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

Blockchain Utilization in Healthcare: Key Requirements and Challenges 区块链在医疗保健中的应用:关键要求和挑战

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531136

T. Kumar, Vidhya Ramani, Ijaz Ahmad, An Braeken, E. Harjula, M. Ylianttila

引用次数: 85

Towards a CAD System for Breast Cancer Based on Individual Microcalcifications? 基于个体微钙化的乳腺癌CAD系统?

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531134

Evgenia Papavasileiou, Redona Brahimetaj, F. Temmermans, Bruno Cornelis, I. Willekens, J. Mey, B. Jansen

{"title":"Towards a CAD System for Breast Cancer Based on Individual Microcalcifications?","authors":"Evgenia Papavasileiou, Redona Brahimetaj, F. Temmermans, Bruno Cornelis, I. Willekens, J. Mey, B. Jansen","doi":"10.1109/HealthCom.2018.8531134","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531134","url":null,"abstract":"Microcalcifications (MCs) are small calcium deposits which are often present in breast tissue. Although they may be an early indicator of breast cancer, they are often present in healthy women. Judging on the malignancy of MCs based on their appearance on mammograms is a challenging task for radiologists as mammography projects a 3D object onto a 2D plane, as a consequence, superposition of tissue may alter their appearance. Although Computer Aided Detection and Diagnosis (CAD) systems have been developed to assist radiologists in detecting and characterizing MCs, they mainly focus on analyzing MCs’ spatial configuration rather than their individual characteristics. In this study, we propose a CAD system for the characterization of individual MCs, whose shape is visualized in 3D by scanning breast tissue with micro-CT, a high resolution 3D imaging modality. By calculating a large amount of radiomic features we are able to classify MCs as benign or malignant with 75.88% accuracy, 62.13% sensitivity and 86.39% specificity, outperforming the state of the art and proving that there is a difference regarding the shape and the texture of individual MCs. Although this methodology is not applicable in vivo, its potential for use in intra operative imaging is high, as it can reduce the waiting time between tissue extraction and result of the anatomopathological investigation, facilitating and accelerating the procedure.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"24 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":"121341914","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: 1

Multipurpose Sensor for Human Movement Analysis 多用途人体运动分析传感器

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531166

Jan Foltýn, Radek Halfar, M. Cerný, N. Noury

引用次数: 0

Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis 利用机器学习方法和图像分析对面部吸引力进行整形手术评估

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531195

Lubomír Štěpánek, P. Kasal, J. Mesták

{"title":"Evaluation of facial attractiveness for purposes of plastic surgery using machine-learning methods and image analysis","authors":"Lubomír Štěpánek, P. Kasal, J. Mesták","doi":"10.1109/HealthCom.2018.8531195","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531195","url":null,"abstract":"Many current studies conclude that facial attractiveness perception is data-based and irrespective of the perceiver. However, analyses of facial geometric image data and its visual impact always exceeded power of classical statistical methods. In this study, we have applied machine-learning methods to identify geometric features of a face associated with an increase of facial attractiveness after undergoing rhinoplasty. Furthermore, we explored how accurate classification of faces into sets of facial emotions and their facial manifestations is, since categorization of human faces into emotions manifestation should take into consideration the fact that total face impression is also dependent on expressed facial emotion.Both profile and portrait facial image data were collected for each patient (n = 42), processed, landmarked and analysed using R language. Multivariate linear regression was performed to select predictors increasing facial attractiveness after undergoing rhinoplasty. The sets of used facial emotions originate from Ekman-Friesen FACS scale, but was improved substantially. Bayesian naive classifiers, decision trees (CART) and neural networks were learned to allow assigning a new face image data into one of facial emotions.Enlargements of both a nasolabial and nasofrontal angle within rhinoplasty were determined as significant predictors increasing facial attractiveness (p < 0.05). Neural networks manifested the highest predictive accuracy of a new face classification into facial emotions. Geometrical shape of a mouth, then eyebrows and finally eyes affect in descending order final classified emotion, as was identified using decision trees.We performed machine-learning analyses to point out which facial geometric features, based on large data evidence, affect facial attractiveness the most, and therefore should preferentially be treated within plastic surgeries.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"25 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":"126082864","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: 14

Brokering Services for Integrating Health Cloud Platforms for Remote Patient Monitoring 用于集成用于远程患者监测的健康云平台的代理服务

