A Proof-of-Concept of Ultra-Edge Smart IoT Sensor: A Continuous and Lightweight Arrhythmia Monitoring Approach
A Proof-of-Concept of Ultra-Edge Smart IoT Sensor: A Continuous and Lightweight Arrhythmia Monitoring Approach
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DOI：
10.1109/access.2021.3056509
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Alasmary, Waleed
中科院分区：
文献类型：
--
作者：
Sakib, Sadman;Fouda, Mostafa M.;Alasmary, Waleed
由于物联网 (IoT) 的普及，物联网设备在边缘网络的使用率大大提高。传统上，物联网设备缺乏进行超边缘分析所需的计算资源。在本文中，我们超越了典型的边缘分析范式（主要限于用户智能手机），并研究如何将智能嵌入到超边缘物联网传感器中。为了概念化具有增强智能的智能物联网传感器，我们选择使用心电图（ECG）跟踪的心律失常检测任务作为移动健康（mHealth）案例之一。由于广泛的噪声过滤和手动特征提取阶段，现有方法对于超边缘物联网传感器不可行。因此，在本文中，为了便于分析，我们提出了一种基于深度学习的轻量级心律失常分类（DL-LAC）方法，该方法仅采用单导联心电图迹线，不需要噪声过滤和手动特征提取步骤。作为所提出的技术，我们设计了一个一维卷积神经网络（CNN）架构。符合 ANSI/AAMI EC57:1998 标准，将四种心跳类型视为类别标签。使用 PhysioNet 的四个不同数据集评估了所提出模型的效率和泛化能力。实验结果表明，所提出的深度学习方法优于基于延迟微分方程（DDE）的优化、K近邻（KNN）和随机森林（RF）等传统方法。当训练好的模型转移到连接到物联网传感器的虚拟化微控制器时，所提出的 DL-LAC 在时间和内存需求方面表现出了令人鼓舞的性能。
Due to the proliferation of the Internet of Things (IoT), the IoT devices are becoming utilized at the edge network at a much higher rate. Conventionally, the IoT devices lack the computation resources required for carrying out ultra-edge analytics. In this paper, we go beyond the typical edge analytics paradigm, which is mostly limited to user-smartphones, and investigate how to embed intelligence into the ultra-edge IoT sensors. To conceptualize the smart IoT sensors with enhanced intelligence, we select the arrhythmia detection task employing Electrocardiogram (ECG) trace as one of the mobile health (mHealth) cases. The existing approaches are not feasible for ultra-edge IoT sensors due to the extensive noise-filtering and manual feature extraction phase. Hence, in this paper, to facilitate the analytics, we propose a Deep Learning-based Lightweight Arrhythmia Classification (DL-LAC) method, which employs only single-lead ECG trace and does not require noise-filtering and manual feature extraction steps. As the proposed technique, we design a one-dimensional Convolutional Neural Network (CNN) architecture. Complying with the ANSI/AAMI EC57:1998 standard, four heartbeat types are taken into consideration as class labels. The efficiency and the generalization ability of the proposed model are evaluated, employing four different datasets from PhysioNet. The experimental results demonstrate that the proposed DL method outperforms traditional methods such as the Delay Differential Equation (DDE)-based optimization, K-Nearest Neighbor (KNN), and Random Forest (RF). The proposed DL-LAC illustrates encouraging performance in terms of time and memory requirement when the trained model is transferred to virtualized microcontrollers connected to IoT sensors.