Pub Date : 2025-02-28DOI: 10.1016/j.bbe.2025.02.005
Shanshan Wang , Xiaoni Wang , Yuxin Zhao, Lin Xie, Jianbao Zhang
Although growing evidences suggest heartbeat evoked potential (HEP) as a biomarker of interoception, little is known about how HEP is related to cardiovascular function. In the article, mental arithmetic and meditation tasks that respectively activate sympathetic and parasympathetic activities were designed, and electroencephalogram and cardiovascular parameters were recorded in healthy young males. Our findings revealed a decrease in HEP during mental arithmetic and an increase during meditation. A correlation between HEP and blood pressure was also observed, indicating that baroreceptor stretch may contribute to HEP generation. Furthermore, HEP showed a positive correlation with parasympathetic activity and a negative correlation with sympathetic activity. Collectively, these results suggest the presence of a potential negative feedback loop between the brain and heart, mediated by HEP.
{"title":"A feedback loop study of brain-heart interaction based on HEP and HRV","authors":"Shanshan Wang , Xiaoni Wang , Yuxin Zhao, Lin Xie, Jianbao Zhang","doi":"10.1016/j.bbe.2025.02.005","DOIUrl":"10.1016/j.bbe.2025.02.005","url":null,"abstract":"<div><div>Although growing evidences suggest heartbeat evoked potential (HEP) as a biomarker of interoception, little is known about how HEP is related to cardiovascular function. In the article, mental arithmetic and meditation tasks that respectively activate sympathetic and parasympathetic activities were designed, and electroencephalogram and cardiovascular parameters were recorded in healthy young males. Our findings revealed a decrease in HEP during mental arithmetic and an increase during meditation. A correlation between HEP and blood pressure was also observed, indicating that baroreceptor stretch may contribute to HEP generation. Furthermore, HEP showed a positive correlation with parasympathetic activity and a negative correlation with sympathetic activity. Collectively, these results suggest the presence of a potential negative feedback loop between the brain and heart, mediated by HEP.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 2","pages":"Pages 181-188"},"PeriodicalIF":5.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519106","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 : 2025-02-27DOI: 10.1016/j.bbe.2025.02.004
Andrea Formaggio , Margherita De Luca , Simone Borrelli , Giovanni Putame , Nello De Vita , Fabio Minelli , Francesco Della Corte , Rosanna Vaschetto , Alberto L. Audenino , Carlo Olivieri , Mara Terzini
This study focuses on optimizing a non-invasive ventilation (NIV) circuit for the treatment of hypoxemic respiratory failure using continuous positive airway pressure (CPAP). A multidomain 0D in silico approach was employed, creating a lumped circuit model of an innovative NIV-CPAP system in Mathworks® Simulink. The model relies on in vitro tests on commercial components characterizing pneumatic resistive behavior, and it exploits an extended resistance-inductance-capacitance model for the patient’s respiratory system, recurring to sigmoidal pressure–volume behavior characteristic of pathological conditions. The NIV-CPAP system was assembled in vitro and connected to a lung simulator to validate the model under healthy and pathological conditions (acute respiratory distress syndrome and chronic obstructive pulmonary disease). The study explored the impact of key features on the ventilation circuit, such as interface leakage, air volume within the circuit, and resistance induced by circuit components.
Validation of the 0D model through in vitro tests showed correlation coefficients between 0.9 and 1. Interface leakage caused reductions of up to 6% in delivered static pressure. Changes in air volume (mask or helmet interface, reservoirs adding) resulted in a maximum 8% decrease in pressure oscillations. Increased resistances from the starting ventilation circuit produced a tidal volume reduction of less than 1%. An optimized configuration that balanced resistances between limbs improved intrinsic positive end-expiratory pressure generation.
The proposed 0D model proved to be effective in guiding the design of the innovative device, providing computational efficiency and flexibility; it demonstrated its reliability as a tool to support the optimization of non-invasive ventilation circuits.