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531151

Raafat Aburukba, A. Sagahyroon, F. Aloul, Niha Thodika

{"title":"Brokering Services for Integrating Health Cloud Platforms for Remote Patient Monitoring","authors":"Raafat Aburukba, A. Sagahyroon, F. Aloul, Niha Thodika","doi":"10.1109/HealthCom.2018.8531151","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531151","url":null,"abstract":"Remote patient monitoring (RPM) uses technology to assess patients’ health within and beyond the healthcare facilities. RPM is becoming a reality by connecting the patient’s healthcare devices to the Internet. Modern healthcare devices come with a cloud-based support that provides patients with new features such as the broad network access to their vitals’ readings from anywhere. This shift of cloud-based support brought a new way to interact with the patients’ devices, as well as, new challenges that are different from the traditional Bluetooth interaction that is heavily studied in the literature. In real situations, patients usually have multiple devices to measure different vital signs that are made by different providers. Hence, it is not possible with the current approaches to have all patient vital readings consolidated in a single location. This consolidation enables the management of the chronic disorders and to analyze the health trends as part of the valuation of the treatment process. This work proposes a cloud-based brokering solution that seamlessly integrates multiple health cloud providers through a set of services that uses the web services cloud standard for the purpose of remote patient monitoring application. This work is validated by a prototype implementation.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"3 5 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":"114146242","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

Human Body Energy Harvesting Solutions for Wearable Technologies 可穿戴技术的人体能量收集解决方案

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531189

A. Proto, L. Peter, M. Cerný, M. Penhaker, Daniele Bibbo, S. Conforto, M. Schmid

引用次数: 7

CardiaQloud: A Remote ECG Monitoring System Using Cloud Services for eHealth and mHealth Applications CardiaQloud:使用云服务的远程心电监测系统，用于电子健康和移动健康应用

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531164

Ismael Villanueva-Miranda, H. Nazeran, R. Martínek

引用次数: 3

A Bispectrum-based Approach for Detecting Deception using EEG Signals 基于双谱的脑电信号欺骗检测方法

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531183

R. Alazrai, Faisal Alqasem, Saqr Alaarag, K. M. Yousef, M. Daoud

{"title":"A Bispectrum-based Approach for Detecting Deception using EEG Signals","authors":"R. Alazrai, Faisal Alqasem, Saqr Alaarag, K. M. Yousef, M. Daoud","doi":"10.1109/HealthCom.2018.8531183","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531183","url":null,"abstract":"Deception is considered a ubiquitous social phenomenon that has a significant implications in clinical, moral, and law enforcement domains. In this study, we propose a novel electroencephalography (EEG)-based approach for detecting deception. The proposed approach utilizes higher-order spectra (HOS) analysis, namely the bispectrum analysis, to construct a representation of the EEG signals. The constructed bispectrum-based representation is utilized to compute a set of features that can be used to identify deception in EEG signals. Specifically, the extracted features are used to train a support vector machine (SVM) classifier to classify EEG signals into guilty or innocent classes. The performance of the proposed EEG-based deception detection approach was evaluated using EEG signals that were recorded for eleven subjects who participated in a guilty knowledge test (GKT). The results reported in the current study demonstrate the efficacy of the proposed in detecting deception using EEG signals. In particular, the average classification accuracy obtained in differentiating between the innocent and guilty classes is equal to 74%.","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"54 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":"129271216","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: 1

Assessment of Gait Parameters in Virtual Environment 虚拟环境中步态参数的评估

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531159

Agnieszka Szczęsna, M. Błaszczyszyn, Magdalena Pawlyta, A. Michalczuk

引用次数: 3

An Interferometric Sensor for Monitoring Respiratory and Heart Rate of the Human Body 一种用于监测人体呼吸和心率的干涉式传感器

2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) Pub Date : 2018-09-01 DOI: 10.1109/HealthCom.2018.8531130

J. Nedoma, M. Fajkus, S. Kepak, J. Cubik, S. Zabka, R. Martínek, Vlastimil Slaný, J. Mareček

{"title":"An Interferometric Sensor for Monitoring Respiratory and Heart Rate of the Human Body","authors":"J. Nedoma, M. Fajkus, S. Kepak, J. Cubik, S. Zabka, R. Martínek, Vlastimil Slaný, J. Mareček","doi":"10.1109/HealthCom.2018.8531130","DOIUrl":"https://doi.org/10.1109/HealthCom.2018.8531130","url":null,"abstract":"This paper focuses on current trends in the noninvasive biomedical monitoring of the human body using a fiber-optic interferometric sensor. An interferometric sensor encapsulated into a thin circular layer of material polyurethane (code PU430 – PH30). This used material is immune to electromagnetic interference (EMI). The sensor is primarily designed for use in electromagnetic interference environments (MRI). The monitored vital parameters of the human body are respiratory rate (RR) and heart rate (HR). The sensor was tested in a laboratory environment on the ten volunteers upon their written consent, when the subjects tested were monitored in the supine and sitting position of the body. Testing was performed towards the reference ECG and the piezoelectric respiratory belt. Based on the objective Bland-Altman analysis (for respiratory rate averaging 96.49 % and for heart rate averaging 95.64 %), the sensor may be acceptable to clinicians. (the sensor is primarily intended for monitoring not for the diagnosis).","PeriodicalId":232709,"journal":{"name":"2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom)","volume":"6 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":"134342830","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: 1