%0 Conference Proceedings
%T IoT Platform for Real-Time Multichannel ECG Monitoring and Classification with Neural Networks
%+ Fudan University [Shanghai]
%+ University of Turku
%A Granados, Jose
%A Westerlund, Tomi
%A Zheng, Lirong
%A Zou, Zhuo
%Z Part 5: Intelligent Electronics and Systems for Industrial IoT
%< avec comité de lecture
%( Lecture Notes in Business Information Processing
%B 11th International Conference on Research and Practical Issues of Enterprise Information Systems (CONFENIS)
%C Shanghai, China
%Y A Min Tjoa
%Y Li-Rong Zheng
%Y Zhuo Zou
%Y Maria Raffai
%Y Li Da Xu
%Y Niina Maarit Novak
%I Springer International Publishing
%3 Research and Practical Issues of Enterprise Information Systems
%V LNBIP-310
%P 181-191
%8 2017-10-18
%D 2017
%R 10.1007/978-3-319-94845-4_16
%K IoT
%K ECG
%K Healthcare
%K AI
%K Neural networks
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 8
%Z WG 8.9
%2 https://inria.hal.science/hal-01888638/document
%2 https://inria.hal.science/hal-01888638/file/470174_1_En_16_Chapter.pdf
%L hal-01888638
%U https://inria.hal.science/hal-01888638
%~ SHS
%~ IFIP
%~ IFIP-TC
%~ IFIP-LNBIP
%~ IFIP-WG
%~ IFIP-TC8
%~ IFIP-WG8-9
%~ IFIP-CONFENIS
%~ IFIP-LNBIP-310