Mima Samir Jreije, Z. A. K. E. balaa, H. E. Balaa, J. Charara, W. Abdallah, M. Hilal, J. Foulquier, E. Touboul
{"title":"HDR brachytherapy, risk analysis and dose evaluation for operators in case of source blockage","authors":"Mima Samir Jreije, Z. A. K. E. balaa, H. E. Balaa, J. Charara, W. Abdallah, M. Hilal, J. Foulquier, E. Touboul","doi":"10.1109/ICABME.2017.8167523","DOIUrl":"https://doi.org/10.1109/ICABME.2017.8167523","url":null,"abstract":"The following article presents the results of a risk analysis exploring potential exposure of medical operators working in a radiotherapy and brachytherapy department. The presented work takes its particularity since the brachytherapy treatment takes place in a common bunker for radiotherapy and brachytherapy. A detailed study of different scenarios involving source blockage is presented. A detailed dosimetric study for operators in case of source blockage is developed.","PeriodicalId":426559,"journal":{"name":"2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127085923","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}
E. Wasserman, N. Kartashev, S. Roudnitsky, O. Zhvalevsky
{"title":"Aggregation, integration, or full-fledged cyber-physical system? Way of researcher in biomedicine","authors":"E. Wasserman, N. Kartashev, S. Roudnitsky, O. Zhvalevsky","doi":"10.1109/ICABME.2017.8167524","DOIUrl":"https://doi.org/10.1109/ICABME.2017.8167524","url":null,"abstract":"Some approaches and techniques that allow conducting of complex physiological research using closed-architecture measurement systems of different manufacturers are discussed. The experience in developing a system with flexible configuration and in solving arising problems by virtue of integration of equipment using an external synchronizing device, is described. Parallels are drawn with the modern technological trend — medical cyber-physical systems.","PeriodicalId":426559,"journal":{"name":"2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114725409","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}
{"title":"Classification of normal and abnormal heart sounds","authors":"Mohammad H. Nassralla, Z. Zein, Hazem M. Hajj","doi":"10.1109/ICABME.2017.8167538","DOIUrl":"https://doi.org/10.1109/ICABME.2017.8167538","url":null,"abstract":"Heart hemodynamic status and detection of a cardiovascular disease can be evaluated by analyzing and visualizing the heart waveform through graphs called the Phonocardiogram (PCG). The normal sounds of the heart generate signals that are in the audible frequency range of the human ear. Due to the significance of cardiac auscultation for recognizing pathological cardiac status, there has been special interest in automating the classification of heart sounds in the past years. The objective of this research is to present an automatic classification algorithm for anomaly (normal vs. abnormal heart status) of PCG recordings. For this purpose, distinctive time and frequency features are extracted out of heart sound signals to build a learning model using random forest. The accuracy of the proposed algorithm is about 12% better than state of the art.","PeriodicalId":426559,"journal":{"name":"2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128075682","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}
{"title":"Boosting-based decision tree for improved screening of vibroarthrographic signals","authors":"Ali H. Al-timemy","doi":"10.1109/ICABME.2017.8167551","DOIUrl":"https://doi.org/10.1109/ICABME.2017.8167551","url":null,"abstract":"Many diseases affect the knee joint, such as Chondromalica Pattelle (CP), which is the most bearing joint in the body. X-ray, MRI and arthroscopy are currently used for screening knee joint diseases. However, some of these techniques may be costly, dangerous as well as some of them being poor in functional resolution. On the other hand, researchers have shown the existence of variation in Vibroarthrography signal, recorded from the knee joint surface, between the normal and abnormal knee. VAG is the recording of vibrations generated from the knee joint surface, during flexion and extension, which may offer a tool of non-invasive screening for knee joint diseases. The main aim of this paper is to improve the VAG signal classification to diagnose CP. Simple time-domain features were used for the first time alongside boosting-based Decision Tree classifier. The area under the receiver operating characteristic curves was 0.816 which shows the effectiveness of the proposed features and boosting-based classifier compared to other methods.","PeriodicalId":426559,"journal":{"name":"2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132971049","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}
