CN114580477B - 一种基于多时间序列融合的可穿戴动态呼吸率估测系统 - Google Patents
一种基于多时间序列融合的可穿戴动态呼吸率估测系统 Download PDFInfo
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Abstract
该发明公开了一种基于多时间序列融合的可穿戴动态呼吸率估测系统,属于信号处理领域。针对现有的呼吸率装置不方便、不连续、测量精度不高,且存在舒适度不高等缺点,本发明提出基于多时间序列融合的方法对多个呼吸调制信号在时间维度进行融合,再基于融合得到的呼吸信号进行呼吸率估测,可以更加精确的估计呼吸率,提高了呼吸率估测的实时性和连续性,通过一个可穿戴设备同时实现多种生理参数的检测。利用PDR信号和EDR信号可以提高呼吸率估测的稳定性,改善由于受试者的运动或则外部干扰对ECG信号或则PPG信号引起大的改变,引起某个调制信号发生大的改变,从而引起呼吸率估计误差的增大。
Description
技术领域
本发明属于信号处理领域,特别是呼吸率测量技术领域,具体设计了一种呼吸率估测方法和呼吸率测量系统。
背景技术
呼吸率是临床诊断疾病最重要的生理参数之一,一些疾病在发病初期会出现呼吸率突然升高或降低的情况,且呼吸率的改变可能先于心率、血压等常见生理参数。因此,对呼吸率进行无创连续实时监测有利于及时检测疾病异常恶化情况,可以有效预防、诊断及提高疾病救治率。通过单个可穿戴设备实现呼吸、心率、血压、血氧及体温的精确实时检测,可以更好地监测人体健康状况。
传统的测量呼吸率的方法有直接测量法和间接测量法。直接测量法主要有胸阻抗法、呼吸气流法和呼吸音测量方法,测量精度高,常作为呼吸率评价的金标准,不过这些方法的缺点是:有侵扰性、设备繁琐、稳定性差及容易受到测试者运动干扰,因此不适合日常检测。另一种间接测量方法主要有:基于肌电、动脉血压、基于心电(Electrocardiograph,ECG)和基于脉搏波(Photoplethysmography,PPG)等信号获取呼吸率的方法。其中基于肌电和动脉血压的测量方法对信号采集和处理要求较高,设备繁琐,成本较高。
由于心血管系统与呼吸系统之间内在的协调机制及其相互作用,ECG和PPG信号中存在能反映呼吸活动的信号成分。基于此,有大量研究工作围绕ECG或PPG信号进行呼吸率间接估测,主要可以分为以下四类:
第一类:利用ECG或PPG直接进行功率谱密度分析或则熵谱密度分析,可以直接得到呼吸率,但是由于PPG和ECG中包含的生理信息比较丰富,多种信号存在频率重叠的情况,不容易直接准确的获取呼吸率,如文献Adami A,Boostani R,Marzbanrad F,Charlton PH:A New Framework to Estimate Breathing Rate From Electrocardiogram,Photoplethysmogram,and Blood Pressure Signals.IEEE Access 2021,9:45832-45844.和Garde A,Karlen W,Ansermino JM,Dumont GA:Estimating respiratory and heartrates from the correntropy spectral density of the photoplethysmogram.PLoSOne 2014,9(1):e86427。
第二类:从ECG和PPG信号中提取脉搏波传导时间,再进行功率谱密度分析,得到呼吸率,如文献Ding X,Yan BP,Karlen W,Zhang YT,Tsang HK:Pulse transit time basedrespiratory rate estimation with singular spectrum analysis.Med Biol EngComput 2020,58(2):257-266。