{"title":"A multidomain 0D model for continuous positive airway pressure ventilation circuit design: Validation and applications","authors":"Andrea Formaggio , Margherita De Luca , Simone Borrelli , Giovanni Putame , Nello De Vita , Fabio Minelli , Francesco Della Corte , Rosanna Vaschetto , Alberto L. Audenino , Carlo Olivieri , Mara Terzini","doi":"10.1016/j.bbe.2025.02.004","DOIUrl":"10.1016/j.bbe.2025.02.004","url":null,"abstract":"<div><div>This study focuses on optimizing a non-invasive ventilation (NIV) circuit for the treatment of hypoxemic respiratory failure using continuous positive airway pressure (CPAP). A multidomain 0D <em>in silico</em> approach was employed, creating a lumped circuit model of an innovative NIV-CPAP system in Mathworks® Simulink. The model relies on <em>in vitro</em> tests on commercial components characterizing pneumatic resistive behavior, and it exploits an extended resistance-inductance-capacitance model for the patient’s respiratory system, recurring to sigmoidal pressure–volume behavior characteristic of pathological conditions. The NIV-CPAP system was assembled <em>in vitro</em> and connected to a lung simulator to validate the model under healthy and pathological conditions (acute respiratory distress syndrome and chronic obstructive pulmonary disease). The study explored the impact of key features on the ventilation circuit, such as interface leakage, air volume within the circuit, and resistance induced by circuit components.</div><div>Validation of the 0D model through <em>in vitro</em> tests showed correlation coefficients between 0.9 and 1. Interface leakage caused reductions of up to 6% in delivered static pressure. Changes in air volume (mask or helmet interface, reservoirs adding) resulted in a maximum 8% decrease in pressure oscillations. Increased resistances from the starting ventilation circuit produced a tidal volume reduction of less than 1%. An optimized configuration that balanced resistances between limbs improved intrinsic positive end-expiratory pressure generation.</div><div>The proposed 0D model proved to be effective in guiding the design of the innovative device, providing computational efficiency and flexibility; it demonstrated its reliability as a tool to support the optimization of non-invasive ventilation circuits.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 2","pages":"Pages 170-180"},"PeriodicalIF":5.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511969","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}
Mental arithmetic can be helpful for the evaluation of neurodevelopmental disorders arising from atypical development of the brain. We propose a novel explainable machine learning method for classifying mental arithmetic calculation tasks from resting brain states and good from bad calculations using Electroencephalography. Empirical mode decomposition features are extracted from intrinsic mode functions of the average signals of all trials. Most relevant features to the mental arithmetic tasks are ranked by a random forest-based recursive feature elimination method. These features identify the changes in frequency bands of the brain rhythms, such as delta, theta, and alpha, during mental tasks for the first time in literature. These unique explainable features are also used to identify brain areas such as frontal, temporal, and occipital lobes involved in mental arithmetic tasks. Moreover, our approach describes the memory regions and that bad calculations excite the brain areas, mostly related to emotions such as frustration and anxiety due to stressful mental arithmetic. Using a random forest classifier, beating the state-of-the-art, this method achieved classification accuracies of 99.30 % and 98.33 % for resting vs calculation and good vs bad calculation brain tasks, respectively. Also, our method outperformed the state of art in handling the inter-subject variability and achieved 98.17 ± 0.47 % and 97.19 ± 0.95 % classification accuracies for resting vs calculation and good vs bad calculation tasks, respectively.
