Pub Date : 2022-01-01DOI: 10.1504/ijmei.2022.10049364
Raman Yadav, Sharda Vashisth, Ranjit Varma
{"title":"Computational fluid dynamics analysis of carotid artery with different plaque shapes","authors":"Raman Yadav, Sharda Vashisth, Ranjit Varma","doi":"10.1504/ijmei.2022.10049364","DOIUrl":"https://doi.org/10.1504/ijmei.2022.10049364","url":null,"abstract":"","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117254954","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}
Pub Date : 2021-09-14DOI: 10.1504/IJMEI.2021.117735
Samla Gauri
The introduction of terahertz (THz) metamaterial biosensor to trace biomarker that induces adverse drug reaction is an ideal thought to overcome drug hypersensitivity reaction. In this study, THz metamaterial-based biosensor was designed mainly for pharmacogenetic screening to study cell behaviour towards prescribed dosage of drugs. The biosensor was designed in COMSOL multiphysics based on resonance vibrational frequency and dielectric material property of the particular biomarkers. The difference in resonance frequency of with and without sample input to the biosensor explained the function-ability of the biosensor, whereas the resonant frequency of normal cells and biomarkers was used to trace targeted biomarkers. In addition, permittivity of the substrate was modified to enhance the biosensor sensitivity and variable of dielectric constant of normal versus biomarkers was analysed as further confirmation study to trace the targeted biomarker. Thus, this study highlighted that the THz metamaterial biosensor has great potential as portable healthcare device for rapid and accurate biomarker analysis as well as diagnosis.
{"title":"Pilot study of THz metamaterial-based biosensor for pharmacogenetic screening","authors":"Samla Gauri","doi":"10.1504/IJMEI.2021.117735","DOIUrl":"https://doi.org/10.1504/IJMEI.2021.117735","url":null,"abstract":"The introduction of terahertz (THz) metamaterial biosensor to trace biomarker that induces adverse drug reaction is an ideal thought to overcome drug hypersensitivity reaction. In this study, THz metamaterial-based biosensor was designed mainly for pharmacogenetic screening to study cell behaviour towards prescribed dosage of drugs. The biosensor was designed in COMSOL multiphysics based on resonance vibrational frequency and dielectric material property of the particular biomarkers. The difference in resonance frequency of with and without sample input to the biosensor explained the function-ability of the biosensor, whereas the resonant frequency of normal cells and biomarkers was used to trace targeted biomarkers. In addition, permittivity of the substrate was modified to enhance the biosensor sensitivity and variable of dielectric constant of normal versus biomarkers was analysed as further confirmation study to trace the targeted biomarker. Thus, this study highlighted that the THz metamaterial biosensor has great potential as portable healthcare device for rapid and accurate biomarker analysis as well as diagnosis.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114911507","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}
Pub Date : 2021-02-03DOI: 10.1504/IJMEI.2021.10035355
A. Belaidi, Mohammed El Amine Abderrahim
Accessibility to information resources in health systems is a very important aspect. This article is about the protection of medical data and focused primarily on access control in health information systems. It is therefore a question of proposing a rigorous modelling allowing to take care of all the aspects related to the secure management of electronic health record. We proposed in the first time a model to the management of the electronic health record in the context of an Algerian health organisation. Based on this modelling and by using Or-BAC model, in a second time, we proposed a model of the access control to this electronic health record. The validation of this model using the MotOrBAC tool allowed us a safe passage to an implementable specification. As a result, we develop a set of simple and effective tools to support this aspect.
{"title":"Access control to the electronic health records: a case study of an Algerian health organisation","authors":"A. Belaidi, Mohammed El Amine Abderrahim","doi":"10.1504/IJMEI.2021.10035355","DOIUrl":"https://doi.org/10.1504/IJMEI.2021.10035355","url":null,"abstract":"Accessibility to information resources in health systems is a very important aspect. This article is about the protection of medical data and focused primarily on access control in health information systems. It is therefore a question of proposing a rigorous modelling allowing to take care of all the aspects related to the secure management of electronic health record. We proposed in the first time a model to the management of the electronic health record in the context of an Algerian health organisation. Based on this modelling and by using Or-BAC model, in a second time, we proposed a model of the access control to this electronic health record. The validation of this model using the MotOrBAC tool allowed us a safe passage to an implementable specification. As a result, we develop a set of simple and effective tools to support this aspect.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114742396","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}
Pub Date : 2021-01-11DOI: 10.1504/IJMEI.2021.113397
Jalal Al Alwan
Despite the abundance of researches relating children and asthma, the racial/ethnic influence on asthma threat have not been fully explained. The aim was to conduct a consistent and new study on a large-scale nationally representative data, including a minority group that has been usually eliminated from racial/ethnic literature. The 2011-2012 National Survey of Children Health (NSCH) dataset was utilised. Asthma was more prevalent among African-American children (22.9%) more than white American children 13.1% (p ≤ .0001). Analysis of the multivariate model revealed a greater risk of asthma for the black African American children comparatively to white American children (adjusted OR 0.522, 95% CI 0.459-0.595). Our findings indicated that childhood asthma was associated with racial/ethnic status, especially with children with low income level.
