Biocybernetics and Biomedical Engineering最新文献

{"title":"Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis","authors":"","doi":"10.1016/j.bbe.2024.09.003","DOIUrl":"10.1016/j.bbe.2024.09.003","url":null,"abstract":"<div><div>This work explores the intricate neural dynamics associated with dyslexia through the lens of Cross-Frequency Coupling (CFC) analysis applied to electroencephalography (EEG) signals evaluated from 48 seven-year-old Spanish readers from the LEEDUCA research platform. The analysis focuses on CFS (Cross-Frequency phase Synchronization) maps, capturing the interaction between different frequency bands during low-level auditory processing stimuli. Then, making use of Gaussian Mixture Models (GMMs), CFS activations are quantified and classified, offering a compressed representation of EEG activation maps. The study unveils promising results specially at the Theta-Gamma coupling (Area Under the Curve = 0.821), demonstrating the method’s sensitivity to dyslexia-related neural patterns and highlighting potential applications in the early identification of dyslexic individuals.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automating synaptic plasticity analysis: A deep learning approach to segmenting hippocampal field potential signal","authors":"","doi":"10.1016/j.bbe.2024.09.005","DOIUrl":"10.1016/j.bbe.2024.09.005","url":null,"abstract":"<div><div>Hippocampal field potentials are widely used in research on neurodegenerative diseases, epilepsy, neuropharmacology, and particularly long- and short-term synaptic plasticity. To conduct these studies, it is necessary to identify specific components within hippocampal field potential signals. However, manually marking the relevant signal points for analysis is a time-consuming, error-prone, and subjective process. Currently, there is no specialized software dedicated to automating this task. In this study, three different recurrent neural network-based deep learning architectures were examined for the automatic segmentation of hippocampal field potential signals in two separate experimental studies. In the first experimental study, 10,836 epochs of field potential signals recorded from 54 rats were used, and in the second experimental study, field potential signals with noise added to the above data at different rates were used. The best model achieved an average f-score of 98.1% on noise-free data and 97.15% on data with noise, highlighting its robustness in real-world scenarios. Furthermore, we assessed system stability using the repeated holdout method, which randomly split the data into training and testing sets 100 times, and each time trained a new version of the system. As a result, the proposed system was proven to be reliable and generalizable by showing similar average scores and low variability across all 100 iterations of the test.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142420204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms","authors":"","doi":"10.1016/j.bbe.2024.10.001","DOIUrl":"10.1016/j.bbe.2024.10.001","url":null,"abstract":"<div><div>Skin lesions are classified in benign or malignant. Among the malignant, melanoma is a very aggressive cancer and the major cause of deaths. So, early diagnosis of skin cancer is very desired. In the last few years, there is a growing interest in computer aided diagnostic (CAD) of skin lesions. Near-Infrared (NIR) spectroscopy may provide an alternative source of information to automated CAD of skin lesions to be used with the modern techniques of machine learning and deep learning (MDL). One of the main limitations to apply MDL to spectroscopy is the lack of public datasets. Since there is no public dataset of NIR spectral data to skin lesions, as far as we know, an effort has been made and a new dataset named NIR-SC-UFES, has been collected, annotated and analyzed generating the gold-standard for classification of NIR spectral data to skin cancer. Next, the machine learning algorithms XGBoost, CatBoost, LightGBM, 1D-convolutional neural network (1D-CNN) and standard algorithms as SVM and PLS-DA were investigated to classify cancer and non-cancer skin lesions. Experimental results indicate that the best performance was obtained by LightGBM with pre-processing using standard normal variate (SNV), feature extraction and data augmentation with Generative Adversarial Networks (GAN) providing values of 0.839 for balanced accuracy, 0.851 for recall, 0.852 for precision, and 0.850 for F-score. The obtained results indicate the first steps in CAD of skin lesions aiming the automated triage of patients with skin lesions <em>in vivo</em> using NIR spectral data.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validation of a body sensor network for cardiorespiratory monitoring during dynamic activities","authors":"","doi":"10.1016/j.bbe.2024.09.002","DOIUrl":"10.1016/j.bbe.2024.09.002","url":null,"abstract":"<div><div>One of the major challenges in the field of wearable devices is to accurately measure physiological parameters during dynamic activities. The aim of this work is to present a completely wearable Wireless Body Sensor Network (WBSN) for cardio-respiratory monitoring during dynamic activities and a validation of the devices composing the WBSN against reference measurement systems. The WBSN is composed of three inertial measurement units (IMUs) to detect the respiratory rate (RR), and of a fourth unit to detect the pulse rate (PR). 