Jinman Kim, C. Lemon, Tanya Baldacchino, M. Khadra, D. Feng
{"title":"Technologies for mHealth","authors":"Jinman Kim, C. Lemon, Tanya Baldacchino, M. Khadra, D. Feng","doi":"10.1201/9781351229067-8","DOIUrl":"https://doi.org/10.1201/9781351229067-8","url":null,"abstract":"","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131059354","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}
{"title":"Virtual Hospitals: Integration of Telemedicine, Healthcare Services, and Cloud Computing","authors":"Shaftab Ahmed, M. Yasin Akhtar Raja","doi":"10.1201/9781351229067-3","DOIUrl":"https://doi.org/10.1201/9781351229067-3","url":null,"abstract":"","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132786988","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}
Pub Date : 2018-10-08DOI: 10.1201/9781351229067-11
E. Mutafungwa, Jyri Hamalainen
{"title":"Use of Small-Cell Technologies for Telemedicine","authors":"E. Mutafungwa, Jyri Hamalainen","doi":"10.1201/9781351229067-11","DOIUrl":"https://doi.org/10.1201/9781351229067-11","url":null,"abstract":"","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131656734","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}
{"title":"Drug-Delivery Systems in eMedicine and mHealth","authors":"A. Ariani, S. Soegijoko, H. N. Rasyid","doi":"10.1201/b19210-20","DOIUrl":"https://doi.org/10.1201/b19210-20","url":null,"abstract":"","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132133311","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}
{"title":"Teledermatology","authors":"Soegijardjo Soegijoko, A. Ariani, Sugiyantini","doi":"10.1201/b19210-28","DOIUrl":"https://doi.org/10.1201/b19210-28","url":null,"abstract":"","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129123027","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}
{"title":"Wireless Instrumentation and Biomedical Applications","authors":"J. Carmo, J. Correia","doi":"10.1201/9781351229067-6","DOIUrl":"https://doi.org/10.1201/9781351229067-6","url":null,"abstract":"","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121109693","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}
Pub Date : 2018-10-08DOI: 10.1201/9781351229067-17
V. Luciano, E. Sardini, A. Dionisi, M. Serpelloni, A. Cadei
Nowadays, with the rapid development of bioengineering, implantable medical devices are being widely employed in treating human ailments These devices, which are implantable in the human body, include pacemakers, smart prostheses, drug pumps, cochlear implants, implantable defibrillators, neurostimulators, bladder stimulators, nerve stimulators, and diaphragm stimulators They perform a real-time control and/or monitoring of several physiological parameters Over 6 million heart patients worldwide have implanted pacemakers and about 150,000 pacemakers are being surgically implanted every year in the United States alone (Wang, 2003) These pacemakers, and defibrillators, help in treating several heart conditions including fibrillation, atrial and ventricular tachyarrhythmia, and bradycardia (Soykan, 2002) These monitoring systems can communicate wirelessly with an external readout unit By means of this architecture, the use of transcutaneous wires and the risk of infection can be avoided and necessary data can be collected easily Wireless communication allows regular monitoring of several physiological parameters like the blood pressure, heart rate, and body temperature of patients during their daily activities at home (Tamura et al , 2011; Valdastri et al , 2004) These data can be collected and sent to a remote medical unit via Internet for the medical staff to access and assess and then assist the patient accordingly (Halperin et al , 2008) As shown in Troyk et al (2007), myoelectric sensors are another example of an implantable medical device They are composed of several myoelectric electrodes implanted in the residual limb of patients with a prosthetic arm These electrodes detect intramuscular myoelectric activity and provide signals that are elaborated to control the prosthesis Typically, implantable medical devices are powered by batteries, which is a severe limitation In most cases, because of battery discharge, an implantable system will need battery replacement through a surgical operation For example, although an implanted defibrillator lasts about 10 years, its battery 2
如今，随着生物工程的飞速发展，植入式医疗器械在治疗人类疾病方面得到了广泛的应用。这些植入式医疗器械包括起搏器、智能假肢、药物泵、人工耳蜗、植入式除颤器、神经刺激器、膀胱刺激器、神经刺激器、神经刺激器等。它们可以实时控制和/或监测几种生理参数，全世界有600多万心脏病患者植入了起搏器，仅在美国每年就有大约15万心脏起搏器通过手术植入(Wang, 2003)。这些起搏器和除颤器有助于治疗多种心脏疾病，包括颤动、心房和室性心动过速和心动过缓(Soykan，2002)这些监测系统可以与外部读出单元进行无线通信，通过这种架构，可以避免使用经皮导线和感染风险，并且可以轻松收集必要的数据。无线通信可以定期监测患者在家中日常活动中的血压、心率和体温等几个生理参数(Tamura等人，2011;Valdastri et al, 2004)这些数据可以通过互联网收集并发送到远程医疗单位，供医务人员访问和评估，然后相应地帮助患者(Halperin et al, 2008)。Troyk et al(2007)显示。肌电传感器是植入式医疗设备的另一个例子，它们由几个肌电电极组成，植入假肢患者的残肢中。这些电极检测肌肉内的肌电活动，并提供精心设计的信号来控制假肢。通常，植入式医疗设备由电池供电，这是一个严重的限制。例如，虽然植入式除颤器可以使用10年左右，但它的电池只能使用2年
