IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)最新文献_第6页

Tail-clamping stimulation increases phase-locking neurons in the hippocampus of anaesthetized rat 夹尾刺激使麻醉大鼠海马锁相神经元增多

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864472

Y. Wang, X. J. Zheng, Z. Feng

{"title":"Tail-clamping stimulation increases phase-locking neurons in the hippocampus of anaesthetized rat","authors":"Y. Wang, X. J. Zheng, Z. Feng","doi":"10.1109/BHI.2014.6864472","DOIUrl":"https://doi.org/10.1109/BHI.2014.6864472","url":null,"abstract":"Phase-locking is an important encoding mode in the hippocampal region of brain where neuronal firing concentrates on a certain phase of the periodic field potential, especially θ rhythm. It can be observed during many behaviors while the locked-phase varies accordingly. In this study, the changes of neuronal phase-locking induced by somatosensory stimulation of tail-clamping were investigated. The local field potential and spike signals were recorded in hippocampal CA1 region of anesthetized rats with microelectrode array. The phase-locking relationship was analyzed with Raleigh test and spike-phase histogram. The results showed that during spontaneous activity, 3 of 20 examined neurons were phase-locking to the θ rhythm of the field potential. However, during the tail clamping periods, the number of phase-locking neurons increased from 3 to 8, and the mean locked-phase shifted towards the negative peak of the field potential cycle, which became θ rhythm dominated. These findings suggest that the hippocampus may encode somatosensory information with the phase-locking neurons and by shifting their locked-phases.","PeriodicalId":177948,"journal":{"name":"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"193 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128195860","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

Using the smartphone camera to monitor heart rate and rhythm in heart failure patients 使用智能手机摄像头监测心力衰竭患者的心率和节律

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864425

R. Lagido, Joana Lobo, J. Cardoso, S. Leite, L. Ferreira, Carla Margarida Castanheira de Sousa Gonçalves

{"title":"Using the smartphone camera to monitor heart rate and rhythm in heart failure patients","authors":"R. Lagido, Joana Lobo, J. Cardoso, S. Leite, L. Ferreira, Carla Margarida Castanheira de Sousa Gonçalves","doi":"10.1109/BHI.2014.6864425","DOIUrl":"https://doi.org/10.1109/BHI.2014.6864425","url":null,"abstract":"Telemonitoring is a tool with proven results demonstrating clinical benefits in reducing mortality and hospi-talizations. In this context, heart rate and rhythm are vital signs of utmost importance for monitoring a patient's condition. Recent advances in mobile technologies have allowed smartphones to be of great usage in this scenario, given its processing power, connectivity capabilities, sensor hardware and camera quality. This paper describes the usage of a smartphone camera to detect the heart rate and rhythm of a patient. A photoplethys-mogram signal is obtained with the user's fingertip placed over the smartphone camera. An evaluation has been performed on 43 subjects with heart failure. For each patient the signal obtained with the smartphone camera was compared with the ECG signal acquired in hospital environment. Results demonstrate the heart rate can be effectively estimated using that approach with an error rate of 4.75%. Atrial Fibrillation detection with our method achieved a specificity of 97% and sensitivity of 75%.","PeriodicalId":177948,"journal":{"name":"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129718702","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: 25

Dual Fuzzy-Snake model for IMT measurement in carotid ultrasound images of low- and high-risk patients 双模糊- snake模型在低危患者颈动脉超声图像中测量IMT

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864454

S. Rosati, G. Balestra, F. Molinari

引用次数: 2

A mobile memory game for patients with Acquired Brain Damage: A preliminary usability study 获得性脑损伤患者的手机记忆游戏:初步可用性研究

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864363

M. Morón, Rosa Yáñez-Gómez, Daniel Cascado Caballero, C. Suárez-Mejías, J. Sevillano

引用次数: 5

Modeling cervix cancer growth and response to radiation therapy: A validation study using patient volumetric tumor data 模拟宫颈癌的生长和对放射治疗的反应:一项使用患者体积肿瘤数据的验证研究

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864406

Antonella Belfatto, M. Garbey, M. Riboldi, D. Ciardo, A. Cecconi, R. Lazzari, B. Jereczek-Fossa, R. Orecchia, G. Baroni, P. Cerveri

