2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)最新文献

Monitoring system of neuronal activity and moving activity without restraint using wireless EEG, NIRS and accelerometer 利用无线脑电图、近红外光谱和加速度计的神经活动和无约束运动监测系统

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-12-01 DOI: 10.1109/IECBES.2010.5742285

K. Iramina, Kana Matsuda, J. Ide, Y. Noguchi

{"title":"Monitoring system of neuronal activity and moving activity without restraint using wireless EEG, NIRS and accelerometer","authors":"K. Iramina, Kana Matsuda, J. Ide, Y. Noguchi","doi":"10.1109/IECBES.2010.5742285","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742285","url":null,"abstract":"In this study, our purpose is the development of measuring and monitoring system to investigate the brain activity in usual behavior without restraint. Using this system, we can obtain physiological information such as EEG, ECG and cerebral blood of the subject always and anywhere, even if subjects walk around or move freely. In this study, we applied this system to evaluate the concentration state of the child with special needs. We invited a 10-years-old-boy with mental retardation to cooperate in our study, and measured the EEG, ECG, NIRS and moving activity while he was in education program for 30 minutes. EEG electrodes were placed according to international 10–20 system at Fz (frontal cortex) and Pz (parietal cortex). The hemodynamic response was also measured at the forehead by NIRS. At the same time, we measured the electrocardiograph(ECG). After removing the body movement artifact from the EEG data, we analyzed the time-frequency response of EEG. As a result, we observed differences in the time-course of EEG power at alpha band (8–12 Hz) and theta band (13–20 Hz), between the concentrating-state (studying) and rest-state.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124002848","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: 6

Stress features identification from EEG signals using EEG Asymmetry & Spectral Centroids techniques 利用脑电不对称和谱质心技术识别脑电信号中的应力特征

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-12-01 DOI: 10.1109/IECBES.2010.5742273

N. Sulaiman, M. N. Taib, S. A. M. Aris, N. Hamid, S. Lias, Z. H. Murat

引用次数: 37

Application of computational fluid dynamics in assessing the hemodynamics in abdominal aortic aneurysms 计算流体动力学在腹主动脉瘤血流动力学评估中的应用

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-11-30 DOI: 10.1109/IECBES.2010.5742194

V. Paramasivam, K. Muthusamy, M. A. Abdul Kadir

{"title":"Application of computational fluid dynamics in assessing the hemodynamics in abdominal aortic aneurysms","authors":"V. Paramasivam, K. Muthusamy, M. A. Abdul Kadir","doi":"10.1109/IECBES.2010.5742194","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742194","url":null,"abstract":"Clinical applications of computational modelling is a fundamentally new approach in medical treatment planning and development of predictive methods. In case of cardiovascular disease, these methods could enable physicians to predict the risk of rupture and to determine the optimal hemodynamic conditions for an individual patient. Abdominal aortic aneurysm (AAA) is a common clinical problem. We present a computational simulation which can be used in the predictive medicine, especially in the diagnosis and treatment of AAAs. For this purpose, we developed a code that provides an integrated set of tools to model clinically relevant hemodynamic conditions important in predicting risk of rupture of AAAs. The blood flow dynamics was solved according to the incompressible Navier-Stokes equations for Newtonian fluids. The pulsatility of blood flow was considered. The computational application is based on the three-dimensional finite element method. A typical idealised fusiform AAA model was used to study the flow effects, flow-induced wall shear stresses and pressure. These three criterias play an important role in assessing the hemodynamics in AAAs.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127092374","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

An efficient method of biometric matching using interpolated ECG data 一种利用插值心电数据进行生物特征匹配的有效方法

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-11-30 DOI: 10.1109/IECBES.2010.5742255

K. Sidek, F. Sufi, I. Khalil, Dhiah Al-Shammary

引用次数: 13

Surface charge study on pollen with a simple microelectrophoresis instrumentation setup 用简易微电泳装置研究花粉的表面电荷

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-11-01 DOI: 10.1109/IECBES.2010.5742262

P. F. Lee, K. K. Cheong, Y.S. Hong, Y.Z. Chong

引用次数: 1

Electrocardiogram signal processing method for exact Heart Rate detection in physical activity monitoring system: Wavelet approach 运动监测系统中精确心率检测的心电图信号处理方法:小波法

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-11-01 DOI: 10.1109/IECBES.2010.5742234

