2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)最新文献_第5页

Deep Learning Based Automated Extraction of Intra-Retinal Layers for Analyzing Retinal Abnormalities 基于深度学习的视网膜内层自动提取及其视网膜异常分析

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531198

Taimur Hassan, Anam Usman, M. Akram, Momina Masood, Ubaidullah Yasin

{"title":"Deep Learning Based Automated Extraction of Intra-Retinal Layers for Analyzing Retinal Abnormalities","authors":"Taimur Hassan, Anam Usman, M. Akram, Momina Masood, Ubaidullah Yasin","doi":"10.1109/HealthCom.2018.8531198","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531198","url":null,"abstract":"Extraction of retinal layers from optical coherence tomography (OCT) scans is critical for analyzing retinal anomalies and manual segmentation of these retinal layers is a very cumbersome task. Recently, deep learning has gained much popularity in medical image analysis due to its underlying precision and robustness. Many researchers have utilized deep learning for extracting retinal layers from OCT images. However, to the best of our knowledge, there is no literature available that presents a robust segmentation framework that is able to extract retinal layers from OCT scans having different retinal pathological syndromes. Therefore, this paper presents a deep convolutional neural network and structure tensor-based segmentation framework (CNN-STSF) for the fully automated segmentation of up to eight retinal layers from normal as well as diseased OCT scans. First of all, the proposed framework computes coherent tensor from the candidate scan through which retinal layers are extracted. Afterwards, the pixels representing the layers are further classified using cloud based deep convolutional neural network (CNN) model trained on 1,200 retinal layers patches. CNN model in the proposed framework computes the probability of each layer pixels and assign it to be part of that layer for which it has the highest probability. The proposed framework was tested and validated on more than 39,000 retinal OCT scans from different publicly available datasets and from local Armed Forces Institute of Ophthalmology (AFIO) dataset where it outperformed all the existing solutions by achieving the overall layer segmentation accuracy of 0.9375.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124904526","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

A Robust PPG-based Heart Rate Monitor for Fitness and eHealth Applications 一个健壮的基于ppg的心率监测器健身和电子健康应用

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531082

Maryamsadat Shokrekhodaei, Stella Quinones, R. Martínek, H. Nazeran

{"title":"A Robust PPG-based Heart Rate Monitor for Fitness and eHealth Applications","authors":"Maryamsadat Shokrekhodaei, Stella Quinones, R. Martínek, H. Nazeran","doi":"10.1109/HealthCom.2018.8531082","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531082","url":null,"abstract":"This paper presents the design and implementation of a Heart Rate Monitor (HRM) based on photoplethysmography (PPG). This optical method is noninvasive and provides a convenient means of implementing a low-cost wearable HRM for eHealth applications. Our novel design consists of a light source that emits a modulated or unmodulated light signal through the finger tissue and measures the changes in the reflected light from the arterial blood. These reflections correspond well with the blood volume changes in synchrony with the heartbeat. Here we aim to provide a detailed description of how to design and implement an HRM device based on two approaches: 1) PPG using an unmodulated light source; 2) PPG using a modulated light source. Noise analysis in these devices enabled us to conceptualize and compare their performance and infer which one offers a better choice for optical heart rate monitoring. Based on the measurement results, the latter approach offers superior performance due to better noise cancellation and higher Signal-to-Noise Ratio (SNR) and is therefore robust against movement artefacts, power line noise, flicker noise of electronic components, as well as background environmental light interference.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131814389","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: 5

Intelligent Clinical Decision Support Systems for Patient-Centered Healthcare in Breast Cancer Oncology 以患者为中心的乳腺癌肿瘤医疗保健智能临床决策支持系统

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531128

B. Sekar, J. Lamy, Naiara Muro, Amaia Ugarriza Pinedo, B. Séroussi, N. Larburu, Gilles Guézennec, J. Bouaud, Frank Guijarro Masero, Mónica Arrúe, Hui Wang

{"title":"Intelligent Clinical Decision Support Systems for Patient-Centered Healthcare in Breast Cancer Oncology","authors":"B. Sekar, J. Lamy, Naiara Muro, Amaia Ugarriza Pinedo, B. Séroussi, N. Larburu, Gilles Guézennec, J. Bouaud, Frank Guijarro Masero, Mónica Arrúe, Hui Wang","doi":"10.1109/HealthCom.2018.8531128","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531128","url":null,"abstract":"Breast cancer is the most common type of cancer in women worldwide, with incidence rate being second highest to other types of cancer. In the current clinical setting, multidisciplinary breast units are introduced to improve the quality of the therapeutic decision based on the best evidence-based practices. DESIREE project aims to provide a web-based software ecosystem for personalized, collaborative and multidisciplinary management of primary breast cancer by specialized breast units, from diagnosis to therapy and follow-ups. In order to provide a multi-model decision support to clinicians in present clinical settings, the project develops and integrates three modalities of decision support, namely guideline-based decision support system (DSS), experience-based DSS and case-based DSS. Visual analytics GUI are developed to properly adapt the results of the DSSs and graphically represent them to the clinician in a user-friendly manner. DESIREE information management system (DESIMS), serves as the interface between the user and DSSs for entering patient data and viewing the results in the visual analytics GUI. In this paper, we present the overall architecture, workflow and integration of the three DSSs in the DESIREE platform.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130433054","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: 8

