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一种用于医学评估的高级仿生光电容积脉搏波（PPG）和心电图（ECG）模式识别系统。

An Advanced Bio-Inspired PhotoPlethysmoGraphy (PPG) and ECG Pattern Recognition System for Medical Assessment.

机构信息

STMicroelectronics-ADG Central R&D, 95121 Catania, Italy.

Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy.

出版信息

Sensors (Basel). 2018 Jan 30;18(2):405. doi: 10.3390/s18020405.

DOI:10.3390/s18020405
PMID:29385774
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5855408/
Abstract

Physiological signals are widely used to perform medical assessment for monitoring an extensive range of pathologies, usually related to cardio-vascular diseases. Among these, both PhotoPlethysmoGraphy (PPG) and Electrocardiography (ECG) signals are those more employed. PPG signals are an emerging non-invasive measurement technique used to study blood volume pulsations through the detection and analysis of the back-scattered optical radiation coming from the skin. ECG is the process of recording the electrical activity of the heart over a period of time using electrodes placed on the skin. In the present paper we propose a physiological ECG/PPG "combo" pipeline using an innovative bio-inspired nonlinear system based on a reaction-diffusion mathematical model, implemented by means of the Cellular Neural Network (CNN) methodology, to filter PPG signal by assigning a recognition score to the waveforms in the time series. The resulting "clean" PPG signal exempts from distortion and artifacts is used to validate for diagnostic purpose an EGC signal simultaneously detected for a same patient. The multisite combo PPG-ECG system proposed in this work overpasses the limitations of the state of the art in this field providing a reliable system for assessing the above-mentioned physiological parameters and their monitoring over time for robust medical assessment. The proposed system has been validated and the results confirmed the robustness of the proposed approach.

摘要

生理信号被广泛用于进行医学评估，以监测广泛的病理，通常与心血管疾病有关。在这些信号中，光电体积描记法（PPG）和心电图（ECG）信号是应用最广泛的。PPG 信号是一种新兴的非侵入性测量技术，通过检测和分析皮肤发出的反向散射光来研究血液体积的脉动。心电图是通过在皮肤上放置电极来记录一段时间内心脏的电活动的过程。在本文中，我们提出了一种生理 ECG/PPG“组合”管道，使用基于反应扩散数学模型的创新生物启发式非线性系统，通过细胞神经网络（CNN）方法实现，通过为时间序列中的波形分配识别分数来过滤 PPG 信号。从失真和伪影中“净化”的 PPG 信号被用于验证同时为同一患者检测到的 EGC 信号的诊断目的。本文提出的多站点组合 PPG-ECG 系统克服了该领域现有技术的局限性，为评估上述生理参数及其随时间的监测提供了可靠的系统，以进行稳健的医学评估。所提出的系统已经过验证，结果证实了所提出方法的稳健性。

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