Pub Date : 2024-10-01DOI: 10.1016/j.bbe.2024.09.003
Diego Castillo-Barnes , Nicolás J. Gallego-Molina , Marco A. Formoso , Andrés Ortiz , Patrícia Figueiredo , Juan L. Luque
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.
{"title":"Probabilistic and explainable modeling of Phase–Phase Cross-Frequency Coupling patterns in EEG. Application to dyslexia diagnosis","authors":"Diego Castillo-Barnes , Nicolás J. Gallego-Molina , Marco A. Formoso , Andrés Ortiz , Patrícia Figueiredo , Juan L. Luque","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":"44 4","pages":"Pages 814-823"},"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}
Pub Date : 2024-10-01DOI: 10.1016/j.bbe.2024.09.005
Sabri Altunkaya
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.
海马场电位被广泛应用于神经退行性疾病、癫痫、神经药理学，特别是长短期突触可塑性的研究。要进行这些研究，就必须识别海马场电位信号中的特定成分。然而，手动标记相关信号点进行分析是一个耗时、易出错且主观的过程。目前，还没有专门的软件来自动完成这项任务。本研究在两项独立的实验研究中，考察了三种不同的基于递归神经网络的深度学习架构，用于自动分割海马场电位信号。在第一项实验研究中，使用了 54 只大鼠记录的 10836 个历时场电位信号；在第二项实验研究中，使用了以不同速率向上述数据添加噪声的场电位信号。最佳模型在无噪声数据上的平均 f 分数为 98.1%，在有噪声数据上的平均 f 分数为 97.15%，这突出表明了该模型在实际应用中的鲁棒性。此外，我们还使用重复保持法评估了系统的稳定性，该方法将数据随机分为训练集和测试集 100 次，每次训练一个新版本的系统。结果表明，所提出的系统在所有 100 次迭代测试中显示出相似的平均得分和较低的变异性，从而证明了该系统的可靠性和通用性。
{"title":"Automating synaptic plasticity analysis: A deep learning approach to segmenting hippocampal field potential signal","authors":"Sabri Altunkaya","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":"44 4","pages":"Pages 804-813"},"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}
Pub Date : 2024-10-01DOI: 10.1016/j.bbe.2024.10.002
Mauro Pietribiasi , John K. Leypoldt , Monika Wieliczko , Malgorzata Twardowska-Kawalec , Malgorzata Debowska , Jolanta Malyszko , Jacek Waniewski
Background
Delivery of bicarbonate during hemodialysis (HD) is aimed at correcting metabolic acidosis in end-stage renal disease patients. We tested modified prescriptions of bicarbonate concentration in dialysis fluid (CD,bic), aimed to achieve an optimal pre-dialytic bicarbonate plasma concentration (CP,bic).
Methods
We used a mathematical model to prescribe individualized HD treatments consisting of 1) adjustment of CD,bic to get the pre-dialytic CP,bic in a prescribed range, 2) increase of bicarbonate load before the long interdialytic break, and 3) a single step of increase in CD,bic after two hours. The outcomes were tested in 24 stable HD patients, monitored during a week of standard HD (Test Week) and a week of modified treatment (Intervention Week).
Results
The response to the model-based prescription was different whether the average CD,bic during the Intervention Week was higher or lower than the constant value used for the Test Week. For patients with lower average CD,bic during the Intervention Week, a significant fraction achieved the target (22 ≤ CP,bic ≤ 24 mEq/L). In the group with higher average CD,bic, the interventions were effective only in increasing post-dialytic CP,bic. The simple step-increase profile was effective in linearizing the intradialytic increase in bicarbonate and decreasing the amount of time spent by patients at high plasma CP,bic.
Conclusions
The interventions were effective mostly in patients who needed to lower their pre-dialytic CP,bic. The resistance of the system to increasing pre-dialytic CP,bic in other patients might be caused by modifications of breathing or in hydrogen generation that were not accounted for by our model.
