EpiSense:迈向癫痫病人护理的智能解决方案

M. El Barachi, F. Oroumchian, Rabia Rauf, Uroosa Khan, Beschier al Hassooni, Alexander al Basosi, S. Kazia
{"title":"EpiSense:迈向癫痫病人护理的智能解决方案","authors":"M. El Barachi, F. Oroumchian, Rabia Rauf, Uroosa Khan, Beschier al Hassooni, Alexander al Basosi, S. Kazia","doi":"10.23919/SpliTech.2019.8783034","DOIUrl":null,"url":null,"abstract":"Epilepsy is a chronic neurological brain disorder that affects 50 million people globally. There are several challenges associated with the care of epileptic patients, including: 1) the timely and accurate diagnosis of the condition; 2) the long-term non-intrusive monitoring and detection of epileptic seizures in real time for suitable interventions; 3) alleviating the mental health issues associated with epilepsy, such as anxiety and depression; and 4) the lack of availability of large scale datasets related to epileptic patients with different profiles, needed to advance research in epilepsy. In this work, we propose EpiSense – a smart healthcare solution for epileptic patients’ care. EpiSense leverages sensory, mobile, and web technologies, as well as machine learning techniques for the real-time detection of epileptic seizures. As part of the system, a patient’s mobile app. is provided to allow the detection of seizures’ occurrence in real time and the sending of alarm notifications to care takers, for appropriate actions. Moreover, a web portal enables doctors to view the progress of their patients and get notified about seizures’ occurrence and statistics. The EpiSense system was designed and implemented, and three machine learning models were tested for real-time epileptic seizure detection. This work gives interesting insights about the possibility of using sensory technologies and data analytics for the improvement of epileptic patients’ care, and offers the possibility of personalized healthcare management.","PeriodicalId":223539,"journal":{"name":"2019 4th International Conference on Smart and Sustainable Technologies (SpliTech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"EpiSense: Towards a smart solution for epileptic patients’ care\",\"authors\":\"M. El Barachi, F. Oroumchian, Rabia Rauf, Uroosa Khan, Beschier al Hassooni, Alexander al Basosi, S. Kazia\",\"doi\":\"10.23919/SpliTech.2019.8783034\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Epilepsy is a chronic neurological brain disorder that affects 50 million people globally. There are several challenges associated with the care of epileptic patients, including: 1) the timely and accurate diagnosis of the condition; 2) the long-term non-intrusive monitoring and detection of epileptic seizures in real time for suitable interventions; 3) alleviating the mental health issues associated with epilepsy, such as anxiety and depression; and 4) the lack of availability of large scale datasets related to epileptic patients with different profiles, needed to advance research in epilepsy. In this work, we propose EpiSense – a smart healthcare solution for epileptic patients’ care. EpiSense leverages sensory, mobile, and web technologies, as well as machine learning techniques for the real-time detection of epileptic seizures. As part of the system, a patient’s mobile app. is provided to allow the detection of seizures’ occurrence in real time and the sending of alarm notifications to care takers, for appropriate actions. Moreover, a web portal enables doctors to view the progress of their patients and get notified about seizures’ occurrence and statistics. The EpiSense system was designed and implemented, and three machine learning models were tested for real-time epileptic seizure detection. This work gives interesting insights about the possibility of using sensory technologies and data analytics for the improvement of epileptic patients’ care, and offers the possibility of personalized healthcare management.\",\"PeriodicalId\":223539,\"journal\":{\"name\":\"2019 4th International Conference on Smart and Sustainable Technologies (SpliTech)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 4th International Conference on Smart and Sustainable Technologies (SpliTech)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/SpliTech.2019.8783034\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 4th International Conference on Smart and Sustainable Technologies (SpliTech)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/SpliTech.2019.8783034","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

癫痫是一种慢性神经性脑部疾病,影响着全球5000万人。与癫痫患者的护理相关的几个挑战包括:1)病情的及时和准确诊断;2)长期非侵入性监测和实时检测癫痫发作,以便进行适当的干预;3)缓解与癫痫相关的心理健康问题,如焦虑和抑郁;4)缺乏与不同类型癫痫患者相关的大规模数据集,这需要推进癫痫研究。在这项工作中,我们提出了EpiSense -一个智能医疗保健解决方案,用于癫痫患者的护理。EpiSense利用感官、移动和网络技术,以及机器学习技术来实时检测癫痫发作。作为系统的一部分,提供了患者的移动应用程序,可以实时检测癫痫发作的发生,并向护理人员发送警报通知,以便采取适当的行动。此外,一个门户网站使医生能够查看病人的病情进展,并获得癫痫发作情况和统计数据的通知。设计并实现了EpiSense系统,并对三种机器学习模型进行了实时癫痫发作检测测试。这项工作提供了关于使用感官技术和数据分析改善癫痫患者护理的可能性的有趣见解,并提供了个性化医疗保健管理的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EpiSense: Towards a smart solution for epileptic patients’ care
Epilepsy is a chronic neurological brain disorder that affects 50 million people globally. There are several challenges associated with the care of epileptic patients, including: 1) the timely and accurate diagnosis of the condition; 2) the long-term non-intrusive monitoring and detection of epileptic seizures in real time for suitable interventions; 3) alleviating the mental health issues associated with epilepsy, such as anxiety and depression; and 4) the lack of availability of large scale datasets related to epileptic patients with different profiles, needed to advance research in epilepsy. In this work, we propose EpiSense – a smart healthcare solution for epileptic patients’ care. EpiSense leverages sensory, mobile, and web technologies, as well as machine learning techniques for the real-time detection of epileptic seizures. As part of the system, a patient’s mobile app. is provided to allow the detection of seizures’ occurrence in real time and the sending of alarm notifications to care takers, for appropriate actions. Moreover, a web portal enables doctors to view the progress of their patients and get notified about seizures’ occurrence and statistics. The EpiSense system was designed and implemented, and three machine learning models were tested for real-time epileptic seizure detection. This work gives interesting insights about the possibility of using sensory technologies and data analytics for the improvement of epileptic patients’ care, and offers the possibility of personalized healthcare management.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信