尽管有大量关于儿童和哮喘的研究，但种族/民族对哮喘威胁的影响尚未得到充分解释。其目的是对具有全国代表性的大规模数据进行一致的新研究，其中包括通常从种族/民族文献中被排除的少数群体。使用2011-2012年全国儿童健康调查(NSCH)数据集。非裔美国儿童哮喘患病率(22.9%)高于美国白人儿童(13.1%)(p≤0.0001)。多变量模型分析显示，与美国白人儿童相比，非洲裔黑人儿童患哮喘的风险更高(调整OR 0.522, 95% CI 0.459-0.595)。我们的研究结果表明，儿童哮喘与种族/民族状况有关，特别是与低收入儿童有关。
{"title":"The impact of income level on childhood asthma in the USA: a secondary analysis study during 2011-2012","authors":"Jalal Al Alwan","doi":"10.1504/IJMEI.2021.113397","DOIUrl":"https://doi.org/10.1504/IJMEI.2021.113397","url":null,"abstract":"Despite the abundance of researches relating children and asthma, the racial/ethnic influence on asthma threat have not been fully explained. The aim was to conduct a consistent and new study on a large-scale nationally representative data, including a minority group that has been usually eliminated from racial/ethnic literature. The 2011-2012 National Survey of Children Health (NSCH) dataset was utilised. Asthma was more prevalent among African-American children (22.9%) more than white American children 13.1% (p ≤ .0001). Analysis of the multivariate model revealed a greater risk of asthma for the black African American children comparatively to white American children (adjusted OR 0.522, 95% CI 0.459-0.595). Our findings indicated that childhood asthma was associated with racial/ethnic status, especially with children with low income level.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123202505","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}
Pub Date : 2021-01-11DOI: 10.1504/IJMEI.2021.113392
Sepide Farhang, A. H. Foruzan
We propose a method for visualisation of the human liver to represent nonlinear behaviour of the tissue and to preserve the object's volume. Our multi-scale model uses dynamic interconnections to keep the size of the gland. We introduce two new parameters to control the influence of an external force on the nonlinear material of the liver. Another novelty in the proposed method is to design a multi-dimension data structure which makes it possible to run our code on conventional CPUs and in real-time. We evaluated the proposed algorithm both quantitatively and qualitatively by synthetic and clinical data. Our model preserved 98.4% and 94.1% of a typical volume in small and large deformation, respectively. The run-time of our model was 0.115 second. Our model preserves the volume of a liver during both small and large deformations and our results are comparable with recent methods.
{"title":"A low-complexity volumetric model with dynamic inter-connections to represent human liver in surgical simulators","authors":"Sepide Farhang, A. H. Foruzan","doi":"10.1504/IJMEI.2021.113392","DOIUrl":"https://doi.org/10.1504/IJMEI.2021.113392","url":null,"abstract":"We propose a method for visualisation of the human liver to represent nonlinear behaviour of the tissue and to preserve the object's volume. Our multi-scale model uses dynamic interconnections to keep the size of the gland. We introduce two new parameters to control the influence of an external force on the nonlinear material of the liver. Another novelty in the proposed method is to design a multi-dimension data structure which makes it possible to run our code on conventional CPUs and in real-time. We evaluated the proposed algorithm both quantitatively and qualitatively by synthetic and clinical data. Our model preserved 98.4% and 94.1% of a typical volume in small and large deformation, respectively. The run-time of our model was 0.115 second. Our model preserves the volume of a liver during both small and large deformations and our results are comparable with recent methods.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124977471","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}
Pub Date : 2021-01-11DOI: 10.1504/IJMEI.2021.113394
S. Hasan, Ameya K. Kulkarni, Sebamai Parija, P. Dash
Epilepsy is the most common neurological disorder characterised by a sudden and recurrent neuronal firing in the brain. As EEG records the electrical activity of the brain so it helps to detect epilepsy of the subject. Early detection of epileptic seizure using EEG signal is most useful in several diagnoses. So aim of the work is to study and compare the different techniques used for feature extraction and classification algorithm. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow accurate and quick diagnose. In this paper, we present a review of significant researches where we can find most suitable method among existing members to improve computing efficiency and detect epilepsy of the subject efficiently and accurately with lesser computational time. The database which is publicly available at Bonn University is taken.
