EdgeCNN: A Hybrid Architecture for Agile Learning of Healthcare Data from IoT Devices

Yu, Jian; Fu, Bin; Cao, Ao; He, Zhenqian; Wu, Di

doi:10.1109/padsw.2018.8644604

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…Several works have considered NFV, network slicing and a hybrid IoT Edge/Cloud architecture to pave the way for health data analytics. In [8] authors consider a hybrid Edge/Cloud IoT architecture for the problem of ECG classification. They consider IoT devices that generate health data, then such raw data is sent to edge servers that implement the ECG classification based on a DL algorithm consisting of a Convolutional Neural Network (CNN).…”

Section: Related Workmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

“…• Herein a hybrid Edge-Cloud IoT architecture is considered as in [8]. However, unlike in [8] herein NFV technology, an cloud-centric paradigms for the development of applications is leveraged for health data analytics. Thereby, our resulting architecture is more flexible, programmable, and e cient in terms of computing and storage resources.…”

Section: Contributionsmentioning

confidence: 99%

“…This allows low latency and circumvents any privacy concerns as data is not stored. Note that our approach leverages the benefits of a hybrid Edge/Cloud architecture more properly than the related work [8]. As they do both training and deployment at the Edge and do not consider the Cloud for training purposes.…”

Section: Output Layer Ecg Time Series Predictionmentioning

confidence: 99%

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CURATE: On-Demand Orchestration of Services for Health Emergencies Prediction and Mitigation

Sanabria-Russo

Serra

Pubill

et al. 2021

IEEE J. Select. Areas Commun.

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Telemedicine, or the ability granted to doctors to remotely assist patients has been greatly benefited by advances in IoT, network communications, Machine Learning and Edge/Cloud computing. With the impeding arrival of 5G, virtualized infrastructures and cloud-native approaches enable the execution of unprecedented procedures during such patient/doctor interactions, allowing medical professionals to e.g. request higher granularity metrics from patients' telemetry equipment, or perform on-demand data mining/processing of patient's stored data in order to provide a more educated diagnostic or prediction.In this work we coalesce the virtues of virtualized infrastructures and IoT into a solution able to satisfy increasing data processing demands for eHealth, e.g. for telemedicine applications, remote assistance or patient pre-screening procedures. The proposed platform, CURATE, leverages Network Functions Virtualisation Management and Orchestration (NFV MANO) for the on-demand instantiation of the required virtual resources on the operator's infrastructure, as well as the concept of 5G Network Slices to guarantee e cient resource allocation and tenant isolation. Results show the proposed platform is able to e ciently make use of the available hardware resources via Network Slices, as well as provide cost-e ective service guarantees employing dynamic scaling operations.

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Section: Related Workmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Output Layer Ecg Time Series Predictionmentioning

confidence: 99%

See 2 more Smart Citations

CURATE: On-Demand Orchestration of Services for Health Emergencies Prediction and Mitigation

Sanabria-Russo

Serra

Pubill

et al. 2021

IEEE J. Select. Areas Commun.

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“…The experimental results show that, after the data enhancement of ECG data related categories, not only the overall accuracy of the deep learning model is improved, but also the ECG categories that the model can diagnose are greatly expanded, which greatly improves the practical usability of the system. This paper is an extended and enhanced version of an earlier conference paper published in IEEE 24th International Conference on Parallel and Distributed Systems [7]. Our initial conference paper does not address the problem of the imbalance of ECG data and its impact on diagnostic accuracy.…”

Section: Introductionmentioning

confidence: 99%

Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks

et al. 2020

Applied Sciences

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Wireless and mobile health applications promote the development of smart healthcare. Effective diagnosis and feedbacks of remote health data pose significant challenges due to streaming data, high noise, network latency and user privacy. Therefore, we explore efficient edge and cloud design to maintain electrocardiogram classification performance while reducing the communication cost. These contributions include: (1) We introduce a hybrid smart medical architecture named edge convolutional neural networks (EdgeCNN) that balances the capability of edge and cloud computing to address the issue for agile learning of healthcare data from IoT devices. (2) We present an effective deep learning model for electrocardiogram (ECG) inference, which can be deployed to run on edge smart devices for low-latency diagnosis. (3) We design a data enhancement method for ECG based on deep convolutional generative adversarial network to expand ECG data volume. (4) We carried out experiments on two representative datasets to evaluate the effectiveness of the deep learning model of ECG classification based on EdgeCNN. EdgeCNN shows superior to traditional cloud medical systems in terms of network Input/Output (I/O) pressure, architecture cost and system high availability. The deep learning model not only ensures high diagnostic accuracy, but also has advantages in aspect of inference time, storage, running memory and power consumption.

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The Emergence of Hybrid Edge-Cloud Computing for Energy Efficiency in Buildings

Himeur

Alsalemi

Bensaali

et al. 2021

Lecture Notes in Networks and Systems

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EdgeCNN: A Hybrid Architecture for Agile Learning of Healthcare Data from IoT Devices

Cited by 15 publications

References 13 publications

CURATE: On-Demand Orchestration of Services for Health Emergencies Prediction and Mitigation

CURATE: On-Demand Orchestration of Services for Health Emergencies Prediction and Mitigation

Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks

The Emergence of Hybrid Edge-Cloud Computing for Energy Efficiency in Buildings

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