引用次数: 1

3-D Half-spheroid models for transcranial Doppler ultrasound propagation channels 经颅多普勒超声传播通道的三维半球体模型

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864467

Alexander J. Weir, Chengxiang Wang, Stuart Parks

引用次数: 3

Dynamic user models for tailoring health behavior interventions 调整健康行为干预措施的动态用户模型

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864489

H. Jimison

引用次数: 0

Feature extraction from ear-worn sensor data for gait analysis 用于步态分析的耳戴式传感器数据特征提取

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864426

Ling Li, L. Atallah, Benny P. L. Lo, Guang-Zhong Yang

{"title":"Feature extraction from ear-worn sensor data for gait analysis","authors":"Ling Li, L. Atallah, Benny P. L. Lo, Guang-Zhong Yang","doi":"10.1109/BHI.2014.6864426","DOIUrl":"https://doi.org/10.1109/BHI.2014.6864426","url":null,"abstract":"Gait analysis has a significant role in assessing human's walking pattern. It is generally used in sports science for understanding body mechanics, and it is also used to monitor patients' neuro-disorder related gait abnormalities. Traditional marker-based systems are well known for tracking gait parameters for gait analysis, however, it requires long set up time therefore very difficult to be applied in everyday realtime monitoring. Nowadays, there is ever growing of interest in developing portable devices and their supporting software with novel algorithms for gait pattern analysis. The aim of this research is to investigate the possibilities of novel gait pattern detection algorithms for accelerometer-based sensors. In particular, we have used e-AR sensor, an ear-worn sensor which registers body motion via its embedded 3-D accelerom-eter. Gait data was given semantic annotation using pressure mat as well as real-time video recording. Important time stamps within a gait cycle, which are essential for extracting meaningful gait parameters, were identified. Furthermore, advanced signal processing algorithm was applied to perform automatic feature extraction by signal decomposition and reconstruction. Analysis on real-word data has demonstrated the potential for an accelerometer-based sensor system and its ability to extract of meaningful gait parameters.","PeriodicalId":177948,"journal":{"name":"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"6 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113932294","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: 4

A data gathering framework to collect Type 2 diabetes patients data 收集2型糖尿病患者数据的数据收集框架

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864349

A. Dagliati, L. Sacchi, Mauro Bucalo, D. Segagni, K. Zarkogianni, A. Martínez-Millana, J. Cancela, Francesco Sambo, G. Fico, Maria Teresa Meneu Barreira, C. Cerra, K. Nikita, C. Cobelli, L. Chiovato, M. Arredondo, R. Bellazzi

{"title":"A data gathering framework to collect Type 2 diabetes patients data","authors":"A. Dagliati, L. Sacchi, Mauro Bucalo, D. Segagni, K. Zarkogianni, A. Martínez-Millana, J. Cancela, Francesco Sambo, G. Fico, Maria Teresa Meneu Barreira, C. Cerra, K. Nikita, C. Cobelli, L. Chiovato, M. Arredondo, R. Bellazzi","doi":"10.1109/BHI.2014.6864349","DOIUrl":"https://doi.org/10.1109/BHI.2014.6864349","url":null,"abstract":"In this work, we present a framework implemented within the EU project MOSAIC, funded under the FP7 framework, to gather Type 2 Diabetes (T2D) patients' data coming from three European hospitals and a local health care agency. A subset of the MOSAIC activities is centered on the development of Temporal Data Mining models to identify relevant clinical pathways in patients' histories and will in particular benefit from the data coming from the medical centers involved in the project. To best exploit this repository, the need for creating a common and sharable data model becomes immediately apparent. This model is the main subject of this paper. The proposed approach relies on the Informatics for Integrating Biology and the Bedside (i2b2) and the Shared Health Research Information Network (SHRINE) open source software tools. It provides an integrated research setting to merge clinical and environmental data that will enable obtaining a broader vision of individual patients' histories, which will be then mined with multivariate models to identify relevant clinical pathways.","PeriodicalId":177948,"journal":{"name":"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124179356","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: 13

Predicting disability levels of community dwelling older individuals using single wrist mounted accelerometer 使用腕部加速度计预测社区居住老年人的残疾水平

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) Pub Date : 2014-06-01 DOI: 10.1109/BHI.2014.6864465

Sathish K. SankarPandi, S. Dlay, W. L. Woo, M. Catt

引用次数: 5