2015 22nd Iranian Conference on Biomedical Engineering (ICBME)最新文献

Discrimination of mental tasks based on EEMD and information theoretic pattern selection 基于EEMD和信息论模式选择的心理任务判别

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-25 DOI: 10.1109/ICBME.2015.7404110

S. Noshadi, Abbas Ebrahimi Moghadam, M. Khademi

{"title":"Discrimination of mental tasks based on EEMD and information theoretic pattern selection","authors":"S. Noshadi, Abbas Ebrahimi Moghadam, M. Khademi","doi":"10.1109/ICBME.2015.7404110","DOIUrl":"https://doi.org/10.1109/ICBME.2015.7404110","url":null,"abstract":"In this paper, we address the discrimination of mental tasks problem and suggest a method based on Ensemble Empirical Mode Decomposition (EEMD), for time-frequency analysis, and a pattern selection method based on an information theoretic measure, namely; Jensen Shannon Divergence (JSD) measure. The method works in three steps: (i) to employ EEMD for EEG signal decomposition into components called Intrinsic Mode Functions (IMFs), followed by applying Hilbert transform to the IMFs to determine the instantaneous frequency and amplitude; (ii) to choose the IMFs containing the most significant information based on the degree of presence in gamma band; (iii) to select segments of instantaneous vectors according to JSD metric, which measures the distances between two concepts. This method was applied to EEG signals of 5 subjects performing 5 mental tasks. The classification of mental tasks was performed using Fisher linear discriminator. The experimental results are compared with the ones obtained by a method that uses the power of gamma band in EEG signals (a traditional and popular method). The experimental results show improvement of the classification accuracy.","PeriodicalId":127657,"journal":{"name":"2015 22nd Iranian Conference on Biomedical Engineering (ICBME)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133122824","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

An efficient method for extracting respiratory activity from single-lead-ECG based on variational mode decomposition 一种基于变分模分解的单导联心电图呼吸活动提取方法

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404141

M. Nazari, S. M. Sakhaei

引用次数: 3

Improving 2D block method in electrical impedance tomography 改进电阻抗层析二维块法

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404150

Saeed Zaravi, R. Amirfattahi, B. Vahdat, A. Hassanipour

引用次数: 0

Machine learning-based signal processing using physiological signals for stress detection 基于机器学习的信号处理，利用生理信号进行应力检测

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404123

A. Ghaderi, J. Frounchi, A. Farnam

{"title":"Machine learning-based signal processing using physiological signals for stress detection","authors":"A. Ghaderi, J. Frounchi, A. Farnam","doi":"10.1109/ICBME.2015.7404123","DOIUrl":"https://doi.org/10.1109/ICBME.2015.7404123","url":null,"abstract":"Stress is a common part of daily life which most people struggle in different occasions. However, having stress for a long time, or a high level of stress will jeopardize our safety, and will disrupt our normal life. Consequently, performance and management ability in critical situations degrade significantly. Therefore, it is necessary to have information in stress cognition and design systems with the ability of stress cognition. In this paper a signal processing approach is introduced based on machine learning algorithms. We used collected biological data such as Respiration, GSR Hand, GSR Foot, Heart Rate and EMG, from different subjects in different situations and places, while they were driving. Then, data segmentation for various time intervals such 100, 200 and 300 seconds is performed for different stress level. We extracted statistical features from the segmented data, and feed this features to the available classifier. We used KNN, K-nearest neighbor, and support vector machine which are the most common classifiers. We classified the stress into three levels: low, medium, and high. Our results show that the stress level can be detected by accuracy of 98.41% for 100 seconds and 200 seconds time intervals and 99% for 300 seconds time intervals.","PeriodicalId":127657,"journal":{"name":"2015 22nd Iranian Conference on Biomedical Engineering (ICBME)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122669860","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: 59

Placebo-induced changes in behavioural parameters during gait: a pilot study 安慰剂诱导的步态过程中行为参数的改变:一项初步研究

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404166

M. E. Andani, A. Mahdavi, F. Bahrami, M. Fiorio

引用次数: 1

Analysis of inter-hemispheric and intra-hemispheric differences of the correlation dimension in the emotional states based on EEG signals 基于脑电信号的情绪状态相关维数的半球间和半球内差异分析

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404106

S. Hatamikia, A. Nasrabadi, N. Shourie

引用次数: 1

Medical image fusion using discrete wavelet transform and lifting scheme 基于离散小波变换和提升方案的医学图像融合

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404158

Tannaz Akbarpour, M. Shamsi, S. Daneshvar

引用次数: 4

Analysis of backscattered ultrasound rf echoes from adjacent scan lines for surface roughness characterization: a phantom study 来自相邻扫描线的后向散射超声射频回波分析用于表面粗糙度表征:一个幻影研究

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404176

A. Jamzad, F. Akbarifar, S. Setarehdan

{"title":"Analysis of backscattered ultrasound rf echoes from adjacent scan lines for surface roughness characterization: a phantom study","authors":"A. Jamzad, F. Akbarifar, S. Setarehdan","doi":"10.1109/ICBME.2015.7404176","DOIUrl":"https://doi.org/10.1109/ICBME.2015.7404176","url":null,"abstract":"Ultrasonography, as a noninvasive and inexpensive routine, can be the modality of choice in roughness characterization of internal body stones for managing treatment methods. In this study, we have investigated the possibility of differentiating roughness levels utilizing backscattered RF data which presumably contain more information than the B-mode images. For this purpose, we modified a conventional medical ultrasound device and recorded RF data from a roughness phantom consisting of 4 standard sandpaper strips. We proposed that the difference of two echoes from adjacent scan lines contains information about the roughness of the imaging surface. Hence, we calculated the Euclidean distance of temporal and spectral features extracted from two adjacent echoes. Then, 3 classifiers of Bayesian, linear, and 1-Nearest Neighbor (NN) were employed for roughness differentiation. The results show that spectral features and 1-NN classifier had the best performance among others. The highest average performance of 99.17%, obtained using all features along with the 1-NN classifier, proves the feasibility of roughness discrimination by acquiring and comparing adjacent echoes.","PeriodicalId":127657,"journal":{"name":"2015 22nd Iranian Conference on Biomedical Engineering (ICBME)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132517765","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

Numerical investigation of role of synovial fluid in a poroelastic model of natural human knee joint during walking 滑液在人体自然膝关节孔弹性模型中作用的数值研究

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404129

Hossein Bahreinizad, H. N. Oscuii

引用次数: 2

A neural network system for diagnosis and assessment of tremor in parkinson disease patients 帕金森病患者震颤诊断与评估的神经网络系统

2015 22nd Iranian Conference on Biomedical Engineering (ICBME) Pub Date : 2015-11-01 DOI: 10.1109/ICBME.2015.7404105

Omid Bazgir, J. Frounchi, S. Habibi, Lorenzo Palma, P. Pierleoni

引用次数: 21