{"title":"Electroencephalograph (EEG) based classification of mental arithmetic using explainable machine learning","authors":"Murtaza Aslam , Fozia Rajbdad , Shoaib Azmat , Kausar Perveen , Morteza Naraghi-Pour , Jian Xu","doi":"10.1016/j.bbe.2025.02.002","DOIUrl":"10.1016/j.bbe.2025.02.002","url":null,"abstract":"<div><div>Mental arithmetic can be helpful for the evaluation of neurodevelopmental disorders arising from atypical development of the brain. We propose a novel explainable machine learning method for classifying mental arithmetic calculation tasks from resting brain states and good from bad calculations using Electroencephalography. Empirical mode decomposition features are extracted from intrinsic mode functions of the average signals of all trials. Most relevant features to the mental arithmetic tasks are ranked by a random forest-based recursive feature elimination method. These features identify the changes in frequency bands of the brain rhythms, such as delta, theta, and alpha, during mental tasks for the first time in literature. These unique explainable features are also used to identify brain areas such as frontal, temporal, and occipital lobes involved in mental arithmetic tasks. Moreover, our approach describes the memory regions and that bad calculations excite the brain areas, mostly related to emotions such as frustration and anxiety due to stressful mental arithmetic. Using a random forest classifier, beating the state-of-the-art, this method achieved classification accuracies of 99.30 % and 98.33 % for resting vs calculation and good vs bad calculation brain tasks, respectively. Also, our method outperformed the state of art in handling the inter-subject variability and achieved 98.17 ± 0.47 % and 97.19 ± 0.95 % classification accuracies for resting vs calculation and good vs bad calculation tasks, respectively.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 2","pages":"Pages 154-169"},"PeriodicalIF":5.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488564","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 : 2025-02-24DOI: 10.1016/j.bbe.2025.02.001
Md Mohiuddin Soliman , Mohammad Tariqul Islam , Phumin Kirawanich , Muhammad E.H. Chowdhury , Touhidul Alam , Ayed M. Alrashdi , Norbahiah Misran , Mohamed S. Soliman
This research analyses contact pressure, sliding distance, and wear rate at the trunnion interface of hip implants during various activities to understand post-hip replacement outcomes. The study uses a numerical model and ISO-7206–6:2013 constraints with an AML hip implant. Greater Fx, Fy, and Fz forces broaden contact pressure distribution. The highest pressure occurs on the proximal superolateral surface, with the lowest on the anterior and posterior surfaces. The HIGH100 (individuals weighing above 100 kg) weight category demonstrates 2 times higher maximum and average contact pressure compared to AVG75 (individuals weighing 75 kg) for sit-down and knee bend activities. Force components and the duration of a full gait cycle influence sliding distance. Stance activities show the highest sliding distance due to rapid changes in force load during the gait cycle. For sit-down and knee bend activities, the total sliding distance is 2.5 times higher in the HIGH100 weight category compared to AVG75. Sliding distance primarily occurs at the proximal superolateral-inferomedial-anterior-posterior contact surface, decreasing distally. Based on contact pressure, sliding distance, and wear volume rate, jogging and stance activities pose the highest risk for hip replacement patients, while cycling is the safest. The HIGH100 weight group exhibits 5- and 4-times greater wear volume rates than AVG75 in sit-down and knee bend activities, respectively. The research findings align with wear degradation observed in retrieved hip implants, validating the study. These insights can assist patients in making informed decisions about performing activities after surgery while enabling physicians to provide accurate guidelines.
{"title":"Contact Pressure, sliding distance and wear rate analysis at trunnion of hip implant for daily Activities: A finite element approach","authors":"Md Mohiuddin Soliman , Mohammad Tariqul Islam , Phumin Kirawanich , Muhammad E.H. Chowdhury , Touhidul Alam , Ayed M. Alrashdi , Norbahiah Misran , Mohamed S. Soliman","doi":"10.1016/j.bbe.2025.02.001","DOIUrl":"10.1016/j.bbe.2025.02.001","url":null,"abstract":"<div><div>This research analyses contact pressure, sliding distance, and wear rate at the trunnion interface of hip implants during various activities to understand post-hip replacement outcomes. The study uses a numerical model and ISO-7206–6:2013 constraints with an AML hip implant. Greater F<sub>x</sub>, F<sub>y</sub>, and F<sub>z</sub> forces broaden contact pressure distribution. The highest pressure occurs on the proximal superolateral surface, with the lowest on the anterior and posterior surfaces. The HIGH100 (individuals weighing above 100 kg) weight category demonstrates 2 times higher maximum and average contact pressure compared to AVG75 (individuals weighing 75 kg) for sit-down and knee bend activities. Force components and the duration of a full gait cycle influence sliding distance. Stance activities show the highest sliding distance due to rapid changes in force load during the gait cycle. For sit-down and knee bend activities, the total sliding distance is 2.5 times higher in the HIGH100 weight category compared to AVG75. Sliding distance primarily occurs at the proximal superolateral-inferomedial-anterior-posterior contact surface, decreasing distally. Based on contact pressure, sliding distance, and wear volume rate, jogging and stance activities pose the highest risk for hip replacement patients, while cycling is the safest. The HIGH100 weight group exhibits 5- and 4-times greater wear volume rates than AVG75 in sit-down and knee bend activities, respectively. The research findings align with wear degradation observed in retrieved hip implants, validating the study. These insights can assist patients in making informed decisions about performing activities after surgery while enabling physicians to provide accurate guidelines.