Medical & Biological Engineering & Computing最新文献

{"title":"Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.","authors":"Xiangguo Yin, Caixiu Yang, Hui Dong, Jingting Liang, Mingxing Lin","doi":"10.1007/s11517-024-03193-x","DOIUrl":"10.1007/s11517-024-03193-x","url":null,"abstract":"<p><p>The uncalibrated brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP) can omit the training process and is closer to the practical application. Filter bank canonical correlation analysis (FBCCA), as a classical approach of uncalibrated SSVEP-based BCI, extracts the fundamental and harmonic ingredients through filter bank decomposition. Nevertheless, this method fails to fully leverage the temporal feature of the signal. The paper suggested utilizing reconstructed data with temporal delay in the computation of the canonical correlation coefficient, and the different combinations of the time-delayed embedding and FBCCA were discussed. We selected the data from seven participants in the Benchmark dataset for parameter optimization and evaluated the method across all participants. The experimental results showed that only embedding the time-delayed version into the first subband (FBdCCA) was better than embedding it into all subbands (FBdCCA(all)), and the accuracy of FBdCCA surpassed that of FBCCA significantly. This suggests that the approach of time-delayed embedding can further enhance the performance of FBCCA.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"355-363"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a spinopelvic complex finite element model for quantitative analysis of the biomechanical response of patients with degenerative spondylolisthesis.","authors":"Ziyang Liang, Xiaowei Dai, Weisen Li, Weimei Chen, Qi Shi, Yizong Wei, Qianqian Liang, Yuanfang Lin","doi":"10.1007/s11517-024-03218-5","DOIUrl":"10.1007/s11517-024-03218-5","url":null,"abstract":"<p><p>Research on degenerative spondylolisthesis (DS) has focused primarily on the biomechanical responses of pathological segments, with few studies involving muscle modelling in simulated analysis, leading to an emphasis on the back muscles in physical therapy, neglecting the ventral muscles. The purpose of this study was to quantitatively analyse the biomechanical response of the spinopelvic complex and surrounding muscle groups in DS patients using integrative modelling. The findings may aid in the development of more comprehensive rehabilitation strategies for DS patients. Two new finite element spinopelvic complex models with detailed muscles for normal spine and DS spine (L4 forwards slippage) modelling were established and validated at multiple levels. Then, the spinopelvic position parameters including peak stress of the lumbar isthmic-cortical bone, intervertebral discs, and facet joints; peak strain of the ligaments; peak force of the muscles; and percentage difference in the range of motion were analysed and compared under flexion-extension (F-E), lateral bending (LB), and axial rotation (AR) loading conditions between the two models. Compared with the normal spine model, the DS spine model exhibited greater stress and strain in adjacent biological tissues. Stress at the L4/5 disc and facet joints under AR and LB conditions was approximately 6.6 times greater in the DS spine model than in the normal model, the posterior longitudinal ligament peak strain in the normal model was 1/10 of that in the DS model, and more high-stress areas were found in the DS model, with stress notably transferring forwards. Additionally, compared with the normal spine model, the DS model exhibited greater muscle tensile forces in the lumbosacral muscle groups during F-E and LB motions. The psoas muscle in the DS model was subjected to 23.2% greater tensile force than that in the normal model. These findings indicated that L4 anterior slippage and changes in lumbosacral-pelvic alignment affect the biomechanical response of muscles. In summary, the present work demonstrated a certain level of accuracy and validity of our models as well as the differences between the models. Alterations in spondylolisthesis and the accompanying overall imbalance in the spinopelvic complex result in increased loading response levels of the functional spinal units in DS patients, creating a vicious cycle that exacerbates the imbalance in the lumbosacral region. Therefore, clinicians are encouraged to propose specific exercises for the ventral muscles, such as the psoas group, to address spinopelvic imbalance and halt the progression of DS.