scholarly journals FETCH: A deep Learning-based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis

IEEE Access ◽  
2022 ◽  
pp. 1-1
Author(s):  
Parag Verma ◽  
Rajeev Tiwari ◽  
Wei-Chiang Hong ◽  
Shuchi Upadhyay ◽  
Yi-Hsuan Yeh
Keyword(s):  
Deep Learning ◽  
Fog Computing ◽  
Monitoring And Diagnosis ◽  
Integrated Environment ◽  
Healthcare Monitoring

scholarly journals A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

2021 ◽  
Vol 11 (5) ◽  
pp. 668
Author(s):  
Sani Saminu ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Isselmou Abd El Kader ◽  
Adamu Halilu Jabire ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Performance Comparison ◽  
Eeg Signal ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Monitoring And Diagnosis

The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.

scholarly journals Secure and failure hybrid delay enabled a lightweight RPC and SHDS schemes in Industry 4.0 aware IIoHT enabled fog computing

2021 ◽  
Vol 19 (1) ◽  
pp. 513-536
Author(s):  
Mazhar Ali Dootio ◽  
◽  
Abdullah Lakhan ◽  
Ali Hassan Sodhro ◽  
Tor Morten Groenli ◽  
...  
Keyword(s):  
Model Simulation ◽  
Blood Pressure Monitoring ◽  
Fog Computing ◽  
Healthcare Applications ◽  
Linear Integer Programming ◽  
Industrial Internet ◽  
Healthcare Monitoring ◽  
Internet Of Healthcare Things ◽  
Security Network ◽  
The Mathematical Model

<abstract><p>These days, the Industrial Internet of Healthcare Things (IIT) enabled applications have been growing progressively in practice. These applications are ubiquitous and run onto the different computing nodes for healthcare goals. The applications have these tasks such as online healthcare monitoring, live heartbeat streaming, and blood pressure monitoring and need a lot of resources for execution. In IIoHT, remote procedure call (RPC) mechanism-based applications have been widely designed with the network and computational delay constraints to run healthcare applications. However, there are many requirements of IIoHT applications such as security, network and computation, and failure efficient RPC with optimizing the quality of services of applications. In this study, the work devised the lightweight RPC mechanism for IIoHT applications and considered the hybrid constraints in the system. The study suggests the secure hybrid delay scheme (SHDS), which schedules all healthcare workloads under their deadlines. For the scheduling problem, the study formulated this problem based on linear integer programming, where all constraints are integer, as shown in the mathematical model. Simulation results show that the proposed SHDS scheme and lightweight RPC outperformed the hybrid for IIoHT applications and minimized 50% delays compared to existing RPC and their schemes.</p></abstract>

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 113-113
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Learning ◽  
Neural Network Model ◽  
Skeletal Maturity ◽  
Low Complexity ◽  
Noise Detection ◽  
Healthcare Monitoring ◽  
Deep Learning Neural Network ◽  
Hand Radiographs

Bote JM, Recas J, Rincon F, Atienza D, Hermida R. A modular low-complexity ECG delineation algorithm for real-time embedded systems. IEEE J Biomed Health Inform 2018;22(2):429-41 https://dx.doi.org/10.1109/JBHI.2017.2671443 Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C, Leijenaar RT, Haibe-Kains B, Lambin P, Gillies RJ, Aerts HJ. Defining the biological basis of radiomic phenotypes in lung cancer. ELife 2017;6:e23421 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28731408/ Larson DB, Chen MC, Lungren MP, Halabi SS, Stence NV, Langlotz CP. Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 2018; 287(1):313-22 http://pubs.rsna.org/doi/10.1148/radiol.2017170236?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed Satija U, Ramkumar B, Manikandan MS. Automated ECG noise detection and classification system for unsupervised healthcare monitoring. IEEE J Biomed Health Inform 2018;22(3):722-32 https://dx.doi.org/10.1109/JBHI.2017.2686436

scholarly journals An IPv6-based framework for fog-assisted healthcare monitoring

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401881951 ◽  
Author(s):  
Jianqiang Hu ◽  
Keshou Wu ◽  
Wei Liang
Keyword(s):  
Cloud Computing ◽  
Chronic Diseases ◽  
Fog Computing ◽  
Internet Protocol ◽  
Cloud Layer ◽  
Classification Model ◽  
Physiological Data ◽  
Response Delay ◽  
Healthcare Monitoring ◽  
Computing Node

The new generation healthcare monitoring system combines technologies of wireless body sensor network, cloud computing, and Bigdata, and there are still limitations in protocol security, response delay, and prediction of potential severity disease. In response to the above situation, an Internet Protocol Version 6 (IPv6)-based framework for fog-assisted healthcare monitoring is proposed. This framework is composite of body-sensing layer, fog layer, and cloud layer. The body-sensing layer generates physiological data, and fog computing node in fog layer collects and analyses time-sensitive data. Fog layer sends physiological data to cloud computing node in cloud layer for further processing. Mobile intelligent device connects fog computing node and helps individuals to predict the potential disease with its level of severity. The proposed framework uses advanced techniques such as IPv6-based network architecture, cloud–fog resource scheduling algorithm based on time threshold, and classification model of chronic diseases based on cascaded deep learning and so on. In order to determine the validity of the framework, health data were systematically generated from 45 patients for 30 days. Results depict that the proposed classification model of chronic diseases has high accuracy in determining the level of severity of potential disease. Moreover, response delay is much lower than Internet Protocol Version 4 (IPv4)-based cloud-assisted environment.

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