{"title":"A systematic review on detection and estimation algorithms of EEG signal for epilepsy","authors":"S. Hasan, Ameya K. Kulkarni, Sebamai Parija, P. Dash","doi":"10.1504/IJMEI.2021.113394","DOIUrl":"https://doi.org/10.1504/IJMEI.2021.113394","url":null,"abstract":"Epilepsy is the most common neurological disorder characterised by a sudden and recurrent neuronal firing in the brain. As EEG records the electrical activity of the brain so it helps to detect epilepsy of the subject. Early detection of epileptic seizure using EEG signal is most useful in several diagnoses. So aim of the work is to study and compare the different techniques used for feature extraction and classification algorithm. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow accurate and quick diagnose. In this paper, we present a review of significant researches where we can find most suitable method among existing members to improve computing efficiency and detect epilepsy of the subject efficiently and accurately with lesser computational time. The database which is publicly available at Bonn University is taken.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127961072","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}
Pub Date : 2020-10-15DOI: 10.1504/ijmei.2020.10032878
Nivaashini Mathappan, R. S. Soundariya, A. Natarajan, Sathish Kumar Gopalan
Breast tumour remains a most important reason of cancer fatality among women globally and most of them pass away due to delayed diagnosis. But premature recognition and anticipation can significantly diminish the chances of death. Risk detection of breast cancer is one of the major research areas in bioinformatics. Various experiments have been conceded to examine the fundamental grounds of breast tumour. Alternatively, it has already been verified that early diagnosis of tumour can give the longer survival chance to a patient. This paper aims at finding an efficient set of features for breast tumour prediction using deep learning approaches called restricted Boltzmann machine (RBM). The proposed framework diagnoses and analyses breast tumour patient's data with the help of deep neural network (DNN) classifier using the Wisconsin dataset of UCI machine learning repository and, thereafter assesses their performance in terms of measures like accuracy, precision, recall, F-measure, etc.
{"title":"Bio-medical analysis of breast cancer risk detection based on deep neural network","authors":"Nivaashini Mathappan, R. S. Soundariya, A. Natarajan, Sathish Kumar Gopalan","doi":"10.1504/ijmei.2020.10032878","DOIUrl":"https://doi.org/10.1504/ijmei.2020.10032878","url":null,"abstract":"Breast tumour remains a most important reason of cancer fatality among women globally and most of them pass away due to delayed diagnosis. But premature recognition and anticipation can significantly diminish the chances of death. Risk detection of breast cancer is one of the major research areas in bioinformatics. Various experiments have been conceded to examine the fundamental grounds of breast tumour. Alternatively, it has already been verified that early diagnosis of tumour can give the longer survival chance to a patient. This paper aims at finding an efficient set of features for breast tumour prediction using deep learning approaches called restricted Boltzmann machine (RBM). The proposed framework diagnoses and analyses breast tumour patient's data with the help of deep neural network (DNN) classifier using the Wisconsin dataset of UCI machine learning repository and, thereafter assesses their performance in terms of measures like accuracy, precision, recall, F-measure, etc.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122506728","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}
Pub Date : 2020-08-25DOI: 10.1504/ijmei.2021.10020669
Pankaj Nanglia, A. N. Mahajan, D. Rathee, S Kumar
Manual computation of lung cancer is a time taking process. In the medical industry, software aided detection (SAD) aims to optimise the classification process. This paper proposes lung cancer detection for computed tomography (CT) images. It uses speed up robust feature (SURF) for feature extraction, genetic algorithm (GA) for feature optimisation and feed forward back propagation (FFBP), neural network (NN) for classification. The training mechanism utilises 200 cancerous images and the proposed method results in 96% classification accuracy and 94.7% sensitivity. This paper also discusses the possible future modifications in the presented work.
