论文标题
使用增强的卷积神经网络在物联网中有效分类ECG信号
Effective classification of ECG signals using enhanced convolutional neural network in IOT
论文作者
论文摘要
在本文中，建议采用基于物联网技术的新型心电图监测方法。本文提出了一个基于动态源路由（DSR）的物联网医疗平台的路由系统，并通过能源和链路质量（REL）进行路由。此外，在本研究中测试了人工神经网络（ANN），支持向量机（SVM）和卷积神经网络（CNN）基于ECG信号分类的方法。 Deep-ECG将采用Deep CNN提取重要特征，然后将使用简单和快速距离功能进行比较，以有效地对心脏问题进行分类。这项工作提出了用于从移动手表用户获得的ECG数据分类的算法，以识别异常数据。马萨诸塞州理工学院（MIT）和贝丝以色列医院（MIT/BIH）心律失常数据库已用于实验建议的方法。结果表明，所提出的策略在分类准确性方面优于他人。
In this paper, a novel ECG monitoring approach based on IoT technology is suggested. This paper proposes a routing system for IoT healthcare platforms based on Dynamic Source Routing (DSR) and Routing by Energy and Link Quality (REL). In addition, the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Convolution Neural Networks (CNNs)-based approaches for ECG signal categorization were tested in this study. Deep-ECG will employ a deep CNN to extract important characteristics, which will then be compared using simple and fast distance functions in order to classify cardiac problems efficiently. This work has suggested algorithms for the categorization of ECG data acquired from mobile watch users in order to identify aberrant data. The Massachusetts Institute of Technology (MIT) and Beth Israel Hospital (MIT/BIH) Arrhythmia Database have been used for experimental verification of the suggested approaches. The results show that the proposed strategy outperforms others in terms of classification accuracy.
