Skip to Main content Skip to Navigation
Conference papers

IoT Platform for Real-Time Multichannel ECG Monitoring and Classification with Neural Networks

Abstract : Internet of Things (IoT) platforms applied to health promise to offer solutions to the challenges in healthcare systems by providing tools for lowering costs while increasing efficiency in diagnostics and treatment. Many of the works on this topic focus on explaining the concepts and interfaces between different parts of an IoT platform, including the generation of knowledge based on smart sensors gathering bio-signals from the human body which are processed by data mining and more recently, deep neural networks hosted on cloud computing infrastructure. These techniques are designed to serve as useful intelligent companions to healthcare professionals in their practice. In this work we present details about the implementation of an IoT Platform for real-time analysis and management of a network of bio-sensors and gateways, as well as the use of a cloud deep neural network architecture for the classification of ECG data into multiple cardiovascular conditions.
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/hal-01888638
Contributor : Hal Ifip <>
Submitted on : Friday, October 5, 2018 - 11:21:48 AM
Last modification on : Saturday, October 6, 2018 - 1:14:36 AM
Long-term archiving on: : Sunday, January 6, 2019 - 2:22:00 PM

File

470174_1_En_16_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Jose Granados, Tomi Westerlund, Lirong Zheng, Zhuo Zou. IoT Platform for Real-Time Multichannel ECG Monitoring and Classification with Neural Networks. 11th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS), Oct 2017, Shanghai, China. pp.181-191, ⟨10.1007/978-3-319-94845-4_16⟩. ⟨hal-01888638⟩

Share

Metrics

Record views

120

Files downloads

16