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 2","pages":"Pages 137-153"},"PeriodicalIF":5.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479429","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 : 2025-01-01DOI: 10.1016/j.bbe.2024.11.001
Haoneng Lin , Jing Zou , Sen Deng , Ka Po Wong , Angelica I. Aviles-Rivero , Yiting Fan , Alex Pui-Wai Lee , Xiaowei Hu , Jing Qin
The Segment Anything Model (SAM) exhibits exceptional generalization capabilities in diverse domains, owing to its interactive learning mechanism designed for precise image segmentation. However, applying SAM to out-of-distribution domains, especially in 3D medical image segmentation, poses challenges. Existing methods for adapting 2D segmentation models to 3D medical data treat 3D volumes as a mere stack of 2D slices. The essential inter-slice information, which is pivotal to faithful 3D medical image segmentation tasks, is unfortunately neglected. In this work, we present the 3D Medical SAM-Adapter (3DMedSAM), a pioneer cross-dimensional adaptation, leveraging SAM’s pre-trained knowledge while accommodating the unique characteristics of 3D medical data. Firstly, to bridge the dimensional gap from 2D to 3D, we design a novel module to replace SAM’s patch embedding, ensuring a seamless transition into 3D image processing and recognition. Besides, we incorporate a 3D Adapter while maintaining the majority of pre-training parameters frozen, enriching deep features with abundant 3D spatial information and achieving efficient fine-tuning. Given the diverse scales of anomalies present in medical images, we also devised a multi-scale 3D mask decoder to elevate the network’s proficiency in medical image segmentation. Through various experiments, we showcase the effectiveness of 3DMedSAM in achieving accurate and robust 3D segmentation on both single-target segmentation and multi-organ segmentation tasks, surpassing the limitations of current methods.
{"title":"Volumetric medical image segmentation via fully 3D adaptation of Segment Anything Model","authors":"Haoneng Lin , Jing Zou , Sen Deng , Ka Po Wong , Angelica I. Aviles-Rivero , Yiting Fan , Alex Pui-Wai Lee , Xiaowei Hu , Jing Qin","doi":"10.1016/j.bbe.2024.11.001","DOIUrl":"10.1016/j.bbe.2024.11.001","url":null,"abstract":"<div><div>The Segment Anything Model (SAM) exhibits exceptional generalization capabilities in diverse domains, owing to its interactive learning mechanism designed for precise image segmentation. However, applying SAM to out-of-distribution domains, especially in 3D medical image segmentation, poses challenges. Existing methods for adapting 2D segmentation models to 3D medical data treat 3D volumes as a mere stack of 2D slices. The essential inter-slice information, which is pivotal to faithful 3D medical image segmentation tasks, is unfortunately neglected. In this work, we present the 3D Medical SAM-Adapter (3DMedSAM), a pioneer cross-dimensional adaptation, leveraging SAM’s pre-trained knowledge while accommodating the unique characteristics of 3D medical data. Firstly, to bridge the dimensional gap from 2D to 3D, we design a novel module to replace SAM’s patch embedding, ensuring a seamless transition into 3D image processing and recognition. Besides, we incorporate a 3D Adapter while maintaining the majority of pre-training parameters frozen, enriching deep features with abundant 3D spatial information and achieving efficient fine-tuning. Given the diverse scales of anomalies present in medical images, we also devised a multi-scale 3D mask decoder to elevate the network’s proficiency in medical image segmentation. Through various experiments, we showcase the effectiveness of 3DMedSAM in achieving accurate and robust 3D segmentation on both single-target segmentation and multi-organ segmentation tasks, surpassing the limitations of current methods.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 1-10"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093056","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 : 2025-01-01DOI: 10.1016/j.bbe.2025.01.004
Alireza Karimi , Marie Darche , Ansel Stanik , Reza Razaghi , Iman Mirafzal , Kamran Hassani , Mojtaba Hassani , Elizabeth White , Ivana Gantar , Stéphane Pagès , Laura Batti , Ted S. Acott , Michel Paques
Objective
Aging results in significant structural and functional changes in the anterior segment of the eye, influencing intraocular pressure (IOP) and overall ocular health. Although aging is a well-established risk factor for primary open-angle glaucoma, a leading cause of irreversible blindness, the specific mechanisms through which aging drives morphological changes in anterior segment tissues and affects aqueous humor dynamics remain incompletely understood.
