Medical & Biological Engineering & Computing最新文献

{"title":"The role of eye movement signals in non-invasive brain-computer interface typing system.","authors":"Xi Liu, Bingliang Hu, Yang Si, Quan Wang","doi":"10.1007/s11517-024-03070-7","DOIUrl":"10.1007/s11517-024-03070-7","url":null,"abstract":"<p><p>Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177411","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":"Numerical study of the induction of intratumoral apoptosis under microwave ablation by changing slot length of microwave coaxial antenna.","authors":"Hyunjung Kim, Donghyuk Kim","doi":"10.1007/s11517-024-03068-1","DOIUrl":"10.1007/s11517-024-03068-1","url":null,"abstract":"<p><p>Recent advances in technology have led to an increase in the detection of previously undetected deep-located tumor tissue. As a result, the medical field is using a variety of methods to treat deep-located tumors, and minimally invasive treatment techniques are being explored. In this study, therapeutic effect of microwave ablation (MWA) on tumor generated inside liver tissue was analyzed through numerical analysis. The distribution of electromagnetic fields in biological tissues emitted by microwave coaxial antenna (MCA) was calculated through the wave equation, and the thermal behavior of the tissue was analyzed through the Pennes bioheat equation. Among various treatment conditions constituting MWA, tumor radius and the slot length inside the MCA were changed, and the resulting treatment effect was quantitatively confirmed through three apoptotic variables. As a result, each tumor radius has optimal power condition for MWA, 2.6W, 2.4W, and 3.0W respectively. This study confirmed optimal therapeutic conditions for MWA. Three apoptotic variables were used to quantitatively identify apoptotic temperature maintenance inside tumor tissue and thermal damage to surrounding normal tissue. The findings of this study are expected to serve as a standard for treatment based on actual MWA treatment.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140137337","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":"Modeling and control of COVID-19 disease using deep reinforcement learning method.","authors":"Nazanin Ghazizadeh, Sajjad Taghvaei, Seyyed Arash Haghpanah","doi":"10.1007/s11517-024-03153-5","DOIUrl":"https://doi.org/10.1007/s11517-024-03153-5","url":null,"abstract":"<p><p>The prevalence of epidemics has been studied by researchers in various fields. In the last 2 years, the outbreak of COVID-19 has affected the health, economy, and industry of communities around the world and has caused the death of millions of people. Therefore, many researchers have tried to model and control the prevalence of this disease. In this article, the new SQEIAR model for the spread of the COVID-19 disease is provided, which, compared to previous models, explores the effects of additional interventions on the outbreak and incorporates a wider range of variables and parameters to enhance its accuracy and alignment with reality. These modifications in the model lead to a more rapid eradication and control of the disease. This model includes six variables of the group of susceptible, quarantined, exposed, symptomatic, asymptomatic, and recovered individuals and includes three control inputs such as quarantine of susceptible, vaccination, and treatments. In order to minimize symptomatic infectious individuals and susceptible individuals and also to reduce treatment, vaccination, and quarantine costs, an optimal control approach using the Deep Deterministic Policy Gradient (DDPG) method has been applied to the system. This algorithm is applied to the model in different cases of control inputs, and for each case, optimal control inputs are obtained. In the following, the number of deaths due to the disease and the total number of symptomatic infectious individuals for each of these optimal control cases has been calculated. The results of the implemented control structure demonstrated a reduction of 60% in the number of deaths and 74% in the number of symptomatically infected individuals compared to the uncontrolled model. Finally, to test the performance of the control system, noise was applied to the system in various ways, including three methods: applying noise to observer variables, applying noise to control inputs, and applying uncertainty to model parameters. Therefore, we found that this control system was robust and performed well in different conditions despite the disturbance.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141471957","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":"Continuous mobile measurement of camptocormia angle using four accelerometers.","authors":"K Naderi Beni, K Knutzen, J P Kuhtz-Buschbeck, N G Margraf, R Rieger","doi":"10.1007/s11517-024-03149-1","DOIUrl":"https://doi.org/10.1007/s11517-024-03149-1","url":null,"abstract":"<p><p>Camptocormia, a severe flexion deformity of the spine, presents challenges in monitoring its progression outside laboratory settings. This study introduces a customized method utilizing four inertial measurement unit (IMU) sensors for continuous recording of the camptocormia angle (CA), incorporating both the consensual malleolus and perpendicular assessment methods. The setup is wearable and mobile and allows measurements outside the laboratory environment. The practicality for measuring CA across various activities is evaluated for both the malleolus and perpendicular method in a mimicked Parkinson disease posture. Multiple activities are performed by a healthy volunteer. Measurements are compared against a camera-based reference system. Results show an overall root mean squared error (RMSE) of 4.13° for the malleolus method and 2.71° for the perpendicular method. Furthermore, patient-specific calibration during the standing still with forward lean activity significantly reduced the RMSE to 2.45° and 1.68° respectively. This study presents a novel approach to continuous CA monitoring outside the laboratory setting. The proposed system is suitable as a tool for monitoring the progression of camptocormia and for the first time implements the malleolus method with IMU. It holds promise for effectively monitoring camptocormia at home.