U. Yoon, Yeonsik Noh, Young Myeon Han, Min Yong Kim, Jae Hoon Jung, I. Hwang, H. Yoon, I. Jeong

{"title":"Electrocardiogram signal processing method for exact Heart Rate detection in physical activity monitoring system: Wavelet approach","authors":"U. Yoon, Yeonsik Noh, Young Myeon Han, Min Yong Kim, Jae Hoon Jung, I. Hwang, H. Yoon, I. Jeong","doi":"10.1109/IECBES.2010.5742234","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742234","url":null,"abstract":"Physical Activity Monitoring is a device that can measure the human activity quantity quantitatively through Heart Rate detection in real time. R-Spike detection of ECG is required for this Heart Rate detection. Since Physical Activity Monitoring System is usually used during activity or exercise, however, signal measured in ECG System is contaminated by diverse noises. Diverse noises become the factors of failure in R-Spike detection. Such factors impede the exact HR detection. This paper suggests method to convolute wavelet function and scaling function as the optimum signal disposition method for optimum R-Spike detection. This method was compared with the R-Spike detection method that uses quadratic spline wavelet presented before. To verify performance of signal disposition method suggested in this paper, the ECG of noise stress test database (NSTDB) and MIT-Database were tested in combination. Then, the sensitivity of R-Spike detection rate for noise was also additionally tested by gradually lowering SNR of NSTDB. Then, it was verified through ECG signal that was actually measured in physical activity monitoring.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127286773","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: 6

Pressure loss for bifurcation geometry of the human lung airway 人肺气道分岔几何的压力损失

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-11-01 DOI: 10.1109/IECBES.2010.5742221

M. Kang, Jin-Won Lee

引用次数: 2

Application of modeling techniques to diabetes diagnosis 建模技术在糖尿病诊断中的应用

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-11-01 DOI: 10.1109/IECBES.2010.5742227

A. Aibinu, M. Salami, A. Shafie

{"title":"Application of modeling techniques to diabetes diagnosis","authors":"A. Aibinu, M. Salami, A. Shafie","doi":"10.1109/IECBES.2010.5742227","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742227","url":null,"abstract":"In recent times, the introduction of complex-valued neural networks (CVNN) has widened the scope and applications of real-valued neural network (RVNN) and parametric modeling techniques. In this paper, new expert systems for automatic diagnosis and classification of diabetes using CVNN and RVNN based parametric modeling approaches have been suggested. Application of complex data normalization technique converts the real valued input data to complex valued data (CVD) by the process of phase encoding over unity magnitude. CVNN learn the relationship between the input and output phase encoded data during training and the coefficients of Complex-valued autoregressive (CAR) model can be extracted from the complex-valued weights and coefficients of the trained network. Classification of the obtained CAR or RVAR model coefficients results in required distinct classes for diagnosis purpose. Similar operations can be performed for real-valued autoregressive technique except for CVD normalization. The effect of data normalization techniques, activation functions, learning rate, number of neurons in the hidden layer and the number of epoch using the suggested techniques on PIMA INDIA diabetes dataset have been evaluated in this paper. Results obtained compares favorably with earlier reported results.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128499015","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: 23

Adaptive Resonance Associative Memory for multi-channel emotion recognition 多通道情感识别的自适应共振联想记忆

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-11-01 DOI: 10.1109/IECBES.2010.5742261

S. C. Siow, C. Loo, A. Tan, W. S. Liew

引用次数: 3

A framework for decision tree-based method to index data from large protein sequence databases 基于决策树的大型蛋白质序列数据库数据索引方法框架

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES) Pub Date : 2010-11-01 DOI: 10.1109/IECBES.2010.5742212

K. Jaber, R. Abdullah, N. Rashid

{"title":"A framework for decision tree-based method to index data from large protein sequence databases","authors":"K. Jaber, R. Abdullah, N. Rashid","doi":"10.1109/IECBES.2010.5742212","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742212","url":null,"abstract":"Currently, the size of biological databases has increased significantly with the growing number of users and the rate of queries where some databases are of terabyte size. Hence, there is an increasing need to access databases at the fastest possible rate. Where biologists are concerned, the need is more of a means to fast, scalable and accuracy searching in biological databases. This may seem to be a simple task, given the speed of current available gigabytes processors. However, this is far from the truth as the growing number of data which are deposited into the database are ever increasing. Hence, searching the database becomes a difficult and time-consuming task. Here, the computer scientist can help to organize data in a way that allows biologists to quickly search existing information and to predict new entries. In this paper, a decision tree indexing method is presented. This method of indexing can effectively and rapidly retrieve all the similar proteins from a large database for a given protein query. A theoretical and conceptual frameworks is derived, based on published works using indexing techniques for different applications.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125254755","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