30 healthy volunteers (17 men, mean age 25.9 ± 6.0 years, mean weight 68.7 ± 9.7 kg, mean height 170.9 ± 9.5 cm) were enrolled in a validation protocol consisting in walking, running, and cycling. The participants had to simultaneously wear the devices of the WBSN and reference instruments. The IMU-based system proved to be particularly effective in monitoring RR during cycling, with a RMSE of 3.77 bpm for the complete cohort, and during running. The respiratory signal during walking exhibited a frequency content like the stride, making it difficult to properly filter the desired signal content. PR showed good agreement with the reference heart rate monitor. The system exploits information regarding motion to improve RR estimation during dynamic activities thanks to an ad hoc signal processing algorithm.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000676/pdfft?md5=7b1f1f42608cbb77aef909206d34b316&pid=1-s2.0-S0208521624000676-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142312638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative evaluation of the effect of circle of willis structures on cerebral hyperperfusion: A multi-scale model analysis","authors":"","doi":"10.1016/j.bbe.2024.08.005","DOIUrl":"10.1016/j.bbe.2024.08.005","url":null,"abstract":"<div><p>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.</p><p>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.</p><p>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%.</p><p>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.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inference-enabled tracking of acute mental stress via multi-modal wearable physiological sensing: A proof-of-concept study","authors":"","doi":"10.1016/j.bbe.2024.09.004","DOIUrl":"10.1016/j.bbe.2024.09.004","url":null,"abstract":"<div><h3>Objective</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>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. <u>Significance</u>: The ability to continuously monitor and track mental stress can collectively improve human wellbeing.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142274624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An in-vitro cell culture system for accurately reproducing the coupled hemodynamic signals at the artery endothelium","authors":"Lixue Liang, Xueying Wang, Dong Chen, Yanxia Wang, Xiaoyue Luo, Bo Liu, Yu Wang, Kai-rong Qin","doi":"10.1016/j.bbe.2024.08.001","DOIUrl":"https://doi.org/10.1016/j.bbe.2024.08.001","url":null,"abstract":"Microfluidics has been an effective technology to reconstruct the in-vivo physiological hemodynamic microenvironment, which is significantly important for preventing and curing circulatory system-related diseases. However, these existing microfluidic systems have failed to accurately reproduce the arterial blood pressure, shear stress, circumferential strain, as well as their coupling relationship, and have not taken into account whether the cells at various locations in the culture chamber are subjected to consistent mechanical stimulation. To solve the above shortcomings, this study developed an in-vitro endothelial cell culture system (ECCS) containing a microfluidic chip and afterload components based on the hemodynamic principles to reappear the desired hemodynamic signals and their coupling relationship accurately, while a relatively uniform area of stress and strain distribution was selected in the microfluidic chip for a more reliable cell mechanobiology study. The sensitivity of global hemodynamic behaviors of the ECCS was analyzed, and numerical simulation and in-vitro experiments were implemented to verify the performance of the proposed ECCS. Finally, the cellular hemodynamic response was tested using human umbilical vein endothelial cells, demonstrating that the proposed in-vitro ECCS has better biological effectiveness. In general, the proposed ECCS in this study provided a more accurate and reliable tool for reproducing the in-vivo hemodynamic microenvironment and showed good potential in the mechanobiology study.","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlations between vascular properties and mental dysfunctions in long-COVID-19 support the vascular depression hypothesis","authors":"","doi":"10.1016/j.bbe.2024.07.001","DOIUrl":"10.1016/j.bbe.2024.07.001","url":null,"abstract":"<div><h3>Objectives</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>Brachial WA-CA = 13 yrs, in average (p &lt; 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.</p></div><div><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000482/pdfft?md5=977e303aaf5ed949204aeb13fb38c49b&pid=1-s2.0-S0208521624000482-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141941432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallel collaboration and closed-loop control of a cursor using multimodal physiological signals","authors":"","doi":"10.1016/j.bbe.2024.07.004","DOIUrl":"10.1016/j.bbe.2024.07.004","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000512/pdfft?md5=330cd4fbe5c5491b0301fa371de3d879&pid=1-s2.0-S0208521624000512-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141941466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics","authors":"","doi":"10.1016/j.bbe.2024.07.006","DOIUrl":"10.1016/j.bbe.2024.07.006","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0208521624000536/pdfft?md5=93b6aff09e56179150aed20a868b9e84&pid=1-s2.0-S0208521624000536-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}