基于改进的非负矩阵分解技术的抗运动干扰心电信号感知方法  

Motion-Robust Electrocardiogram Signal Sensing Based on Modified Non-Negative Matrix Factorization

作　　者：曹烨彤 李凡[2] 刘晓晨 谢睆冉 陈慧杰 CAO Ye-tong;LI Fan;LIU Xiao-chen;XIE Huan-ran;CHEN Hui-jie(School of Computer Science and Technology,Shandong University,Qingdao,Shandong 266237,China;School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China;College of Computer Science,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]山东大学计算机科学与技术学院,山东青岛266237 [2]北京理工大学计算机学院,北京100081 [3]北京工业大学计算机学院,北京100124

出　　处：《电子学报》2024年第12期4153-4165,共13页Acta Electronica Sinica

基　　金：国家自然科学基金(No.62372045,No.62072040,No.62202019);中国博士后科学基金(No.2021M700302)。

摘　　要：连续监测心电信号对于加强心血管疾病的早期筛查和诊断至关重要.然而,现有的心电信号监测方法存在依赖昂贵设备、依赖用户执行特殊操作、应用场景受限等弊端,无法满足广泛人群在日常生活状态下长期连续监测心电的迫切需求.为了克服上述问题,本研究提出了一种基于改进的非负矩阵分解技术的抗运动干扰心电信号感知方法 .其基本思想是利用成本低廉的腕戴式智能设备集成的陀螺仪,连续感知身体振动中隐含的心脏活动信息并生成细粒度心电信号.为了有效应对身体运动干扰难以消除的固有挑战,本研究提出了一种基于改进的非负矩阵分解技术的创新方法 .该方法能够在未经训练的情况下,成功提取因心跳引发的微弱心冲击振动信号,有效克服运动干扰问题.此外,针对心冲击振动信号在心动周期中波形动态性强且缺乏明确起止点的特点,本研究首次提出了四种全新的形态特征,并结合机器学习算法,精准识别心冲击振动信号中的尖峰点,从而实现对心动周期的精确切分.最后,本研究基于循环生成对抗网络,构建了心冲击振动信号与心电信号之间的映射关系.得益于该网络的创新设计,模型在无需用户提供训练数据的情况下,也能高效生成精准的心电波形.本研究对18位志愿者进行了大量实验,结果表明所提出的连续心电信号监测方法非常有效,抗运动干扰效果显著.在身体静止和运动的情况下,平均幅值误差分别为7.92%和9.02%,均满足医学标准规定的误差范围低于10%的要求.Continuous electrocardiogram(ECG)monitoring is crucial for effectively preventing and diagnosing car-diovascular diseases.However,existing ECG monitoring methods are limited by their reliance on expensive equipment un-available to common users,the stringent requirements of the monitoring process,and confined application scenarios,mak-ing them insufficient to meet the urgent need for long-term continuous ECG monitoring of the general population in their daily lives.Given these limitations,this study proposes a motion-robust ECG signal sensing method based on modified non-negative matrix factorization(NMF).The basic idea is to leverage a gyroscope embedded into a low-cost wrist-worn wear-able to characterize cardiac activities encoded into body vibrations and interpret them to generate fine-grained ECG signals accurately.As eliminating body motion interference is inherently hard,this work innovatively employs modified NMF to tackle the problem;this can effectively handle body motion interference,even if untrained,and extract the cardiogenic body vibrations from noisy gyroscope data.Due to the lack of clear pattern of cardiogenic body vibrations in each cardiac cycles,current cardiac cycle segmentation solutions cannot be applied.Thus,this work deeply analyses the morphological features of cardiogenic body vibrations and utilizes machine learning techniques for the identification of spike points for segmenta-tion.Finally,cycle generative adversarial network(CycleGAN)framework is employed to construct a correlation mapping model between the cardiogenic body vibrations and the ECG signals.With innovative construction,this model can accurate generation of the ECG signals without the need for a huge amount of training data.Extensive experiments with 18 volun-teers confirm the effectiveness of the proposed method,with the average amplitude errors of 7.92%and 9.02%for station-ary and moving scenarios,respectively.These values fall well within the acceptable range of medical standards for error tol-erance of less than 10%.

关 键 词：心电 腕戴式智能设备 心冲击振动 非负矩阵分解 循环生成对抗网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]