Evaluation of wearable Kinematic Algorithms for the Monitoring of Ecological Activity 用于生态活动监测的可穿戴运动学算法评价

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/healthcom.2018.8531098

N. Noury, B. Perriot

引用次数: 0

ANALYSIS OF CLOUD COMPUTING SECURITY IN PERSPECTIVE OF SAUDI ARABIA 以沙特阿拉伯为例的云计算安全分析

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531141

Yara AlHumaidan, Lama AlAjmi, Moudhi M. Aljamea, M. Mahmud

引用次数: 1

Measurement System for Classification of Hand’s Gesture 手势分类测量系统

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531079

L. Peter, Filip Maryncak, A. Proto, M. Penhaker

引用次数: 0

Healthcom 2018 Program Healthcom 2018计划

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/healthcom.2018.8531092

引用次数: 0

VITASENIOR-MT: a telehealth solution for the elderly focused on the interaction with TV VITASENIOR-MT:专注于与电视互动的老年人远程医疗解决方案

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531126

Gabriel Pires, Pedro D. F. Correia, Dario Jorge, Diogo Mendes, Nelson Gomes, Pedro Dias, Pedro Ferreira, Ana C. Lopes, A. Manso, L. Almeida, Luis Oliveira, Renato Panda, Paulo Monteiro, Carla Gracio, T. Pereira

{"title":"VITASENIOR-MT: a telehealth solution for the elderly focused on the interaction with TV","authors":"Gabriel Pires, Pedro D. F. Correia, Dario Jorge, Diogo Mendes, Nelson Gomes, Pedro Dias, Pedro Ferreira, Ana C. Lopes, A. Manso, L. Almeida, Luis Oliveira, Renato Panda, Paulo Monteiro, Carla Gracio, T. Pereira","doi":"10.1109/HealthCom.2018.8531126","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531126","url":null,"abstract":"Remote monitoring of health parameters is a promising approach to improve the health condition and quality of life of particular groups of the population, which can also alleviate the current expenditure and demands of healthcare systems. The elderly, usually affected by chronic comorbidities, are a specific group of the population that can strongly benefit from telehealth technologies, allowing them to reach a more independent life, by living longer in their own homes. Usability of telehealth technologies and their acceptance by end-users are essential requirements for the success of telehealth implementation. Older people are resistant to new technologies or have difficulty in using them due to vision, hearing, sensory and cognition impairments. In this paper, we describe the implementation of an IoT-based telehealth solution designed specifically to address the elderly needs. The end-user interacts with a TV-set to record biometric parameters, and to receive warning and recommendations related to health and environmental sensor recordings. The familiarization of older people with the TV is expected to provide a more user-friendly interaction ensuring the effectiveness integration of the end-user in the overall telehealth solution.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125542424","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: 8

Personalized Effect of Health Behavior on Blood Pressure: Machine Learning Based Prediction and Recommendation 健康行为对血压的个性化影响:基于机器学习的预测和推荐

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531109

Po-Han Chiang, S. Dey

引用次数: 24

A Low Complexity Low Power Indoor Positioning System Based on Wireless Received Signal Strength 基于无线接收信号强度的低复杂度低功耗室内定位系统

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531137

Hung-Yi Li, Hsi-Pin Ma

{"title":"A Low Complexity Low Power Indoor Positioning System Based on Wireless Received Signal Strength","authors":"Hung-Yi Li, Hsi-Pin Ma","doi":"10.1109/HealthCom.2018.8531137","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531137","url":null,"abstract":"In this paper, we propose an indoor positioning system based on Bluetooth low energy (BLE), and the localization tag is with characteristics of low power, low cost, and high portability. The proposed system consists of BLE tags, BLE/WiFi repeaters, and a fusion server. The BLE tag is a device which broadcasts BLE beacons. The BLE/WiFi repeaters collect the beacons transmitted from the tag and extract the received signal strength (RSS). The RSS values are then transmitted to the fusion server through WiFi, and the server will estimate the position of the BLE tag with RSS-based positioning algorithm. We propose an indoor positioning algorithm which is a hybrid from received signal strength indication (RSSI)-fingerprint and cell of origin (CoO) to estimate the BLE tag’s location. Some modifications are made to typical RSSI-fingerprint and CoO algorithm to get better accuracy. The test results show 1.2 m to 1.4 m positioning accuracy. The size of the BLE tag is 1.7 cm in radius and 0.5 cm thick. Each BLE tag costs 3 US dollars. The current consumption of the tag is 50 μA which can be used without charge for 136 days with a CR2025 battery.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127352206","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