convenient wearable-enabled physiological sensing. Significance: The ability to continuously monitor and track mental stress can collectively improve human wellbeing.

Objectives

Vascular depression hypothesis (VDH) bases on co-occurrence of vascular and mental dysfunctions in advanced age; however, there may be still a controversy about whether there is some direct association between vascular and mental properties or the co-occurrence is only a statistical artifact caused by commonness of these dysfunctions in the elderly. COVID-19 gave opportunity to test VDH under conditions different from aging.

Methods

25 patients were examined 3–6 month after SARS-CoV-2 infection. Subjective worsening of mental functions, presumably caused by the disease, was quantified with three psychometric tests. Blood flow waveforms were obtained for the left brachial and common carotid arteries. The waveform shape changes continuously with age; therefore, an individual shape can be characterized by the index WA being the calendar age (CA) of the average healthy rested subject having the most similar shape (consequently, in healthy rested subjects WA-CA = 0, in average). The mathematical functional analysis was used to calculate WA.

Results

Brachial WA-CA = 13 yrs, in average (p < 0.00005; Cohen’s d = 0.99), and was correlated with tests scores (r = 0.55, 0.65, 0.46). Mean carotid WA-CA were smaller (7.2 and 1.6) but they were also correlated with the scores (right: r = 0.44, 0.55, 0.32; left: r = 0.49, 0.51, 0.38). Scores of two tests were inversely correlated with the systolic (r = -0.54, −0.58) and diastolic (r = -0.46, −0.56) pressures.

Conclusions

Since neither vascular nor mental problems are common after COVID-19, these relatively high correlations indicate that vascular and mental properties are not independent, i.e., they support VDH. Note that this not only concerns cerebral vasculature.

This paper explores the parallel collaboration of multimodal physiological signals, combining eye tracker output signals, motor imagery, and error-related potentials to control a computer mouse. Specifically, a parallel working mechanism is implemented in the decision layer, where the eye tracker manages cursor movements, and motor imagery manages click functions. Meanwhile, the eye tracker output signals are integrated with electroencephalography data to detect the idle state for asynchronous control. Additionally, error-related potentials evoked by visual feedback, are detected to reduce the cost of error corrections. To efficiently collect data and provide continuous evaluations, we performed offline training and online testing in the designed paradigm. To further validate the practicability, we conducted online experiments on the real-world computer, focusing on a scenario of opening and closing files. The experiments involved seventeen subjects. The results showed that the stability of the eye tracker was optimized from 67.6% to 95.2% by the designed filter, providing the support for parallel control. The accuracy of motor imagery conducted simultaneously with fixations reached 93.41 ± 2.91%, proving the feasibility of parallel control. Furthermore, the real-world experiments took 45.86 ± 14.94 s to complete three movements and clicks, and showed a significant improvement compared to the baseline experiment without automatic error correction, validating the practicability of the system and the efficacy of error-related potentials detection. Moreover, this system freed users from the stimulus paradigm, enabling a more natural interaction. To sum up, the parallel collaboration of multimodal physiological signals is novel and feasible, the designed mouse is practical and promising.

For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual’s specific needs. The authors propose a novel approach for the integration of a neural architecture search framework with deep reinforcement learning to autonomously generate and train architectures, optimized for each subject over model size and analytical prediction performance, for the blood glucose prediction task in individuals with Type-1 diabetes. The authors evaluate the proposed approach on the OhioT1DM dataset, which includes blood glucose monitoring records at 5-min intervals over 8 weeks for 12 patients with Type-1 diabetes mellitus. Prior work focused on predicting blood glucose levels in 30 and 45-min prediction horizons, equivalent to 6 and 9 data points, respectively. Compared to the previously achieved best error, the proposed method demonstrates improvements of 18.4 % and 22.5 % on average for mean absolute error in the 30-min and 45-min prediction horizons, respectively, through the proposed deep reinforcement learning framework. Using the deep reinforcement learning framework, the best-case and worst-case analytical performance measured over root mean square error and mean absolute error was obtained for subject ID 570 and subject ID 584, respectively. Models optimized for performance on the prediction task and model size were obtained after implementing neural architecture search in conjunction with deep reinforcement learning on these two extreme cases. The authors demonstrate improvements of 4.8 % using Long Short Term Memory-based architectures and 5.7 % with Gated Recurrent Units-based architectures for patient ID 570 on the analytical prediction performance by integrating neural architecture search with deep reinforcement learning framework. The patient with the lowest performance (ID 584) on the deep reinforcement learning method had an even greater performance boost, with improvements of 10.0 % and 12.6 % observed for the Long Short-Term Memory and Gated Recurrent Units, respectively. The subject-specific optimized models over performance and model size from the neural architecture search in conjunction with deep reinforcement learning had a reduction in model size which ranged from 20 to 150 times compared to the model obtained using only the deep reinforcement learning method. The smaller size, indicating a reduction in model complexity in terms of the number of trainable network parameters, was achieved without a loss in the prediction performance.

Background and motivation:

The application of artificial intelligence in medical research, particularly unsupervised learning techniques, has shown promising potential. Medical time series data poses a unique challenge for analysis due to its complexity. Existing unsupervised learning methods often fail to effectively classify these variations, highlighting a gap in current approaches. We introduce a methodological clustering classification framework designed to accurately handle such data, aiming for improved classification tasks in biomedical signals.

Methods:

To address these challenges, we introduce a novel approach for the analysis and classification of medical time series data. Our method integrates agglomerative hierarchical clustering with Hilbert vector space representations of medical signals and biological sequences. We rigorously define the mathematical principles and conduct evaluations using simulations of cardiac signals, real-world neural signal datasets, open-source protein sequences, and the MNIST dataset for illustrative purposes.

Results:

The proposed method exhibited a 96% success rate in classifying protein sequences by function and effectively identifying families within a large protein set. In cardiac signal analysis, it retained 0.996 variance in a condensed 6-dimensional space, accurately classifying 87.4% of simulated atrial flutter groups and 99.91% of main groups when excluding conduction direction. For neural signals, it demonstrated near-perfect tracking accuracy of neural activity in mouse brain recordings, as confirmed by expert evaluations.

Conclusion:

Our proposed method offers a novel, translational approach for the treatment and classification of medical and biological time series, addressing some of the prevalent challenges in the field and paving the way for more reliable and effective biomedical signal analysis.