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"575-594"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EthoWatcher OS: improving the reproducibility and quality of categorical and morphologic/kinematic data from behavioral recordings in laboratory animals.","authors":"João Antônio Marcolan, José Marino-Neto","doi":"10.1007/s11517-024-03212-x","DOIUrl":"10.1007/s11517-024-03212-x","url":null,"abstract":"<p><p>Behavioral recordings annotated by human observers (HOs) from video recordings are a fundamental component of preclinical animal behavioral models of neurobiological diseases. These models are often criticized for their vulnerability to reproducibility issues. Here, we present the EthoWatcher-Open Source (EW-OS), with tools and procedures for the use of blind-to-condition categorical transcriptions that are simultaneous with tracking, for the assessment of HOs intra- and interobserver reliability during training and data collection, for producing video clips of samples of behavioral categories that are useful for observer training. The use of these tools can inform and optimize the performance of observers, thus favoring the reproducibility of the data obtained. Categorical and machine vision-derived outputs are presented in an open data format for increased interoperability with other applications, where behavioral categories are associated frame-by-frame with tracking, morphological and kinematic attributes of an animal's image. The center of mass (X and Y pixel coordinates), the animal's area in square millimeters, the length and width in millimeters, and the angle in degrees were recorded. It also assesses the variation in each morphological descriptor to produce kinematic descriptors. While the initial measurements are in pixels, they are later converted to millimeters using the scale calibrated by the user via the graphical user interfaces. This process enables the creation of databases suitable for machine learning processing and behavioral pharmacology studies. EW-OS is constructed for continued collaborative development, available through an open-source platform, to support initiatives toward the adoption of good scientific practices in behavioral analysis, including tools for evaluating the quality of the data that can alleviate problems associated with low reproducibility in the behavioral sciences.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"511-523"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting mild cognitive impairment by applying integrated random forest to finger tapping.","authors":"Yuko Sano, Shota Suzumura, Junpei Sugioka, Tomohiko Mizuguchi, Akihiko Kandori, Izumi Kondo","doi":"10.1007/s11517-025-03306-0","DOIUrl":"https://doi.org/10.1007/s11517-025-03306-0","url":null,"abstract":"<p><p>Early detection of dementia is essential to reduce the decline in quality of life (QoL) and the increase in medical and nursing care costs associated with dementia in an aging society. In this study, we aimed to develop a simple screening test for mild cognitive impairment (MCI), a preliminary stage of dementia, by creating an analytical method to accurately detect MCI through finger-tapping measurement. We extracted 248 characteristics from the finger-tapping waveforms of 182 MCI patients and 352 normal controls, applying five conventional classification methods along with an improved Random Forest (RF) method proposed in this study (Integrated RF). In the proposed method, the RF classification model for the MCI and normal control groups is supplementally integrated with the RF classification model for the Alzheimer's disease and normal control groups to generate a new classification model. When comparing the discrimination accuracy of each method, the proposed method achieved the highest accuracy, with an F1-score of 0.795 (recall = 0.778 and precision = 0.814). These results demonstrate the potential of finger-tapping measurement as a highly accurate screening test for MCI.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preterm birth prediction from electrohysterogram using multivariate empirical mode decomposition.","authors":"Jiawen Cui, Xu Zhang, Xinhui Li, Xuanyu Luo, Xiang Chen, Zongzhi Yin","doi":"10.1007/s11517-025-03293-2","DOIUrl":"https://doi.org/10.1007/s11517-025-03293-2","url":null,"abstract":"<p><p>Electrohysterogram (EHG) is an electrophysiological signal describing uterine contractions that can be non-invasively measured on maternal abdominal surface. This signal contains vital physiological and pathological information for assessing delivery abnormalities, such as preterm birth. However, extracting information that effectively characterizes the association with abnormal delivery from the weak EHG signal is challenging. We present a preterm birth predicting method using multivariate empirical mode decomposition (MEMD) algorithm that adaptively decomposes multichannel EHG signals into different intrinsic mode functions (IMFs). MEMD maintains spectral consistency across channels and avoids mode-mixing problems across IMFs due to its powerful fine-grained signal structure decoupling capability. On this basis, a total of 180 features were extracted from the IMFs and the final eight features were chosen using a two-step feature selection algorithm. A support vector machine (SVM) classifier was employed for decision-making. Specifically, cost-sensitive algorithm was used to solve the data imbalance problem. The proposed method was evaluated using 300 EHG recordings in TPEHG database. The results show that our method outperforms other state-of-the-art methods in terms of sensitivity (85.16%), specificity (96.54%), <math> <msub><mrow><mi>F</mi> <mn>1</mn></mrow> <mtext>score</mtext></msub> </math> (91.04%), accuracy (94.36%), and AUC (97.31%). This study provides a powerful tool with wide applications for preterm birth risk diagnosis in clinical obstetric.