Methods
In this study, we employed cutting-edge light sheet fluorescence microscopy (LSFM) to capture high-resolution, volumetric images of cleared human donor eyes’ anterior segment tissues. This advanced imaging enabled a comprehensive morphological analysis of key parameters, including central and peripheral corneal thickness (CCT and PCT), iris thickness, anterior chamber area (ACA), and ciliary body area (CBA). By integrating these morphological parameters with computational fluid dynamics (CFD) models, we analyzed aqueous humor dynamics across n = 6 female human donor eyes, spanning a wide age range of 5 to 94 years (all of Caucasian descent).
Results
The CCT and PCT demonstrated thinning with age, accompanied by a reduction in ACA. In contrast, the CBA remained relatively stable across all age groups. Computational fluid dynamics analysis showed a decline in aqueous humor velocity and wall shear stress, with younger eyes exhibiting higher velocities and shear stress, compared to older eyes.
Conclusion
These findings emphasize the value of integrating LSFM and CFD approaches to provide a detailed understanding of how aging impacts the anterior segment and its fluid dynamics. This study contributes to the understanding of age-related ocular changes, highlighting the importance of considering these changes in the diagnosis and management of age-related eye diseases.
{"title":"Impact of aging on anterior segment morphology and aqueous humor dynamics in human Eyes: Advanced imaging and computational techniques","authors":"Alireza Karimi , Marie Darche , Ansel Stanik , Reza Razaghi , Iman Mirafzal , Kamran Hassani , Mojtaba Hassani , Elizabeth White , Ivana Gantar , Stéphane Pagès , Laura Batti , Ted S. Acott , Michel Paques","doi":"10.1016/j.bbe.2025.01.004","DOIUrl":"10.1016/j.bbe.2025.01.004","url":null,"abstract":"<div><h3>Objective</h3><div>Aging results in significant structural and functional changes in the anterior segment of the eye, influencing intraocular pressure (IOP) and overall ocular health. Although aging is a well-established risk factor for primary open-angle glaucoma, a leading cause of irreversible blindness, the specific mechanisms through which aging drives morphological changes in anterior segment tissues and affects aqueous humor dynamics remain incompletely understood.</div></div><div><h3>Methods</h3><div>In this study, we employed cutting-edge light sheet fluorescence microscopy (LSFM) to capture high-resolution, volumetric images of cleared human donor eyes’ anterior segment tissues. This advanced imaging enabled a comprehensive morphological analysis of key parameters, including central and peripheral corneal thickness (CCT and PCT), iris thickness, anterior chamber area (ACA), and ciliary body area (CBA). By integrating these morphological parameters with computational fluid dynamics (CFD) models, we analyzed aqueous humor dynamics across <em>n</em> = 6 female human donor eyes, spanning a wide age range of 5 to 94 years (all of Caucasian descent).</div></div><div><h3>Results</h3><div>The CCT and PCT demonstrated thinning with age, accompanied by a reduction in ACA. In contrast, the CBA remained relatively stable across all age groups. Computational fluid dynamics analysis showed a decline in aqueous humor velocity and wall shear stress, with younger eyes exhibiting higher velocities and shear stress, compared to older eyes.</div></div><div><h3>Conclusion</h3><div>These findings emphasize the value of integrating LSFM and CFD approaches to provide a detailed understanding of how aging impacts the anterior segment and its fluid dynamics. This study contributes to the understanding of age-related ocular changes, highlighting the importance of considering these changes in the diagnosis and management of age-related eye diseases.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 62-73"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093057","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 : 2025-01-01DOI: 10.1016/j.bbe.2025.01.001
Marta Mikuła-Zdańkowska , Dawid Borycki , Piotr Węgrzyn , Karolis Adomavičius , Egidijus Auksorius , Maciej Wojtkowski
We demonstrate an experimental Spatio-Temporal Optical Coherence Tomography (STOC-T) system featuring optimized illumination and an increased lateral resolution of approximately 3 µm. The integration of high-speed phase randomization with a numerical averaging process facilitates a noticeable improvement in the signal-to-noise ratio. The effectiveness of this enhancement is demonstrated through volumetric imaging of a scattering object, and it enables in vivo imaging of the human retina at the cellular level. Additionally, the experiment is supported by computational aberration-correction techniques to achieve high-resolution in vivo imaging of the human retina. The visualization of retinal cone mosaics, and ganglion cell somas was achieved through contrast enhancement during the averaging process.