第三类:从ECG或则PPG中提取呼吸相关的调制信号,如:幅度调制、频率调制和基线漂移等信号,对每类信号分别进行呼吸率估计,再对不同类调制信号所估测得到的多个呼吸率值进行平均、中值、加权平权,自回归模型或则有选择地排除异常呼吸率值之后再进行融合得到最终估测值,如文献Peter H Charlton5,2,Timothy Bonnici5,1,4,LionelTarassenko2,David AClifton2,Richard Beale1 and Peter J Watkinson3:Anassessment of algorithms to estimate respiratory rate from theelectrocardiogram and photoplethysmogram.2016Institute of Physics andEngineering in Medicine 2016.和Pimentel MAF,Johnson AEW,Charlton PH,Birrenkott D,Watkinson PJ,Tarassenko L,Clifton DA:Toward a Robust Estimationof Respiratory Rate From Pulse Oximeters.IEEE Trans Biomed Eng 2017,64(8):1914-1923.。这些方法可能为了保证呼吸精度而设定了较高的呼吸率融合标准,从而导致大量的数据被抛弃,虽然这样得到的误差减小了,但其不能反应其真实误差,从而具有的实用价值有限。
第四类:利用从ECG或则PPG中提取呼吸相关的调制信号,对这些调制信号在时域上进行一个融合,而不是呼吸率值层面上的融合,例如:Lakdawala从ECG信号中提取R波的幅度变化和R波的持续时间作为两类信号,通过点对点相乘得到融合后的呼吸信号,再进行呼吸率的估计[17]。以及Khreis等人利用kalman滤波器和呼吸质量评价指标对ECG中提取的4类呼吸相关的调制信号进行有选择的融合,如文献Khreis S,Ge D,Rahman HA,CarraultG:Breathing Rate Estimation Using Kalman Smoother With Electrocardiogram andPhotoplethysmogram.IEEE Trans Biomed Eng2020,67(3):893-904.。
考虑到在呼吸率层面进行融合,无法最大限度利用ECG或PPG信号中的呼吸相关信息,本专利提出基于多时间序列融合的方法对多个呼吸调制信号在时间维度进行融合,再基于融合得到的呼吸信号进行呼吸率估测,以期提高呼吸率估测精度。
发明内容
本发明针对现有的呼吸率装置不方便、不连续、测量精度不高,且存在舒适度不高等缺点进行改进,提供了一种从ECG和PPG中获取呼吸率的方法和呼吸率测量系统。
本发明技术方案为一种基于多时间序列融合的可穿戴动态呼吸率估测系统,该系统包括:信号采集及预处理单元、特征提取单元、特征预处理单元、融合单元、呼吸率估测单元;
所述信号采集及预处理单元首先采集目标脉搏波信号(PPG)和心电信号(ECG),并对采集到脉搏波信号和心电信号进行预处理;预处理方法为,通过一个高通滤波器去除ECG信号和PPG信号中比较低频的噪声;预处理后的ECG和PPG还需要划分窗口,进行分段处理,窗口长度为60秒,带着57秒的重叠。再将每个窗口内的ECG和PPG传输给特征提取单元;
所述特征提取单元对收到的脉搏波信号和心电信号进行特征提取,并将提取到的特征传输给特征的预处理单元;提取的特征包括:
特征1:ECG信号的幅度调制信号(EAM),指ECG信号每一个周期内,ECG的R波幅度值差异变化,;
特征2:ECG信号的频率调制信号(EFM),指ECG信号相邻两个周期内,相邻两个R波之间的时间间隔;
特征3:ECG信号的基线漂移(EBW),指ECG同一个周期内,ECG的R波和Q波幅度值之和的一半;