{"title":"Implantable Systems","authors":"V. Luciano, E. Sardini, A. Dionisi, M. Serpelloni, A. Cadei","doi":"10.1201/9781351229067-17","DOIUrl":"https://doi.org/10.1201/9781351229067-17","url":null,"abstract":"Nowadays, with the rapid development of bioengineering, implantable medical devices are being widely employed in treating human ailments These devices, which are implantable in the human body, include pacemakers, smart prostheses, drug pumps, cochlear implants, implantable defibrillators, neurostimulators, bladder stimulators, nerve stimulators, and diaphragm stimulators They perform a real-time control and/or monitoring of several physiological parameters Over 6 million heart patients worldwide have implanted pacemakers and about 150,000 pacemakers are being surgically implanted every year in the United States alone (Wang, 2003) These pacemakers, and defibrillators, help in treating several heart conditions including fibrillation, atrial and ventricular tachyarrhythmia, and bradycardia (Soykan, 2002) These monitoring systems can communicate wirelessly with an external readout unit By means of this architecture, the use of transcutaneous wires and the risk of infection can be avoided and necessary data can be collected easily Wireless communication allows regular monitoring of several physiological parameters like the blood pressure, heart rate, and body temperature of patients during their daily activities at home (Tamura et al , 2011; Valdastri et al , 2004) These data can be collected and sent to a remote medical unit via Internet for the medical staff to access and assess and then assist the patient accordingly (Halperin et al , 2008) As shown in Troyk et al (2007), myoelectric sensors are another example of an implantable medical device They are composed of several myoelectric electrodes implanted in the residual limb of patients with a prosthetic arm These electrodes detect intramuscular myoelectric activity and provide signals that are elaborated to control the prosthesis Typically, implantable medical devices are powered by batteries, which is a severe limitation In most cases, because of battery discharge, an implantable system will need battery replacement through a surgical operation For example, although an implanted defibrillator lasts about 10 years, its battery 2","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126673001","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}
D. Clifton, Marco A. F. Pimentel, K. Niehaus, Lei A. Clifton, T. Peto, D. Crook, P. Watkinson
Healthcare systems worldwide are entering a new phase: ever-increasing quantities of complex, massively multivariate data concerning all aspects of patient care are starting to be routinely acquired and stored [1], throughout the life of a patient. This exponential growth in data quantities far outpaces the capability of clinical experts to cope, resulting in a so-called data deluge, in which the data are largely unexploited. There is huge potential for using advances in large-scale machine learning methodologies* to exploit the contents of these complex data sets by performing robust, scalable, automated inference to improve healthcare outcomes significantly by using patient-specific probabilistic models, a field in which there is little existing research [2] and which promises to develop into a new
{"title":"Intelligent Electronic Health Systems","authors":"D. Clifton, Marco A. F. Pimentel, K. Niehaus, Lei A. Clifton, T. Peto, D. Crook, P. Watkinson","doi":"10.1201/b19210-6","DOIUrl":"https://doi.org/10.1201/b19210-6","url":null,"abstract":"Healthcare systems worldwide are entering a new phase: ever-increasing quantities of complex, massively multivariate data concerning all aspects of patient care are starting to be routinely acquired and stored [1], throughout the life of a patient. This exponential growth in data quantities far outpaces the capability of clinical experts to cope, resulting in a so-called data deluge, in which the data are largely unexploited. There is huge potential for using advances in large-scale machine learning methodologies* to exploit the contents of these complex data sets by performing robust, scalable, automated inference to improve healthcare outcomes significantly by using patient-specific probabilistic models, a field in which there is little existing research [2] and which promises to develop into a new","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121922082","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}
{"title":"Integrating Telemedicine and Telehealth—Advancing Health at a Distance","authors":"H. Rashvand, K. Hsiao","doi":"10.1201/9781351229067-1","DOIUrl":"https://doi.org/10.1201/9781351229067-1","url":null,"abstract":"","PeriodicalId":285949,"journal":{"name":"TeleMedicine and Electronic Medicine","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124631160","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}
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