Epilepsy is one of the most common neurological disorders affecting up to 1% of the world's population and approximately 2.5 million people in the United States. Seizures in more than 30% of epilepsy patients are refractory to anti-epileptic drugs. An important biomedical research effort is focused on the development of an energy efficient implantable device for the real-time control of seizures. In this paper we propose an Internet of Medical Things (IoMT) based automated seizure detection and drug delivery system (DDS) for the control of seizures. The proposed system will detect seizures and inject a fast acting anti-convulsant drug at the onset to suppress seizure progression. The drug injection is performed in two stages. Initially, the seizure detector detects the seizure from the electroencephalography (EEG) signal using a hyper-synchronous signal detection circuit and a signal rejection algorithm (SRA). In the second stage, the drug is released in the seizure onset area upon seizure detection. The design was validated using a system-level simulation and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry leads to a considerable reduction of power consumption compared to previous approaches. The proposed approach can be generalized to other sensor modalities and the use of both wearable and implantable solutions, or a combination of the two.
The Epilepsies are a common, chronic neurological disorder affecting more than 50 million individuals across the globe. It is characterized by unprovoked, recurring (similar or different type) seizures which are commonly diagnosed through clinical EEGs. Good-quality, open-access and free EEG data can act as a catalyst for on-going state-of-the-art (SOTA) research works for detection, prediction and management of epilepsy and seizures. They can also aid in improving the quality of life (QOL) of these diseased individuals and contribute research in healthcare multimedia, data analytics and Artif…
Deakin University（迪肯大学）；Pusan National University（釜山国立大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；University of New South Wales（新南威尔士大学）；The University of Sydney（悉尼大学）；Memorial University（纪念大学）
Recent advances in communication technologies and Internet-of-Medical-Things have transformed smart healthcare enabled by artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and processing that may be infeasible in realistic healthcare scenarios due to the high scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), as an emerging distributed collaborative AI paradigm, is particularly attractive for smart healthcare, by coordinating multiple clients (e.g., hospitals) to perform AI training without sharing …
Controlling the surrounding world and predicting future events has always seemed like a dream, but that could become a reality using a Brain-Computer/Machine Interface (BCI/BMI). Epilepsy is a group of neurological diseases characterized by epileptic seizures. It affects millions of people worldwide, with 80 percent of cases occurring in developing countries. This can result in accidents and sudden, unexpected death. Seizures can happen undetectably in newborns, comatose, or motor-impaired patients, especially due to the fact that many medical personnel is not qualified for EEG signal analysis…
In the case of diabetes, fingertip pricking for a blood sample is inconvenient for glucose measurement. Invasive approaches like laboratory test and one-touch glucometer enhance the risk of blood-related infections. To mitigate this important issue, in the current paper, we propose a novel Internet-of-Medical-Things (IoMT) enabled edge-device for precise, non-invasive blood glucose measurement. In this work, a near-infrared (NIR) spectroscopic technique using two wavelengths (940 nm, 1300 nm) is taken to detect the glucose molecule from human blood. The novel device called iGLU is based on NIR…
Monash University（莫纳什大学）；Alfred Health（阿尔弗雷德健康）；The University of Melbourne（墨尔本大学）；Faculty of Information Technology, Monash University（莫纳什大学信息技术学院）；St. Vincent’s Hospital, The University of Melbourne（墨尔本大学圣文森特医院）；Turner Institute for Brain and Mental Health, Monash University（莫纳什大学特纳脑与心理健康研究所）；Monash Biomedical Imaging, Monash University（莫纳什大学生物医学成像）；Wellcome Centre for Human Neuroimaging, UCL（伦敦大学学院人类神经成像威尔康姆中心）
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains…
Close to half of the world population have smartphones, while a typical flagship smartphone today has been integrated with more than 20 smart components and sensors, making a smartphone a highly integrated platform that can potentially mimic the five senses of humans. Recent advancement in achieving high compactness, high performance computing, high flexibility, and multiplexed functionality in smartphones have enabled them for many cutting-edge healthcare applications, such as single-molecule imaging, medical diagnosis, and biosensing, which were conventionally done with bulky and sophisticat…
The healthcare industry has witnessed significant transformations in e-health services where Electronic Health Records (EHRs) are transferred to mobile edge clouds to facilitate healthcare. Many edge cloud-based system designs have been proposed, but some technical challenges still remain, such as low quality of services (QoS), data privacy and system security due to centralized healthcare architectures. In this paper, we propose a novel hybrid approach of data offloading and data sharing for healthcare using edge cloud and blockchain. First, an efficient data offloading scheme is proposed whe…
Diabetes is considered as an critical comorbidity linked with the latest coronavirus disease 2019 (COVID-19) which spreads through Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Cov-2). The diabetic patients have higher threat of infection from novel corona virus. Depending on the region in the globe, 20% to 50% of patients infected with COVID-19 pandemic had diabetes. The current article discussed the risk associated with diabetic patients and also recommendation for controlling diabetes during this pandemic situation. The article also discusses the case study of COVID-19 at various re…