特征4:PPG信号的幅度调制信号(PAM),指PPG每一个周期内,收缩期峰值的幅度差异;
特征5:PPG信号的频率调制信号(PFM),相邻两个PPG周期峰值点(收缩期结束点)之间的宽度;
特征6:PPG信号的基线漂移(PBW),指同一PPG周期内收缩期的开始和结束时,两者幅度值之和的一半;
将特征提取单元得到的6个特征一同输入特征预处理单元,具体操作步骤如下:
步骤1:利用z_score方法分别对6个特征进行规范化,使其均值为0,方差为1;
步骤2:对z_score方法规范化后6个特征再进行mapminmax归一化,使得信号都在-1和1之间波动;
步骤3:对步骤2归一化后的信号进行重采样到指定频率;
步骤4:对步骤3插值后的信号,利用巴通沃斯带通滤波进行滤波,截止频带为[0.1,1]赫兹;
接下来,将特征预处理单元处理的6个特征,输入到融合单元,融合单元包括:信号筛选部分和信号融合部分,步骤为:
步骤F1:6个特征预处理后的信号分别利用傅里叶变换进行呼吸信号质量评价指标,计算出每个信号在当前窗口内的RQI;
步骤F2:比较6个信号的RQI,从中选出3个RQI最大的信号;
步骤F3:对RQI最大的信号利用主成分分析方法进行融合,取第一主要成分作为三者融合后的呼吸信号;
最后,将融合后的呼吸信号输入到呼吸率估测单元,利用功率谱密度估计呼吸率。
进一步的,所述特征提取单元的具体计算方法分别为:
ECG的特征信号计算方法为:
步骤E1:其中ECG的R波提取是先利用基于数学形态学的方法检测到R波;
步骤E2:将步骤E1检测到的R波横坐标值,带入仅滤波处理的ECG波形中,选择每个R波前后10个点内最大值点,作为最终的R波峰值点;
步骤E3:然后通过R波峰值点定位ECG的Q波位置;
步骤E4:计算ECG的幅度调制、频率调制和基线漂移信号;
EAM=filtecg(R_peak(i))-filtecg(Q_valley(i))
EFM=R_peak(i+1)-R_peak(i)
EBW=1/2(filtecg(R_peak(i))+filtecg(Q_valley(i)))
PPG的特征信号计算方法为:
步骤P1:PPG信号每个周期内,找到收缩期斜坡上斜率最大的点;
步骤P2:通过PPG每个周期收缩期斜坡上斜率最大的点,定位找到PPG收缩期的峰值点;
步骤P3:通过PPG的峰值点,定位找到PPG的每个周期内收缩期开始的谷值点;
步骤P4:通过PPG收缩期的峰值点和谷值点,计算PPG的幅度调制、频率调制和基线漂移信号;
PAM=filtppg(S_peak(i))-filtppg(S_valley(i))
PFM=S_peak(i+1)-S_peak(i)
其中filtecg表示滤波后的ECG信号,R_peak(i)表示ECG的第i个R波峰,Q_valley表示ECG的R波位置,特征点标注如图1所示;filtppg表示滤波后的PPG信号,S_peak表示PPG的收缩期的峰值点,S_valley表示PPG收缩期的谷值点,特征点标注如图2所示。
进一步的,所述RQI的计算方法为:
对信号进行功率谱密度分析,找到(0.04,1)范围内的峰值最大的点和临近两个点的FT系数,三者的FT系数相加作为Pmax,整个合理呼吸频率范围内的FT系数相加作为PALL,计算出每个信号的RQI为:本发明的有益效果
1.更加精确的估计呼吸率,提高了呼吸率估测的实时性和连续性,仅通过一个可穿戴设备同时实现多种生理参数的检测。
2.同时利用PDR信号和EDR信号可以提高呼吸率估测的稳定性,改善由于受试者的运动或则外部干扰对ECG信号或则PPG信号引起大的改变,引起某个调制信号发生大的改变,从而引起呼吸率估计误差的增大。选出每个窗口内质量比较好的调制信号进行融合。
3.从时域上对多个呼吸相关的调制信号融合比起呼吸率层面上的融合可以提供更多呼吸相关的信息。
附图说明
图1为ECG信号的特征点;
图2为PPG信号的特征点;
图3为呼吸率测量系框图;
图4为特征预处理单元流程图;
