Bio-Algorithms and Med-Systems最新文献_第6页

Brain–computer interface for electric wheelchair based on alpha waves of EEG signal 基于脑电信号α波的电动轮椅脑机接口

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-09-01 DOI: 10.1515/bams-2021-0095

Kacper Banach, Mateusz Małecki, M. Rosół, A. Broniec

{"title":"Brain–computer interface for electric wheelchair based on alpha waves of EEG signal","authors":"Kacper Banach, Mateusz Małecki, M. Rosół, A. Broniec","doi":"10.1515/bams-2021-0095","DOIUrl":"https://doi.org/10.1515/bams-2021-0095","url":null,"abstract":"Abstract Objectives Helping patients suffering from serious neurological diseases that lead to hindering the independent movement is of high social importance and an interdisciplinary challenge for engineers. Brain–computer interface (BCI) interfaces based on the electroencephalography (EEG) signal are not easy to use as they require time consuming multiple electrodes montage. We aimed to contribute in bringing BCI systems outside the laboratories so that it could be more accessible to patients, by designing a wheelchair fully controlled by an algorithm using alpha waves and only a few electrodes. Methods The set of eight binary words are designed, that allow to move forward, backward, turn right and left, rotate 45° as well as to increase and decrease the speed of the wheelchair. Our project includes: development of a mobile application which is used as a graphical user interface, real-time signal processing of the EEG signal, development of electric wheelchair engines control system and mechanical construction. Results The average sensitivity, without training, was 79.58% and specificity 97.08%, on persons who had no previous contact with BCI. Conclusions The proposed system can be helpful for people suffering from incurable diseases that make them closed in their bodies and for whom communication with the surrounding world is almost impossible.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47731778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Blue-emitting polystyrene scintillators for plastic scintillation dosimetry 塑料闪烁剂量测定用蓝色发光聚苯乙烯闪烁体

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-09-01 DOI: 10.1515/bams-2021-0088

Ł. Kapłon, G. Moskal

引用次数: 11

Regularization based discriminative feature pattern selection for the classification of Parkinson cases using machine learning 基于正则化的判别特征模式选择用于机器学习的帕金森病例分类

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-08-19 DOI: 10.1515/bams-2021-0064

Kamalakannan Kaliyan, Anand Ganesan

{"title":"Regularization based discriminative feature pattern selection for the classification of Parkinson cases using machine learning","authors":"Kamalakannan Kaliyan, Anand Ganesan","doi":"10.1515/bams-2021-0064","DOIUrl":"https://doi.org/10.1515/bams-2021-0064","url":null,"abstract":"Abstract Objectives This paper focuses on developing a regularization-based feature selection approach to select the most effective attributes from the Parkinson’s speech dataset. Parkinson’s disease is a medical condition that progresses as the dopamine-producing nerve cells are affected. Early diagnosis often reduces the effect on the individuals, minimizes the advancement over time. In recent times, intelligent computational models are used in many complex cases to diagnose a clinical condition with high precision. These models are intended to find meaningful representation from the data to diagnose the disease. Machine learning acts as a tool, gears up the model learning process through a mathematical baseline. But, not in all cases, machine learning will be demanded to perform optimally. It comes with a few constraints, mainly the representation of the data. The learning models expect a clean, noise-free input, which in-turns produces better discriminative patterns over different categories of classes. Methods The proposed model identified five candidate features as predictors. This feature subset is trained with different varieties of supervised classifiers to trace out the best-performing model. Results The results are validated through accuracy, precision, recall, and receiver’s operational characteristic curves. The proposed regularization- based feature selection model outperformed the benchmark algorithms by attaining 100% accuracy on most of the classifiers, other than linear discriminant analysis (99.90%) and naïve Bayes (99.51%). Conclusions This paper exhibits the need for intelligent models to analyze complex data patterns to assist medical practitioners in better disease diagnosis. The results exhibit that the regularization methods find the best features based on their importance score, which improved the model performance over other feature selection methods.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43747534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Machine learning-based approach for segmentation of intervertebral disc degeneration from lumbar section of spine using MRI images 基于机器学习的腰椎椎间盘退变MRI图像分割方法

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-08-12 DOI: 10.1515/bams-2020-0047

Jayashri Shinde, Y. Joshi, R. Manthalkar, Joshi

{"title":"Machine learning-based approach for segmentation of intervertebral disc degeneration from lumbar section of spine using MRI images","authors":"Jayashri Shinde, Y. Joshi, R. Manthalkar, Joshi","doi":"10.1515/bams-2020-0047","DOIUrl":"https://doi.org/10.1515/bams-2020-0047","url":null,"abstract":"Abstract Objectives Intervertebral disc segmentation is one of the methods to diagnose spinal disease through the degeneration in asymptomatic and symptomatic patients. Even though numerous intervertebral disc segmentation techniques are available, classifying the grades in the intervertebral disc is a hectic challenge in the existing disc segmentation methods. Thus, an effective Whale Spine-Generative Adversarial Network (WSpine-GAN) method is proposed to segment the intervertebral disc for effective grade classification. Methods The proposed WSpine-GAN method effectively performs the disc segmentation, wherein the weights of Spine-GAN are optimally tuned using Whale Optimization Algorithm (WOA). Then, the refined disc features, such as pixel-based features and the connectivity features are extracted. Finally, the K-Nearest Neighbor (KNN) classifier based on the pfirrmann’s grading system performs the grade classification. Results The implementation of the grade classification strategy based on the proposed WSpine-GAN and KNN is performed using the real-time database, and the performance based on the metrics yielded the accuracy, true positive rate (TPR), and false positive rate (FPR) values of 97.778, 97.83, and 0.586% for the training percentage and 92.382, 90.580, and 1.972% for the K-fold value. Conclusions The proposed WSpine-GAN method effectively performs the disc segmentation by integrating the Spine-GANmethod and WOA. Here, the spinal cord images are segmented using the proposed WSpine-GAN method by tuning the weights optimally to enhance the performance of the disc segmentation.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42940821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling the C. elegans germline stem cell genetic network using automated reasoning 利用自动推理对秀丽隐杆线虫生殖系干细胞遗传网络进行建模

