Implementation of an Automated ECG-based Diagnosis Algorithm for a Wireless Body Sensor Plataform

F. Rincón, L. Gutiérrez, M. Jiménez, Víctor Díaz, N. Khaled, David Atienza Alonso, M. Sanchez-Elez, J. Recas, G. Micheli
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引用次数: 7

Abstract

Wireless Body Sensor Networks (WBSN) are poised to become a key enabling technology of personal systems for pervasive healthcare. Recent results have however shown that the conventional approach to their design, which consists in continuous wireless streaming of the sensed data to a central data collector, is unsustainable in terms of network lifetime and autonomy. Furthermore, it was established that wireless data communication is responsible for most of the energy consumption. To address the energy inefficiency of conventional WBSNs, we advocate an advanced WBSN concept where sensor nodes exploit their available, yet limited processing and storage resources to deploy advanced embedded intelligence and processing, to reduce the amount of wireless data communication and consequently energy consumption. More specifically, this paper addresses the design and optimization of an automated real-time electrocardiogram (ECG) signal analysis and cardiovascular arrhythmia diagnosis application for a prototype sensor node called Wireless 25 EEG/ECG system. The satifactory accuracy of this on-line automated ECG-based analysis and diagnosis system is assessed and compared to the salient off-line automated ECG analysis algorithms. More importantly, our results show an energy consumption reduction of 80% to 100% with respect to conventional WBSNs, when our analysis and diagnosis algorithm is used to process the sensed ECG data to extract its relevant features, which are then wirelessly reported to the WBSN central data collector, after the node can automatically determine the potential cardiovascular pathology without human monitoring.
基于心电图的无线身体传感器自动诊断算法的实现
无线身体传感器网络(WBSN)有望成为普及医疗保健的个人系统的关键使能技术。然而,最近的结果表明,传统的设计方法,即将感测数据连续无线传输到中央数据收集器,在网络寿命和自主性方面是不可持续的。此外,无线数据通信是造成大部分能源消耗的原因。为了解决传统WBSN的能源效率低下的问题,我们提倡一种先进的WBSN概念,其中传感器节点利用其可用但有限的处理和存储资源来部署先进的嵌入式智能和处理,以减少无线数据通信的数量,从而减少能源消耗。更具体地说,本文讨论了一个名为Wireless 25 EEG/ECG系统的原型传感器节点的自动实时心电图(ECG)信号分析和心血管心律失常诊断应用的设计和优化。对该在线自动心电分析诊断系统的准确性进行了评估,并与主要的离线自动心电分析算法进行了比较。更重要的是,我们的研究结果表明,当使用我们的分析和诊断算法处理感测的心电数据以提取其相关特征,然后无线报告给WBSN中心数据采集器后,节点可以自动确定潜在的心血管病理,而无需人工监测,与传统的WBSN相比,我们的能耗降低了80%至100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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