{"title":"Lung cancer classification using feed forward back propagation neural network for CT images","authors":"Pankaj Nanglia, A. N. Mahajan, D. Rathee, S Kumar","doi":"10.1504/ijmei.2021.10020669","DOIUrl":"https://doi.org/10.1504/ijmei.2021.10020669","url":null,"abstract":"Manual computation of lung cancer is a time taking process. In the medical industry, software aided detection (SAD) aims to optimise the classification process. This paper proposes lung cancer detection for computed tomography (CT) images. It uses speed up robust feature (SURF) for feature extraction, genetic algorithm (GA) for feature optimisation and feed forward back propagation (FFBP), neural network (NN) for classification. The training mechanism utilises 200 cancerous images and the proposed method results in 96% classification accuracy and 94.7% sensitivity. This paper also discusses the possible future modifications in the presented work.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117007277","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}
Pub Date : 2020-05-07DOI: 10.1504/IJMEI.2020.10015912
R. Vinothkanna, N. Prabakaran, S. Sivakannan
To solve the issue of close human contact in biometric authentication system, relatively a new technique gait recognition is used. The human gait is a common feature for identifying the walking manner of the person during walking and it is viewed as significant indicator for gait function of individual for experimental and research setting. Gait symmetry is usually considered as function of locomotion between the changes of the body and its activities. An exclusive advantage of gait as a biometric is its latent for detection at a distance or at low resolution or when other biometrics might not be perceivable. In this paper we investigate the problem of people recognition by their gait. The coordination and cyclic nature of the body motion makes gait as unique characteristics of each individual, thus a good biometric identification approach. The purpose of this paper is to describe some of the problems that make it difficult to apply gait as a biometric identification and use recent literature to show suggestions being made to solve some of these technical issues.
{"title":"Investigation of problems faced during capturing of gait signals","authors":"R. Vinothkanna, N. Prabakaran, S. Sivakannan","doi":"10.1504/IJMEI.2020.10015912","DOIUrl":"https://doi.org/10.1504/IJMEI.2020.10015912","url":null,"abstract":"To solve the issue of close human contact in biometric authentication system, relatively a new technique gait recognition is used. The human gait is a common feature for identifying the walking manner of the person during walking and it is viewed as significant indicator for gait function of individual for experimental and research setting. Gait symmetry is usually considered as function of locomotion between the changes of the body and its activities. An exclusive advantage of gait as a biometric is its latent for detection at a distance or at low resolution or when other biometrics might not be perceivable. In this paper we investigate the problem of people recognition by their gait. The coordination and cyclic nature of the body motion makes gait as unique characteristics of each individual, thus a good biometric identification approach. The purpose of this paper is to describe some of the problems that make it difficult to apply gait as a biometric identification and use recent literature to show suggestions being made to solve some of these technical issues.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127879629","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}
Pub Date : 2020-05-07DOI: 10.1504/ijmei.2020.10029319
Smita Jhajharia, S. Verma, R. Kumar
Input feature processing is required for obtaining meaningful results for cancer prognosis. In this paper, the extended Kalman filter (EKF) and fuzzy K-means clustering algorithms have been combined into a hybrid algorithm with improved functionality, compared to either of the two separately. The proposed hybrid algorithm implements fuzzy K-means with support vector machine (SVM) coupled with an EKF for data filtering, working with from consecutive filtering and prediction cycles. Fuzzy membership functions are then calculated to map the labels with the attributes which is used by K-means to create a new modified set of attributes supplied to the SVM classifier, with lesser number of support vectors. The number of clusters is added into the training process as the input parameter except the kernel parameters and the SVM penalty factor. The approach was tested for various publicly available datasets like UCL, SEER and a real dataset compiled by the authors.
{"title":"An amalgamated prediction model for breast cancer detection using fuzzy features","authors":"Smita Jhajharia, S. Verma, R. Kumar","doi":"10.1504/ijmei.2020.10029319","DOIUrl":"https://doi.org/10.1504/ijmei.2020.10029319","url":null,"abstract":"Input feature processing is required for obtaining meaningful results for cancer prognosis. In this paper, the extended Kalman filter (EKF) and fuzzy K-means clustering algorithms have been combined into a hybrid algorithm with improved functionality, compared to either of the two separately. The proposed hybrid algorithm implements fuzzy K-means with support vector machine (SVM) coupled with an EKF for data filtering, working with from consecutive filtering and prediction cycles. Fuzzy membership functions are then calculated to map the labels with the attributes which is used by K-means to create a new modified set of attributes supplied to the SVM classifier, with lesser number of support vectors. The number of clusters is added into the training process as the input parameter except the kernel parameters and the SVM penalty factor. The approach was tested for various publicly available datasets like UCL, SEER and a real dataset compiled by the authors.","PeriodicalId":193362,"journal":{"name":"Int. J. Medical Eng. Informatics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122669050","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}
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