{"title":"Imaging of retinal ganglion cells and photoreceptors using Spatio-Temporal Optical Coherence Tomography (STOC-T) without hardware-based adaptive optics","authors":"Marta Mikuła-Zdańkowska , Dawid Borycki , Piotr Węgrzyn , Karolis Adomavičius , Egidijus Auksorius , Maciej Wojtkowski","doi":"10.1016/j.bbe.2025.01.001","DOIUrl":"10.1016/j.bbe.2025.01.001","url":null,"abstract":"<div><div>We demonstrate an experimental Spatio-Temporal Optical Coherence Tomography (STOC-T) system featuring optimized illumination and an increased lateral resolution of approximately 3 <!--> <!-->µm. The integration of high-speed phase randomization with a numerical averaging process facilitates a noticeable improvement in the signal-to-noise ratio. The effectiveness of this enhancement is demonstrated through volumetric imaging of a scattering object, and it enables <em>in vivo</em> imaging of the human retina at the cellular level. Additionally, the experiment is supported by computational aberration-correction techniques to achieve high-resolution <em>in vivo</em> imaging of the human retina. The visualization of retinal cone mosaics, and ganglion cell somas was achieved through contrast enhancement during the averaging process.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 52-61"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093050","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 : 2025-01-01DOI: 10.1016/j.bbe.2024.12.003
Michal Piela, Marian P. Kotas
In this paper, we apply modified spatio-temporal matched filtering (MSTMF) to enhance electroencephalographic (EEG) signals in evoked potentials (EP) based brain–computer interfaces (BCI). Our focus is on the effective treatment of noise in the system under consideration.
The applied MSTMF is a spatio-temporal extension of generalized matched filtering, which allows for optimal enhancement of weak, repeatable signals embedded in colored Gaussian noise. However, since spontaneous EEG signals are often corrupted by high-energy super-Gaussian artifacts, which deviate from this distribution, we propose rejecting these artifacts before applying MSTMF. Particularly effective have been algorithms based on independent component analysis (ICA) and empirical mode decomposition (EMD). After artifacts rejection, performed locally within time segments they occupy, without disturbing other parts of the signal, the classification of brain responses becomes more accurate. Nevertheless, the nonstationarity of the EEG signal remains a challenge that must be addressed.
Therefore, we propose adjusting the MSTMF to the current noise properties to improve its performance in this demanding environment. This can be achieved by properly calculating the noise covariance matrix, which is necessary to determine the filter coefficients, using both the learning and currently processed signal segments.
As a result, we have developed an enhanced method based on MSTMF for improved discrimination of evoked potentials and verified its performance on two publicly available reference databases: BCIAUT-P300 (for IFMBE Scientific Challenge) and Speller (for the BCI Competition III Challenge 2004). For these databases, we have achieved overall accuracies of 92.67% and 99.5%, surpassing the reference methods presented in the literature.