{"title":"Profiled delivery of bicarbonate during weekly cycle of hemodialysis","authors":"Mauro Pietribiasi , John K. Leypoldt , Monika Wieliczko , Malgorzata Twardowska-Kawalec , Malgorzata Debowska , Jolanta Malyszko , Jacek Waniewski","doi":"10.1016/j.bbe.2024.10.002","DOIUrl":"10.1016/j.bbe.2024.10.002","url":null,"abstract":"<div><h3>Background</h3><div>Delivery of bicarbonate during hemodialysis (HD) is aimed at correcting metabolic acidosis in end-stage renal disease patients. We tested modified prescriptions of bicarbonate concentration in dialysis fluid (C<sub>D,bic</sub>), aimed to achieve an optimal pre-dialytic bicarbonate plasma concentration (C<sub>P,bic</sub>).</div></div><div><h3>Methods</h3><div>We used a mathematical model to prescribe individualized HD treatments consisting of 1) adjustment of C<sub>D,bic</sub> to get the pre-dialytic C<sub>P,bic</sub> in a prescribed range, 2) increase of bicarbonate load before the long interdialytic break, and 3) a single step of increase in C<sub>D,bic</sub> after two hours. The outcomes were tested in 24 stable HD patients, monitored during a week of standard HD (Test Week) and a week of modified treatment (Intervention Week).</div></div><div><h3>Results</h3><div>The response to the model-based prescription was different whether the average C<sub>D,bic</sub> during the Intervention Week was higher or lower than the constant value used for the Test Week. For patients with lower average C<sub>D,bic</sub> during the Intervention Week, a significant fraction achieved the target (22 ≤ C<sub>P,bic</sub> ≤ 24 mEq/L). In the group with higher average C<sub>D,bic</sub>, the interventions were effective only in increasing post-dialytic C<sub>P,bic</sub>. The simple step-increase profile was effective in linearizing the intradialytic increase in bicarbonate and decreasing the amount of time spent by patients at high plasma C<sub>P,bic</sub>.</div></div><div><h3>Conclusions</h3><div>The interventions were effective mostly in patients who needed to lower their pre-dialytic CP<sub>,bic</sub>. The resistance of the system to increasing pre-dialytic C<sub>P,bic</sub> in other patients might be caused by modifications of breathing or in hydrogen generation that were not accounted for by our model.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 836-843"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662769","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}
Pub Date : 2024-10-01DOI: 10.1016/j.bbe.2024.10.001
Matheus B. Rocha , Flavio P. Loss , Pedro H. da Cunha , Madson Poltronieri Zanoni , Leandro M. de Lima , Isadora Tavares Nascimento , Isabella Rezende , Tania R.P. Canuto , Luciana de Paula Vieira , Renan Rossoni , Maria C.S. Santos , Patricia Lyra Frasson , Wanderson Romão , Paulo R. Filgueiras , Renato A. Krohling
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 in vivo using NIR spectral data.
{"title":"Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms","authors":"Matheus B. Rocha , Flavio P. Loss , Pedro H. da Cunha , Madson Poltronieri Zanoni , Leandro M. de Lima , Isadora Tavares Nascimento , Isabella Rezende , Tania R.P. Canuto , Luciana de Paula Vieira , Renan Rossoni , Maria C.S. Santos , Patricia Lyra Frasson , Wanderson Romão , Paulo R. Filgueiras , Renato A. Krohling","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":"44 4","pages":"Pages 824-835"},"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}
Pub Date : 2024-10-01DOI: 10.1016/j.bbe.2024.11.004
Paweł Baranowski , Aleksandra Kapusta , Paweł Płatek , Marcin Sarzyński
The paper explores the potential of additive manufacturing (AM), experiments and simulations to develop a personalized shoe sole, with cellular topology used as the insert that minimizes the plantar pressure during running. Five different topologies were manufactured by Fused Filament Fabrication 3D printing technique using thermoplastic polyurethane TPU 95 filaments and tested experimentally and using FEA under compression conditions. The error between the maximum peak force and specific energy absorbed (SEA) from the model and experiment were less than 4.0 % and 6.0 %, respectively. A deformable FE foot model was developed, which was validated against data from the literature on balanced standing and the landing impact test carried out in the study. For the first case, the predicted maximum pressure (Ppeak = 0.20 MPa) was positioned between the data presented in previous papers (0.16 MPa ÷ 0.30 MPa). In the second case, the experimentally measured and numerically predicted force peak values were nearly identical: 1760 N and 1720 N, respectively, falling with the range of 2.2 ÷ 2.5 BW similarly to other studies. Finally, a shoe sole design was proposed based on these topologies, which was simulated in the rearfoot impact to investigate the deformation of the sole and its influence on the foot plantar pressure peak and its distribution. The findings indicated that the sole with cellular structure could drastically reduce plantar pressure and improve overall footwear performance. This research provides valuable guidance and insights for designing, modelling, and simulating customized shoe sole manufactured using the 3D printing technique.
