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2012 25th International Symposium on Computer-Based Medical Systems

Anatomizing the robustness of multichannel MAC protocols for WSNs: An evaluation under MAC oriented design issues impacting QoS

A new deep learning architecture, which is heart rate monitoring system using feature extraction in electrocardiogram signal by Convolutional Neural Network (CNN). Electrocardiogram based healthcare applications is presented in a federated context. The proposed system correctly diagnoses arrhythmias using an auto encoder and a classifier, both based on CNN. The module is provided to explain the classification findings in which the proposed classifier via employing an auto encoder and a classifier could check whether the rhythms of heart are normal, paced up or the heartbeat rate is irregular depending on the patient's situations. The module could offer the explanations of the classification findings in order to allow medical practitioners to quickly make the trustworthy judgments in preliminary diagnoses. Finally, the result shows that the proposed classifier could explain the classification for finding the two arrhythmias conditions which allow healthcare practitioners to rapidly make the correct conclusions.

Heart Rate Monitoring System Using Feature Extraction in Electrocardiogram Signal by Convolutional Neural Network

This paper proposes a multi-channel ZigBee Wireless Sensor Network (WSN) with an automation monitoring system developed in-house. Monitoring is achieved using the Android NDK and SDK is used to write applications for Android embedded platforms. An access control system is included to obtain data from sensors and perform inquiries into node status. In the paper, a multi-channel architecture was used to reduce the problem of hidden terminals and enhance the packet delivery ratio (PDR).

Design Monitoring Systems for Multi-channel Technology in Wireless Sensor Networks

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Design Monitoring Systems for Multi-channel Technology in Wireless Sensor Networks