{"title":"Spatio-temporal matched filter adjustment for enhanced accuracy in brain responses classification","authors":"Michal Piela, Marian P. Kotas","doi":"10.1016/j.bbe.2024.12.003","DOIUrl":"10.1016/j.bbe.2024.12.003","url":null,"abstract":"<div><div>In this paper, we apply modified spatio-temporal matched filtering (MSTMF) to enhance electroencephalographic (EEG) signals in evoked potentials (EP) based brain–computer interfaces (BCI). Our focus is on the effective treatment of noise in the system under consideration.</div><div>The applied MSTMF is a spatio-temporal extension of generalized matched filtering, which allows for optimal enhancement of weak, repeatable signals embedded in colored Gaussian noise. However, since spontaneous EEG signals are often corrupted by high-energy super-Gaussian artifacts, which deviate from this distribution, we propose rejecting these artifacts before applying MSTMF. Particularly effective have been algorithms based on independent component analysis (ICA) and empirical mode decomposition (EMD). After artifacts rejection, performed locally within time segments they occupy, without disturbing other parts of the signal, the classification of brain responses becomes more accurate. Nevertheless, the nonstationarity of the EEG signal remains a challenge that must be addressed.</div><div>Therefore, we propose adjusting the MSTMF to the current noise properties to improve its performance in this demanding environment. This can be achieved by properly calculating the noise covariance matrix, which is necessary to determine the filter coefficients, using both the learning and currently processed signal segments.</div><div>As a result, we have developed an enhanced method based on MSTMF for improved discrimination of evoked potentials and verified its performance on two publicly available reference databases: BCIAUT-P300 (for IFMBE Scientific Challenge) and Speller (for the BCI Competition III Challenge 2004). For these databases, we have achieved overall accuracies of 92.67% and 99.5%, surpassing the reference methods presented in the literature.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 34-51"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093052","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}
Eye diseases such as age-related macular degeneration (AMD) and diabetic retinopathy are common worldwide and affect millions of people. These conditions can cause severe vision problems and even lead to blindness if not treated promptly. Therefore, accurate and timely diagnosis is crucial to manage these diseases effectively and prevent irreversible vision loss. This study introduces a computer-aided diagnosis (CAD) framework for automatically detecting various eye diseases via advanced methodologies and datasets. The main focus is on classifying fundus images, which is essential for precise diagnosis and prognosis. By incorporating cutting-edge techniques such as Vision Transformers (ViTs), this study aims to improve the performance and interpretability of traditional Convolutional Neural Networks (CNNs). ViTs can capture complex patterns and long-range dependencies in fundus images, helping distinguish between different eye diseases and healthy conditions. Furthermore, the study integrates SHapley additive exPlanations (SHAP) explainability techniques to provide insights into the model’s decision-making process, enhancing trust and understanding of its predictions. The results demonstrate significant performance enhancements compared with the baseline models, with an overall accuracy of 95%. This method outperforms previous state-of-the-art methods by a considerable margin. Additionally, metrics such as precision, recall, intersection over union (IoU), and the Matthews correlation coefficient (MCC) show superior performance across various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. These findings underscore the effectiveness and reliability of the proposed approach in automated eye disease detection, indicating its potential for clinical integration and widespread adoption in healthcare settings.