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460384","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":"Multi-file dynamic compression method based on classification algorithm in DNA storage.","authors":"Kun Bi, Qi Xu, Xin Lai, Xiangwei Zhao, Zuhong Lu","doi":"10.1007/s11517-024-03156-2","DOIUrl":"https://doi.org/10.1007/s11517-024-03156-2","url":null,"abstract":"<p><p>The exponential growth in data volume has necessitated the adoption of alternative storage solutions, and DNA storage stands out as the most promising solution. However, the exorbitant costs associated with synthesis and sequencing impeded its development. Pre-compressing the data is recognized as one of the most effective approaches for reducing storage costs. However, different compression methods yield varying compression ratios for the same file, and compressing a large number of files with a single method may not achieve the maximum compression ratio. This study proposes a multi-file dynamic compression method based on machine learning classification algorithms that selects the appropriate compression method for each file to minimize the amount of data stored into DNA as much as possible. Firstly, four different compression methods are applied to the collected files. Subsequently, the optimal compression method is selected as a label, as well as the file type and size are used as features, which are put into seven machine learning classification algorithms for training. The results demonstrate that k-nearest neighbor outperforms other machine learning algorithms on the validation set and test set most of the time, achieving an accuracy rate of over 85% and showing less volatility. Additionally, the compression rate of 30.85% can be achieved according to k-nearest neighbor model, more than 4.5% compared to the traditional single compression method, resulting in significant cost savings for DNA storage in the range of $0.48 to 3 billion/TB. In comparison to the traditional compression method, the multi-file dynamic compression method demonstrates a more significant compression effect when compressing multiple files. Therefore, it can considerably decrease the cost of DNA storage and facilitate the widespread implementation of DNA storage technology.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141452048","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":"VascuConNet: an enhanced connectivity network for vascular segmentation.","authors":"Muwei Jian, Ronghua Wu, Wenjin Xu, Huixiang Zhi, Chen Tao, Hongyu Chen, Xiaoguang Li","doi":"10.1007/s11517-024-03150-8","DOIUrl":"https://doi.org/10.1007/s11517-024-03150-8","url":null,"abstract":"<p><p>Medical image segmentation commonly involves diverse tissue types and structures, including tasks such as blood vessel segmentation and nerve fiber bundle segmentation. Enhancing the continuity of segmentation outcomes represents a pivotal challenge in medical image segmentation, driven by the demands of clinical applications, focusing on disease localization and quantification. In this study, a novel segmentation model is specifically designed for retinal vessel segmentation, leveraging vessel orientation information, boundary constraints, and continuity constraints to improve segmentation accuracy. To achieve this, we cascade U-Net with a long-short-term memory network (LSTM). U-Net is characterized by a small number of parameters and high segmentation efficiency, while LSTM offers a parameter-sharing capability. Additionally, we introduce an orientation information enhancement module inserted into the model's bottom layer to obtain feature maps containing orientation information through an orientation convolution operator. Furthermore, we design a new hybrid loss function that consists of connectivity loss, boundary loss, and cross-entropy loss. Experimental results demonstrate that the model achieves excellent segmentation outcomes across three widely recognized retinal vessel segmentation datasets, CHASE_DB1, DRIVE, and ARIA.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141428092","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":"Proximal cementation of a collarless polished tapered hip stem: biomechanical analysis using a validated finite element model.","authors":"Carol Sze Yee Ling, Aiman Izmin, Mitsugu Todo, Azhar M Merican, Desmond Y R Chong","doi":"10.1007/s11517-024-03152-6","DOIUrl":"https://doi.org/10.1007/s11517-024-03152-6","url":null,"abstract":"<p><p>Total hip replacement (THR) with cemented stem is a common procedure for patients with hip osteoarthritis. When primary THR fails, removal of the cement is problematic and poses challenges during revision surgeries. The possibility of proximal partial cementing of the hip stem was explored to mitigate the problem. 