Swiss Federal Institute of Technology Lausanne (EPFL)（洛桑联邦理工学院）
The healthcare landscape is moving from the reactive interventions focused on symptoms treatment to a more proactive prevention, from one-size-fits-all to personalized medicine, and from centralized to distributed paradigms. Wearable IoT devices and novel algorithms for continuous monitoring are essential components of this transition. Hyperdimensional (HD) computing is an emerging ML paradigm inspired by neuroscience research with various aspects interesting for IoT devices and biomedical applications. Here we explore the not yet addressed topic of optimal encoding of spatio-temporal data, su…
VIT-AP University（维泰大学阿马拉瓦蒂分校）；University of North Texas（北德克萨斯大学）；Dallas College（达拉斯学院）
Smart healthcare which is built as healthcare Cyber-Physical System (H-CPS) from Internet-of-Medical-Things (IoMT) is becoming more important than before. Medical devices and their connectivity through Internet with alongwith the electronics health record (EHR) and AI analytics making H-CPS possible. IoMT-end devices like wearables and implantables are key for H-CPS based smart healthcare. Smart garment is a specific wearable which can be used for smart healthcare. There are various smart garments that help users to monitor their body vitals in real-time. Many commercially available garments c…
EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems
ETH Zürich（苏黎世联邦理工学院）；University of Bologna（博洛尼亚大学）；Aarhus University Hospital（奥胡斯大学医院）；Università di Modena e Reggio Emilia（摩德纳-雷焦·艾米里亚大学）；Lausanne University Hospital (CHUV)（洛桑大学医院）；Danish Epilepsy Centre (Filadelfia)（丹麦癫痫中心（Filadelfia））
University of Jeddah（吉达大学）；Menoufia University（米努菲亚大学）；University of Bradford（布拉德福德大学）；Sejong University（世宗大学）；Misr Higher Institute of Commerce and Computers（米斯尔高等商业与计算机学院）；Nile University（尼罗河大学）；Harbin Institute of Technology（哈尔滨工业大学）
. In this paper, an effective computer-aided diagnosis (CAD) system is presented to detect MI signals using the convolution neural network (CNN) for urban healthcare in smart cities. Two types of transfer learning techniques are employed to retrain the pre-trained VGG-Net (Fine-tuning and VGG-Net as fixed feature extractor) and obtained two new networks VGG-MI1 and VGG-MI2. In the VGG-MI1 model, the last layer of the VGG-Net model is replaced with a specific layer according to our requirements and various functions are optimized to reduce overfitting. In the VGG-MI2 model, one layer of the VGG…
Energy Autonomous Wearable Sensors (EAWS) have attracted a large interest due to their potential to provide reliable measurements and continuous bioelectric signals, which help to reduce health risk factors early on, ongoing assessment for disease prevention, and maintaining optimum, lifelong health quality. This review paper presents recent developments and state-of-the-art research related to three critical elements that enable an EAWS. The first element is wearable sensors, which monitor human body physiological signals and activities. Emphasis is given on explaining different types of tran…
Malaviya National Institute of Technology（马拉维亚国家理工学院）；Maharaja Surajmal Institute of Technology（马哈拉贾·苏拉杰马尔理工学院）
In the field of neuroscience, Brain activity analysis is always considered as an important area. Schizophrenia(Sz) is a brain disorder that severely affects the thinking, behaviour, and feelings of people all around the world. Electroencephalography (EEG) is proved to be an efficient biomarker in Sz detection. EEG is a non-linear time-seriesi signal and utilizing it for investigation is rather crucial due to its non-linear structure. This paper aims to improve the performance of EEG based Sz detection using a deep learning approach. A novel hybrid deep learning model known as SzHNN (Schizophre…
Improving efficiency of healthcare systems is a top national interest worldwide. However, the need of delivering scalable healthcare services to the patients while reducing costs is a challenging issue. Among the most promising approaches for enabling smart healthcare (s-health) are edge-computing capabilities and next-generation wireless networking technologies that can provide real-time and cost-effective patient remote monitoring. In this paper, we present our vision of exploiting multi-access edge computing (MEC) for s-health applications. We envision a MEC-based architecture and discuss t…
Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cl…
To best of the authors knowledge, this article presents the first-ever non-invasive glucometer that takes into account serum glucose for high accuracy. In case of blood glucose measurement, serum glucose value has always been considered precise blood glucose value during prandial modes. Serum glucose can be measured in laboratory and more stable glucose level compare to capillary glucose. However, this invasive approach is not convenient for frequent measurement. Sometimes, Conventional invasive blood glucose measurement may be responsible for cause of trauma and chance of blood related infect…
The use of cloud services that process privacy-sensitive information such as digital banking, pervasive healthcare, smart home applications requires an implicit continuous authentication solution which will make these systems less vulnerable to the spoofing attacks. Physiological signals can be used for continuous authentication due to their personal uniqueness. Ubiquitous wrist-worn wearable devices are equipped with photoplethysmogram sensors which enable to extract heart rate variability (HRV) features. In this study, we show that these devices can be used for continuous physiological authe…
