2012 Third International Conference on Emerging Applications of Information Technology最新文献_第2页

Automated detection of apnea and hypopnea events 自动检测呼吸暂停和低呼吸事件

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6407868

B. Koley, D. Dey

{"title":"Automated detection of apnea and hypopnea events","authors":"B. Koley, D. Dey","doi":"10.1109/EAIT.2012.6407868","DOIUrl":"https://doi.org/10.1109/EAIT.2012.6407868","url":null,"abstract":"This paper presents an automatic method for detection of apnea and hypopnea events occurred during sleep from the single channel recording of oronasal airflow signal. For the identification of events, three time domain measures were extracted from each of the overlapping short segment windows of respiration signal. The feature set includes area, upper 90th percentile and variance, which were used to characterize changes in the airflow signal during normal and abnormal breathing events (i.e., apnea, hypopnea). An ensemble of three binary Support Vector Machine (SVM) based classifiers arranged in one-against-all strategy, were used to classify the feature vector among three categories, according to its origin from some breathing events like normal, apnea and hypopnea. The consecutive decisions of classifier model on time sequenced consecutive overlapped windows were combined by some heuristic rules to identify abnormal breathing events from normal breathings. In this study, 14 polysomnography (PSG) recordings diagnosed as obstructive sleep apnea syndrome were analyzed. Independent test was performed on 6 recordings. The cross-validation and independent test accuracies of apneic event detection were found to be 93.3% and 92.8%, respectively. For hypopnea event these two accuracies were 90.1% and 89.6%. The proposed system can be used for home based monitoring of suspected apneic subject, and can count total number of apnea and hypopnea events occurred during sleep.","PeriodicalId":194103,"journal":{"name":"2012 Third International Conference on Emerging Applications of Information Technology","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131177381","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: 17

Modified Roaming Optimization for multi-modal optima 多模态优化的改进漫游优化

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6407861

C. Jada, H. Yenala, K. K. Rachavarapu, N. K. Chittipolu, S. Omkar

引用次数: 1

High-speed comparator architectures for fast binary comparison 用于快速二进制比较的高速比较器架构

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6408016

S. Deb, S. Chaudhury

引用次数: 9

A formal approach to information security metrics 信息安全度量的正式方法

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6408003

A. Chakraborty, A. Sengupta, C. Mazumdar

引用次数: 7

Mention detection and classification in bio-chemical domain using Conditional Random Field 提出了生化领域中条件随机场的检测与分类方法

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6407943

Asif Ekbal, S. Saha, K. Ravi

引用次数: 1

An environment monitoring interface using GRASS GIS and Python 一个使用GRASS GIS和Python的环境监测界面

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6407912

A. De Sarkar, N. Biyahut, S. Kritika, N. Singh

引用次数: 1

A new self-tuning fuzzy proportional-derivative controller for high-order systems 一种新的高阶系统自整定模糊比例导数控制器

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6407853

R. Mudi, R. R. De (Maity)

引用次数: 1

Study of inter-session variability of long term memory and complexity of EEG signals 长时记忆的会话间变异性与脑电信号复杂性研究

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6407873

S. Chatterjee, S. Bhattacharyya, A. Khasnobish, A. Konar, D. Tibarewala, R. Janarthanan

引用次数: 2

Materialized view construction using linear regression on attributes 在属性上使用线性回归的物化视图构造

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6407899

P. Ghosh, S. Sen, N. Chaki

引用次数: 9

Region selection in handwritten character recognition using Artificial Bee Colony Optimization 基于人工蜂群优化的手写字符识别区域选择

2012 Third International Conference on Emerging Applications of Information Technology Pub Date : 2012-11-01 DOI: 10.1109/EAIT.2012.6407891

A. Roy, N. Das, R. Sarkar, S. Basu, M. Kundu, M. Nasipuri

{"title":"Region selection in handwritten character recognition using Artificial Bee Colony Optimization","authors":"A. Roy, N. Das, R. Sarkar, S. Basu, M. Kundu, M. Nasipuri","doi":"10.1109/EAIT.2012.6407891","DOIUrl":"https://doi.org/10.1109/EAIT.2012.6407891","url":null,"abstract":"Detection of local regions with optimal discriminating information from a sample of handwritten character image is one of the most challenging tasks to the pattern recognition community. In order to identify such regions, the idea of Artificial Bee Colony Optimization has been utilized in the present work. The technique is evaluated to pin point the set of local regions offering optimal discriminating feature set for handwritten numeral and character recognition. Initially, 8 directional gradient features are extracted from every region of different levels of partitions created using a CG based Quad Tree partitioning approach. Then, using the present approach, at each level, sampling process is done based on support Vector Machine (SVM) in every single region. Applying the technique we have gained 33%, 14%, 9%, 19%interms of region reduction and 0.2%, 0.4%, 0%, 1.6% in terms of recognition for Arabic, Hindi, Telugu numerals and Bangla Basic character datasets respectively. Though the success rate has not improved significantly for all the datasets, sizable amount of reduction in regions has occurred for every dataset using the present technique. Thus the cost and time of feature extraction is reduced significantly without dropping the general recognition rate.","PeriodicalId":194103,"journal":{"name":"2012 Third International Conference on Emerging Applications of Information Technology","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126949295","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: 20