3D finite element analysis was performed to investigate the feasibility of reduced cement length for effective implant fixation and load transmission. Three levels of cement reduction (40 mm, 80 mm, and 100 mm) in the femoral stem were evaluated. All models were assigned loadings of peak forces acting on the femur during walking and stair climbing. The experimental and predicted max/min principal bone strains were fitted into regression models and showed good correlations. FE results indicated stress increment in the femoral bone, stem, and cement due to cement reduction. A notable increase of bone stress was observed with large cement reduction of 80-100 mm, particularly in Gruen zones 3 and 5 during walking and Gruen zones 3 and 6 during stair climbing. The increase of cement stresses could be limited to 11% with a cement reduction of 40 mm. The findings suggested that a 40-mm cement reduction in hip stem fixation was desirable to avoid unwanted complications after cemented THR.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141428091","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":"SMILEY-assistive application to support social and emotional skills in SPCD individuals.","authors":"Muskan Chawla, Surya Narayan Panda, Vikas Khullar","doi":"10.1007/s11517-024-03151-7","DOIUrl":"https://doi.org/10.1007/s11517-024-03151-7","url":null,"abstract":"<p><p>According to the available studies, mobile applications have provided significant support in improving the diverse skills of special individuals with social pragmatic communication disorder (SPCD). Over the last decade, SPCD has affected 8 to 11% of individuals, and therapy sessions cost between $50 and $150 per hour. This preliminary study aims to develop an interactive, user-friendly intervention to enhance social and emotional interaction skills in individuals with SPCD. The proposed intervention is an Android application that enhances social and emotional interaction skills. This pilot study involved 29 human subjects aged 7-13 years with pragmatic communication deficits. In a randomized controlled trial, the intervention was developed and implemented with consideration of caregiver and professional requirements. The improvement was analyzed using standard scales, including the Social Communication Questionnaire (SCQ) and the Social Communication Disorder Scale (SCDS). Moreover, the outcomes were examined through statistical parameters (mean, standard deviation) and tests (t-test). The intervention significantly improved the social and emotional skills of individuals with deficits. Before using the intervention, the identified statistical values for SCQ (mean = 6.48 and standard deviation = 3.37) and SCDS (mean = 8.17 and standard deviation = 4.79). However, after using the intervention, values for SCQ (mean = 8.24 and standard deviation = 3.95) and SCDS (mean = 9.48 and standard deviation = 4.72) were improved in comparison to the before-intervention outcome. The evaluation of the t-scores and p-values indicates that there has been significant improvement in the performance of individuals after the successful completion of the intervention. The proposed and applied intervention resulted in a significant impact in terms of improvement in social and emotional skills. The study concluded that it allows individuals to practice social and emotional interaction skills in a structured, controlled, and interactive environment. The proposed intervention has been found acceptable as per the reviews of caregivers and professionals, based on essential criteria including user experience, usability, interactive nature, reliability, and creditability.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421648","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":"Classification of Parkinson's disease severity using gait stance signals in a spatiotemporal deep learning classifier.","authors":"Brenda G Muñoz-Mata, Guadalupe Dorantes-Méndez, Omar Piña-Ramírez","doi":"10.1007/s11517-024-03148-2","DOIUrl":"https://doi.org/10.1007/s11517-024-03148-2","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332364","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":"Multi-label classification of retinal diseases based on fundus images using Resnet and Transformer.","authors":"Jiaqing Zhao, Jianfeng Zhu, Jiangnan He, Guogang Cao, Cuixia Dai","doi":"10.1007/s11517-024-03144-6","DOIUrl":"https://doi.org/10.1007/s11517-024-03144-6","url":null,"abstract":"<p><p>Retinal disorders are a major cause of irreversible vision loss, which can be mitigated through accurate and early diagnosis. Conventionally, fundus images are used as the gold diagnosis standard in detecting retinal diseases. In recent years, more and more researchers have employed deep learning methods for diagnosing ophthalmic diseases using fundus photography datasets. Among the studies, most of them focus on diagnosing a single disease in fundus images, making it still challenging for the diagnosis of multiple diseases. In this paper, we propose a framework that combines ResNet and Transformer for multi-label classification of retinal disease. This model employs ResNet to extract image features, utilizes Transformer to capture global information, and enhances the relationships between categories through learnable label embedding. On the publicly available Ocular Disease Intelligent Recognition (ODIR-5 k) dataset, the proposed method achieves a mean average precision of 92.86%, an area under the curve (AUC) of 97.27%, and a recall of 90.62%, which outperforms other state-of-the-art approaches for the multi-label classification. The proposed method represents a significant advancement in the field of retinal disease diagnosis, offering a more accurate, efficient, and comprehensive model for the detection of multiple retinal conditions.</p>","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318749","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}