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultra-short-term stress measurement using RGB camera-based remote photoplethysmography with reduced effects of Individual differences in heart rate.","authors":"Seungkeon Lee, Young Do Song, Eui Chul Lee","doi":"10.1007/s11517-024-03213-w","DOIUrl":"10.1007/s11517-024-03213-w","url":null,"abstract":"<p><p>Stress is linked to health problems, increasing the need for immediate monitoring. Traditional methods like electrocardiograms or contact photoplethysmography require device attachment, causing discomfort, and ultra-short-term stress measurement research remains inadequate. This paper proposes a method for ultra-short-term stress monitoring using remote photoplethysmography (rPPG). Previous predictions of ultra-short-term stress have typically used pulse rate variability (PRV) features derived from time-segmented heart rate data. However, PRV varies at the same stress levels depending on heart rates, necessitating a new method to account for these differences. This study addressed this by segmenting rPPG data based on normal-to-normal intervals (NNIs), converted from peak-to-peak intervals, to predict ultra-short-term stress indices. We used NNI counts corresponding to average durations of 10, 20, and 30 s (13, 26, and 39 NNIs) to extract PRV features, predicting the Baevsky stress index through regressors. The Extra Trees Regressor achieved R² scores of 0.6699 for 13 NNIs, 0.8751 for 26 NNIs, and 0.9358 for 39 NNIs, surpassing the time-segmented approach, which yielded 0.4162, 0.6528, and 0.7943 for 10, 20, and 30-s intervals, respectively. These findings demonstrate that using NNI counts for ultra-short-term stress prediction improves accuracy by accounting for individual bio-signal variations.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"497-510"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142401794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model.","authors":"Lizhi Pan, Zhongyi Ding, Haifeng Zhao, Ruinan Mu, Jianmin Li","doi":"10.1007/s11517-024-03207-8","DOIUrl":"10.1007/s11517-024-03207-8","url":null,"abstract":"<p><p>Human-machine interface (HMI) has been extensively developed and applied in rehabilitation. However, the performance of amputees on continuous movement decoding was significantly decreased compared with that of able-bodied individuals. To explore the impact of the absence of joint movements on the performance of HMI in rehabilitation, a generic musculoskeletal model (MM) was employed in this study to evaluate and compare the performance of subjects completing a series of on-line tasks with the wrist and metacarpophalangeal (MCP) joints unconstrained and constrained. The performance of the generic MM has been demonstrated in previous studies. The electromyography (EMG) signals of four muscles were employed as inputs of the generic MM to realize the continuous movement decoding of wrist and MCP joints. Ten able-bodied subjects were recruited to perform the on-line tasks. The completion time, the number of overshoots, and the path efficiency of the tasks were taken as the indexes to quantify the subjects' performance. The muscle activation associated with the movement was analyzed. Across all tasks and subjects, the average values of the three indexes with the joints unconstrained were 7.7 s, 0.59, and 0.38, respectively, while those with the joints constrained were 17.86 s, 1.47, and 0.22, respectively. The results demonstrated that the subjects performed better with the wrist and MCP joints unconstrained than with those joints constrained in the on-line tasks, suggesting that the absence of joint movements can be a reason of the decreased performance of continuous movement decoding with HMIs. Meanwhile, it is revealed that the different performance on motion behaviors is caused by the absence of joint movements.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"525-533"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated analysis of gene expressions and targeted mirnas for explaining crosstalk between oral and esophageal squamous cell carcinomas through an interpretable machine learning approach.","authors":"Khushi Yadav, Yasha Hasija","doi":"10.1007/s11517-024-03210-z","DOIUrl":"10.1007/s11517-024-03210-z","url":null,"abstract":"<p><p>This study explores the bidirectional relation of esophageal squamous cell carcinoma (ESCC) and oral squamous cell carcinoma (OSCC), examining shared risk factors and underlying molecular mechanisms. By employing random forest (RF) classifier, enhanced with interpretable machine learning (IML) through SHapley Additive exPlanations (SHAP), we analyzed gene expression from two GEO datasets (GSE30784 and GSE44021). The GSE30784 dataset comprises 167 OSCC samples and 45 control group, whereas the GSE44021 dataset encompasses 113 ESCC