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-08-08 DOI: 10.1101/2021.08.08.455525

A. Amar, E. Hubbard, H. Kugler

{"title":"Modeling the C. elegans germline stem cell genetic network using automated reasoning","authors":"A. Amar, E. Hubbard, H. Kugler","doi":"10.1101/2021.08.08.455525","DOIUrl":"https://doi.org/10.1101/2021.08.08.455525","url":null,"abstract":"Computational methods and tools are a powerful complementary approach to experimental work for studying regulatory interactions in living cells and systems. We demonstrate the use of formal reasoning methods as applied to the Caenorhabditis elegans germ line, which is an accessible model system for stem cell research. The dynamics of the underlying genetic networks and their potential regulatory interactions are key for understanding mechanisms that control cellular decision-making between stem cells and differentiation. We model the “stem cell fate” versus entry into the “meiotic development” pathway decision circuit in the young adult germ line based on an extensive study of published experimental data and known/hypothesized genetic interactions. We apply a formal reasoning framework to derive predictive networks for control of differentiation. Using this approach we simultaneously specify many possible scenarios and experiments together with potential genetic interactions, and synthesize genetic networks consistent with all encoded experimental observations. In silico analysis of knock-down and overexpression experiments within our model recapitulate published phenotypes of mutant animals and can be applied to make predictions on cellular decision-making. This work lays a foundation for developing realistic whole tissue models of the C. elegans germ line where each cell in the model will execute a synthesized genetic network.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81559848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps 心脏疾病预测的机器学习方法综述:未来研究差距的方向

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-07-08 DOI: 10.1515/bams-2020-0069

Fathima. A. Vellameeran, T. Brindha

引用次数: 1

International Conference Cybernetic Modelling of Biological Systems MCSB 2021 生物系统控制论建模国际会议MCSB 2021

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-06-01 DOI: 10.1515/bams-2021-0050

Z. Wisniowski

引用次数: 1

Frontmatter

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-06-01 DOI: 10.1515/bams-2021-frontmatter2

引用次数: 0

Image retrieval of MRI brain tumour images based on SVM and FCM approaches 基于SVM和FCM方法的MRI脑肿瘤图像检索

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-05-24 DOI: 10.1515/bams-2021-0011

Sonia Bansal, Vineet Mehan

{"title":"Image retrieval of MRI brain tumour images based on SVM and FCM approaches","authors":"Sonia Bansal, Vineet Mehan","doi":"10.1515/bams-2021-0011","DOIUrl":"https://doi.org/10.1515/bams-2021-0011","url":null,"abstract":"Abstract Objectives The key test in Content-Based Medical Image Retrieval (CBMIR) frameworks for MRI (Magnetic Resonance Imaging) pictures is the semantic hole between the low-level visual data caught by the MRI machine and the elevated level data seen by the human evaluator. Methods The conventional component extraction strategies centre just on low-level or significant level highlights and utilize some handmade highlights to diminish this hole. It is important to plan an element extraction structure to diminish this hole without utilizing handmade highlights by encoding/consolidating low-level and elevated level highlights. The Fleecy gathering is another packing technique, which is applied in plan depiction here and SVM (Support Vector Machine) is applied. Remembering the predefinition of bunching amount and enlistment cross-section is until now a significant theme, a new predefinition advance is extended in this paper, in like manner, and another CBMIR procedure is suggested and endorsed. It is essential to design a part extraction framework to diminish this opening without using painstakingly gathered features by encoding/joining low-level and critical level features. Results SVM and FCM (Fuzzy C Means) are applied to the power structures. Consequently, the incorporate vector contains all the objectives of the image. Recuperation of the image relies upon the detachment among request and database pictures called closeness measure. Conclusions Tests are performed on the 200 Image Database. Finally, exploratory results are evaluated by the audit and precision.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2021-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2021-0011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45921227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Computational studies of the mitochondrial carrier family SLC25. Present status and future perspectives 线粒体载体家族SLC25的计算研究。现状与未来展望

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-04-27 DOI: 10.1515/bams-2021-0018

A. Pasquadibisceglie, F. Polticelli

{"title":"Computational studies of the mitochondrial carrier family SLC25. Present status and future perspectives","authors":"A. Pasquadibisceglie, F. Polticelli","doi":"10.1515/bams-2021-0018","DOIUrl":"https://doi.org/10.1515/bams-2021-0018","url":null,"abstract":"Abstract The members of the mitochondrial carrier family, also known as solute carrier family 25 (SLC25), are transmembrane proteins involved in the translocation of a plethora of small molecules between the mitochondrial intermembrane space and the matrix. These transporters are characterized by three homologous domains structure and a transport mechanism that involves the transition between different conformations. Mutations in regions critical for these transporters’ function often cause several diseases, given the crucial role of these proteins in the mitochondrial homeostasis. Experimental studies can be problematic in the case of membrane proteins, in particular concerning the characterization of the structure–function relationships. For this reason, computational methods are often applied in order to develop new hypotheses or to support/explain experimental evidence. Here the computational analyses carried out on the SLC25 members are reviewed, describing the main techniques used and the outcome in terms of improved knowledge of the transport mechanism. Potential future applications on this protein family of more recent and advanced in silico methods are also suggested.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/bams-2021-0018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43877219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2