{"title":"Influence of 3D-printed cellular shoe soles on plantar pressure during running − Experimental and numerical studies","authors":"Paweł Baranowski , Aleksandra Kapusta , Paweł Płatek , Marcin Sarzyński","doi":"10.1016/j.bbe.2024.11.004","DOIUrl":"10.1016/j.bbe.2024.11.004","url":null,"abstract":"<div><div>The paper explores the potential of additive manufacturing (AM), experiments and simulations to develop a personalized shoe sole, with cellular topology used as the insert that minimizes the plantar pressure during running. Five different topologies were manufactured by Fused Filament Fabrication 3D printing technique using thermoplastic polyurethane TPU 95 filaments and tested experimentally and using FEA under compression conditions. The error between the maximum peak force and specific energy absorbed (SEA) from the model and experiment were less than 4.0 % and 6.0 %, respectively. A deformable FE foot model was developed, which was validated against data from the literature on balanced standing and the landing impact test carried out in the study. For the first case, the predicted maximum pressure (<em>P<sub>peak</sub></em> = 0.20 MPa) was positioned between the data presented in previous papers (0.16 MPa ÷ 0.30 MPa). In the second case, the experimentally measured and numerically predicted force peak values were nearly identical: 1760 N and 1720 N, respectively, falling with the range of 2.2 ÷ 2.5 BW similarly to other studies. Finally, a shoe sole design was proposed based on these topologies, which was simulated in the rearfoot impact to investigate the deformation of the sole and its influence on the foot plantar pressure peak and its distribution. The findings indicated that the sole with cellular structure could drastically reduce plantar pressure and improve overall footwear performance. This research provides valuable guidance and insights for designing, modelling, and simulating customized shoe sole manufactured using the 3D printing technique.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 858-873"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142744575","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}
Pub Date : 2024-10-01DOI: 10.1016/j.bbe.2024.11.002
Kyeonghwan Lee, Jaewon Lee, Miyoung Shin
We recently investigated beat score map (BSM)-based methods for electrocardiogram (ECG)-based arrhythmia classification. Although BSM-based methods show impressive performance, they are somewhat resource-intensive owing to the arrangement of beat score vectors generated from 1D ECG sequences with zero-padding across time points. To address this issue, we propose a lightweight BSM (Lw-BSM) method that significantly reduces the size of the original BSM while capturing the characteristics of beat arrangement patterns as does the original BSM. Specifically, two types of Lw-BSMs are generated without zero-padding and evaluated for multiclass arrhythmia prediction. Experimental results on two public datasets, MIT-BIH and SPH, demonstrate that arrhythmia classification using Lw-BSM images is quite comparable to that using the original BSM images as an input to CNN-based classification models. At the same time, the image size can be reduced significantly. Moreover, it is observed that this approach is almost insensitive to the selection of the R-peak detection algorithm, showing stable performance across different R-peak algorithms.
{"title":"Lightweight beat score map method for electrocardiogram-based arrhythmia classification","authors":"Kyeonghwan Lee, Jaewon Lee, Miyoung Shin","doi":"10.1016/j.bbe.2024.11.002","DOIUrl":"10.1016/j.bbe.2024.11.002","url":null,"abstract":"<div><div>We recently investigated beat score map (BSM)-based methods for electrocardiogram (ECG)-based arrhythmia classification. Although BSM-based methods show impressive performance, they are somewhat resource-intensive owing to the arrangement of beat score vectors generated from 1D ECG sequences with zero-padding across time points. To address this issue, we propose a lightweight BSM (Lw-BSM) method that significantly reduces the size of the original BSM while capturing the characteristics of beat arrangement patterns as does the original BSM. Specifically, two types of Lw-BSMs are generated without zero-padding and evaluated for multiclass arrhythmia prediction. Experimental results on two public datasets, MIT-BIH and SPH, demonstrate that arrhythmia classification using Lw-BSM images is quite comparable to that using the original BSM images as an input to CNN-based classification models. At the same time, the image size can be reduced significantly. Moreover, it is observed that this approach is almost insensitive to the selection of the R-peak detection algorithm, showing stable performance across different R-peak algorithms.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"44 4","pages":"Pages 844-857"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701318","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}
Pub Date : 2024-09-23DOI: 10.1016/j.bbe.2024.09.002
Alessandra Angelucci , Federica Camuncoli , Federica Dotti , Filippo Bertozzi , Manuela Galli , Marco Tarabini , Andrea Aliverti
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.
{"title":"Validation of a body sensor network for cardiorespiratory monitoring during dynamic activities","authors":"Alessandra Angelucci , Federica Camuncoli , Federica Dotti , Filippo Bertozzi , Manuela Galli , Marco Tarabini , Andrea Aliverti","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":"44 4","pages":"Pages 794-803"},"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}
Pub Date : 2024-09-20DOI: 10.1016/j.bbe.2024.08.005
Suqin Huang , Bao Li , Jincheng Liu , Liyuan Zhang , Hao Sun , Huanmei Guo , Yanping Zhang , Fuyou Liang , Yanjun Gong , Youjun Liu
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.
{"title":"Quantitative evaluation of the effect of circle of willis structures on cerebral hyperperfusion: A multi-scale model analysis","authors":"Suqin Huang , Bao Li , Jincheng Liu , Liyuan Zhang , Hao Sun , Huanmei Guo , Yanping Zhang , Fuyou Liang , Yanjun Gong , Youjun Liu","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":"44 4","pages":"Pages 782-793"},"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}
Pub Date : 2024-09-19DOI: 10.1016/j.bbe.2024.09.004
Yuanyuan Zhou , Azin S. Mousavi , Yekanth R. Chalumuri , Jesse D. Parreira , Mihir Modak , Jesus Antonio Sanchez-Perez , Asim H. Gazi , Omer T. Inan , Jin-Oh Hahn
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.
{"title":"Inference-enabled tracking of acute mental stress via multi-modal wearable physiological sensing: A proof-of-concept study","authors":"Yuanyuan Zhou , Azin S. Mousavi , Yekanth R. Chalumuri , Jesse D. Parreira , Mihir Modak , Jesus Antonio Sanchez-Perez , Asim H. Gazi , Omer T. Inan , Jin-Oh Hahn","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":"44 4","pages":"Pages 771-781"},"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}
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.
{"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":"7 1","pages":""},"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}
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