{"title":"Advancing eye disease detection: A comprehensive study on computer-aided diagnosis with vision transformers and SHAP explainability techniques","authors":"Hossam Magdy Balaha , Asmaa El-Sayed Hassan , Rawan Ayman Ahmed , Magdy Hassan Balaha","doi":"10.1016/j.bbe.2024.11.005","DOIUrl":"10.1016/j.bbe.2024.11.005","url":null,"abstract":"<div><div>Eye diseases such as age-related macular degeneration (AMD) and diabetic retinopathy are common worldwide and affect millions of people. These conditions can cause severe vision problems and even lead to blindness if not treated promptly. Therefore, accurate and timely diagnosis is crucial to manage these diseases effectively and prevent irreversible vision loss. This study introduces a computer-aided diagnosis (CAD) framework for automatically detecting various eye diseases via advanced methodologies and datasets. The main focus is on classifying fundus images, which is essential for precise diagnosis and prognosis. By incorporating cutting-edge techniques such as Vision Transformers (ViTs), this study aims to improve the performance and interpretability of traditional Convolutional Neural Networks (CNNs). ViTs can capture complex patterns and long-range dependencies in fundus images, helping distinguish between different eye diseases and healthy conditions. Furthermore, the study integrates SHapley additive exPlanations (SHAP) explainability techniques to provide insights into the model’s decision-making process, enhancing trust and understanding of its predictions. The results demonstrate significant performance enhancements compared with the baseline models, with an overall accuracy of 95%. This method outperforms previous state-of-the-art methods by a considerable margin. Additionally, metrics such as precision, recall, intersection over union (IoU), and the Matthews correlation coefficient (MCC) show superior performance across various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. These findings underscore the effectiveness and reliability of the proposed approach in automated eye disease detection, indicating its potential for clinical integration and widespread adoption in healthcare settings.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 23-33"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093053","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 : 2025-01-01DOI: 10.1016/j.bbe.2024.12.001
Min-Kyoung Kang , Keum-Shik Hong , Dalin Yang , Ho Kyung Kim
Mild cognitive impairment (MCI) is recognized as an early stage preceding Alzheimer’s disease. Functional near-infrared spectroscopy (fNIRS) has recently been used to differentiate MCI patients from healthy controls (HCs) by analyzing their hemodynamic responses. This paper proposes a new method that uses the entire time series data from all fNIRS channels, skipping the feature extraction step. It involves a multi-scale convolutional neural network (CNN) integrated with long short-term memory (LSTM) layers to extract spatial and temporal features simultaneously. The study involves 64 participants (37 MCI patients and 27 HCs) performing three mental tasks: N-back, Stroop, and verbal fluency tests (VFT). The algorithm’s performance was assessed using 10-fold cross-validation across oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT). The highest classification accuracies were achieved with HbT, reaching 93.22 % for the N-back task, 91.14 % for the Stroop task, and 89.58 % for the VFT. It was found that using all types of hemodynamic signals from all channels provides better results than analyzing the region of interest data, eliminating the need for data segmentation and feature extraction procedures. Additionally, HbR (or HbT) gives better classification accuracy than HbO. The developed method can be implemented online for clinical applications and real-time monitoring of cognitive disorders.
{"title":"Multi-scale neural networks classification of mild cognitive impairment using functional near-infrared spectroscopy","authors":"Min-Kyoung Kang , Keum-Shik Hong , Dalin Yang , Ho Kyung Kim","doi":"10.1016/j.bbe.2024.12.001","DOIUrl":"10.1016/j.bbe.2024.12.001","url":null,"abstract":"<div><div>Mild cognitive impairment (MCI) is recognized as an early stage preceding Alzheimer’s disease. Functional near-infrared spectroscopy (fNIRS) has recently been used to differentiate MCI patients from healthy controls (HCs) by analyzing their hemodynamic responses. This paper proposes a new method that uses the entire time series data from all fNIRS channels, skipping the feature extraction step. It involves a multi-scale convolutional neural network (CNN) integrated with long short-term memory (LSTM) layers to extract spatial and temporal features simultaneously. The study involves 64 participants (37 MCI patients and 27 HCs) performing three mental tasks: <em>N</em>-back, Stroop, and verbal fluency tests (VFT). The algorithm’s performance was assessed using 10-fold cross-validation across oxyhemoglobin (HbO), deoxyhemoglobin (HbR), and total hemoglobin (HbT). The highest classification accuracies were achieved with HbT, reaching 93.22 % for the <em>N</em>-back task, 91.14 % for the Stroop task, and 89.58 % for the VFT. It was found that using all types of hemodynamic signals from all channels provides better results than analyzing the region of interest data, eliminating the need for data segmentation and feature extraction procedures. Additionally, HbR (or HbT) gives better classification accuracy than HbO. The developed method can be implemented online for clinical applications and real-time monitoring of cognitive disorders.</div></div>","PeriodicalId":55381,"journal":{"name":"Biocybernetics and Biomedical Engineering","volume":"45 1","pages":"Pages 11-22"},"PeriodicalIF":5.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143093055","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号