Saeed Zahran, C. Marque, Mahmoud Hassan, M. Yochum, N. Nader, W. Falou, M. Khalil
{"title":"Graph analysis of uterine networks using EHG source connectivity","authors":"Saeed Zahran, C. Marque, Mahmoud Hassan, M. Yochum, N. Nader, W. Falou, M. Khalil","doi":"10.1109/ICABME.2017.8167554","DOIUrl":"https://doi.org/10.1109/ICABME.2017.8167554","url":null,"abstract":"Emerging evidence show that the connectivity analysis of the uterine signals is a powerful tool in characterizing pregnancy and labor contractions. Here, we present the results of studying the connectivity between uterine sources identified from the electrohysterogram (EHG) signals, which reflects the electrical activity of the uterine muscle. We started by evaluating the effect of the two key steps involved in EHG source connectivity processing: i) the algorithm used in the solution of the inverse problem and ii) the method used for the estimation of the functional connectivity. We evaluate three different inverse solutions (to reconstruct the dynamics of uterine sources) and three connectivity measures (to compute statistical couplings between the reconstructed sources). The networks obtained by each combination of the inverse/connectivity methods were compared to a reference network (ground truth) generated by the model. The method was then applied to real EHG signals in order to discriminate pregnancy and labor contractions.","PeriodicalId":426559,"journal":{"name":"2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"10 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132738022","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}
A. Kabbara, W. Falou, M. Khalil, Hassan Eid, Mahmoud Hassan
{"title":"A scalp-EEG network-based analysis of Alzheimer's disease patients at rest","authors":"A. Kabbara, W. Falou, M. Khalil, Hassan Eid, Mahmoud Hassan","doi":"10.1109/ICABME.2017.8167549","DOIUrl":"https://doi.org/10.1109/ICABME.2017.8167549","url":null,"abstract":"Most brain disorders including Alzheimer's disease (AD) are related to alterations in the normal brain network organization and function. Exploring these network alterations using non-invasive and easy to use technique is a topic of great interest. In this paper, we collected EEG resting-state data from AD patients and healthy control subjects. Functional connectivity between scalp EEG signals was quantified using the phase locking value (PLV) for 6 frequency bands, θ (4–8 Hz), α1(8–10 Hz), α2(10–13 Hz), ß(13–30 Hz), γ(30–45 Hz), and broad band (0.2–45 Hz). To assess the differences in network properties, graph-theoretical analysis was performed. AD patients showed decrease of mean connectivity, average clustering and global efficiency in the lower alpha band. Positive correlation between the cognitive score and the extracted graph measures was obtained, suggesting that EEG could be a promising technique to derive new biomarkers of AD diagnosis.","PeriodicalId":426559,"journal":{"name":"2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132701879","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}
{"title":"Classification of meaningful and meaningless visual objects: A graph similarity approach","authors":"A. Mheich, Mahmoud Hassan, F. Wendling","doi":"10.1109/ICABME.2017.8167542","DOIUrl":"https://doi.org/10.1109/ICABME.2017.8167542","url":null,"abstract":"Cognition involves dynamic reconfiguration of functional brain networks at sub-second time scale. A precise tracking of these reconfigurations to categorize visual objects remains elusive. Here, we use dense electroencephalography (EEG) data recorded during naming meaningful (tools, animals…) and scrambled objects from 20 healthy subjects. We combine technique for identifying functional brain networks and recently developed algorithm for estimating networks similarity to discriminate between the two categories. First, we showed that dynamic networks of both categories can be segmented into several brain network states (times windows with consistent brain networks) reflecting sequential information processing from object representation to reaction time. Second, using a network similarity algorithm, results showed high intra-category and very low inter-category values. An average accuracy of 76% was obtained at different brain network states.","PeriodicalId":426559,"journal":{"name":"2017 Fourth International Conference on Advances in Biomedical Engineering (ICABME)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196397","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}