samples and 113 control group. Our analysis led to identification of 20 key genes, such as XBP1, VGLL1, and RAD1, which are significantly associated with development of ESCC and OSCC. Further investigations were conducted using tools like NetworkAnalyst 3.0, Single Cell Portal, and miRNET 2.0, which highlighted complex interactions between these genes and specific miRNA targets including hsa-mir-124-3p and hsa-mir-1-3p. Our model achieved high precision in identifying genes linked to crucial processes like programmed cell death and cancer pathways, suggesting new avenues for diagnosis and treatment. This study confirms the bidirectional relationship between OSCC and ESCC, laying groundwork for targeted therapeutic approaches. This study helps to identify shared biological pathways and genetic factors of these conditions for designing personalized medicine strategies and to improve disease management.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"483-495"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation of a virtual cohort of TAVI patients for in silico trials: a statistical shape and machine learning analysis.","authors":"Roberta Scuoppo, Salvatore Castelbuono, Stefano Cannata, Giovanni Gentile, Valentina Agnese, Diego Bellavia, Caterina Gandolfo, Salvatore Pasta","doi":"10.1007/s11517-024-03215-8","DOIUrl":"10.1007/s11517-024-03215-8","url":null,"abstract":"<p><strong>Purpose: </strong>In silico trials using computational modeling and simulations can complement clinical trials to improve the time-to-market of complex cardiovascular devices in humans. This study aims to investigate the significance of synthetic data in developing in silico trials for assessing the safety and efficacy of cardiovascular devices, focusing on bioprostheses designed for transcatheter aortic valve implantation (TAVI).</p><p><strong>Methods: </strong>A statistical shape model (SSM) was employed to extract uncorrelated shape features from TAVI patients, enabling the augmentation of the original patient population into a clinically validated synthetic cohort. Machine learning techniques were utilized not only for risk stratification and classification but also for predicting the physiological variability within the original patient population.</p><p><strong>Results: </strong>By randomly varying the statistical shape modes within a range of ± 2σ, a hundred virtual patients were generated, forming the synthetic cohort. Validation against the original patient population was conducted using morphological measurements. Support vector machine regression, based on selected shape modes (principal component scores), effectively predicted the peak pressure gradient across the stenosis (R-squared of 0.551 and RMSE of 11.67 mmHg). Multilayer perceptron neural network accurately predicted the optimal device size for implantation with high sensitivity and specificity (AUC = 0.98).</p><p><strong>Conclusion: </strong>The study highlights the potential of integrating computational predictions, advanced machine learning techniques, and synthetic data generation to improve predictive accuracy and assess TAVI-related outcomes through in silico trials.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"467-482"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750893/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142401783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating deep learning techniques for optimal neurons counting and characterization in complex neuronal cultures.","authors":"Angel Rio-Alvarez, Pablo García Marcos, Paula Puerta González, Esther Serrano-Pertierra, Antonello Novelli, M Teresa Fernández-Sánchez, Víctor M González","doi":"10.1007/s11517-024-03202-z","DOIUrl":"10.1007/s11517-024-03202-z","url":null,"abstract":"<p><p>The counting and characterization of neurons in primary cultures have long been areas of significant scientific interest due to their multifaceted applications, ranging from neuronal viability assessment to the study of neuronal development. Traditional methods, often relying on fluorescence or colorimetric staining and manual segmentation, are time consuming, labor intensive, and prone to error, raising the need for the development of automated and reliable methods. This paper delves into the evaluation of three pivotal deep learning techniques: semantic segmentation, which allows for pixel-level classification and is solely suited for characterization; object detection, which focuses on counting and locating neurons; and instance segmentation, which amalgamates the features of the other two but employing more intricate structures. The goal of this research is to discern what technique or combination of those techniques yields the optimal results for automatic counting and characterization of neurons in images of neuronal cultures. Following rigorous experimentation, we conclude that instance segmentation stands out, providing superior outcomes for both challenges.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"545-560"},"PeriodicalIF":2.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}