图5为多时间序列融合流程图。
具体实施方式
一种呼吸率测量方法,包括以下步骤:
1、从可穿戴设备中获取心电信号(ECG)和光电容积脉搏波(PPG);
2、对获取的ECG和PPG信号分别利用巴通沃斯高通滤波器进行滤波;
3、从滤波后的心电信号(ECG)和光电容积脉搏波(PPG)中提取出各自的幅度调制、频率调制和基线漂移等呼吸相关的调制信号。
4、对所有呼吸相关的调制信号进行一个预处理,包括:规范化、归一化、重采样和滤波
5、利用基于傅里叶变换的呼吸质量指标选出呼吸质量指标最大的3个信号
6、利用主成分分析方法对信号对呼吸质量指标最大的3个信号进行融合
7、最后利用功率谱密度估计融合后信号的呼吸率值。
以下为基于真实数据的实例说明:
为达到上诉目的,本发明的ECG信号和PPG信号采集单元,可采用可穿戴的设备得到ECG和PPG信号,但本实验采用的是公开的Capnobase数据库,其中包含了42位受试者在手术过程中采集的8分钟长的ECG信号、PPG信号以及参考的呼吸信号,三者的采样频率都是300Hz。42位受试者中有29位未成年人(平均年龄:8.14±5.43岁)和13位成年人(平均年龄:47.21±9.02岁)。
1、通过S/5收集软件(Datex–Ohmeda,Finland)同时记录了8分钟的ECG信号、PPG信号和参考呼吸信号,其中ECG是在胸部通过3导联获得,PPG是在指端通过脉搏波血氧仪获得,参考呼吸信号是通过测量肺阻抗的变化。
2、对收集到的42名受试者的ECG信号、PPG信号和参考呼吸进行预处理,首先进行高通滤波(本实施方式中使用的Butterworth滤波器),截止频率为0.05赫兹;然后对每个受试者的信号进行分段处理,利用60秒长的窗口,带有57秒的重叠,每3秒更新一次呼吸率估测结果,提高呼吸率估测的实时性。
3、对预处理后ECG和PPG信号进行特征提取,在本实施案例中分别提取了两者在每个窗口内的幅度调制、频率调制和基线漂移,三类呼吸相关的调制信号,即EAM、EFM、EBW、PAM、PFM和PBW。
4、对提取的6个调制信号进行预处理,先利用Z-score进行规范化,再对Z_score方法规范化后的6个调制信号进行mapminmax归一化,归一化后的信号利用FFT方法进行一维重采样到300Hz的频率,重采样后的信号利用带通滤波(本实施方式中使用的Butterworth滤波器),频带为[0.1,1]赫兹。
5、特征预处理后的6个信号分别利用基于傅里叶变换的呼吸信号质量评价指标(Respiratory quality indices,RQI)分别计算出预处理后的6个调制信号在当前窗口内的RQI值。找出每个窗口内RQI最大的3个调制信号。
6、对每个窗口内RQI最大的3个调制信号利用主成分分析方法进行融合,将主成分分析后的第一主要成分作为融合后的呼吸信号。
7、对融合得到的呼吸信号利用功率谱密度进行呼吸率估计。
结果如表1
表1
表1中MAE表示平均误差;SD表示标准差;RMSE表示均方根误差;MAE表示平均绝对误差。EAM、EFM、EBW、PAM、PFM和PBW表示根据单个调制信号和参考信号之前的估计误差;ECG_PCA是指不经过筛选直接对ECG的三个调制信号EAM、EFM和EBW直接利PCA融合得到的;PPG_PCA是指不经过筛选直接对PPG的三个调制信号PAM、PFM和PBW直接利用PCA融合得到的;ALL_PCA是指直接对6个调制信号不经过RQI筛选直接进行融合得到的;ALL_PCA(RQI)是每窗口内选取RQI最大的三个调制信号,利用PCA进行融合得到的。
Claims (2)
1.一种基于多时间序列融合的可穿戴动态呼吸率估测系统,该系统包括:信号采集及预处理单元、特征提取单元、特征预处理单元、融合单元、呼吸率估测单元;
所述信号采集及预处理单元首先采集目标脉搏波信号(PPG)和心电信号(ECG),并对采集到脉搏波信号和心电信号进行预处理;预处理方法为,通过一个高通滤波器去除ECG信号和PPG信号中比较低频的噪声;预处理后的ECG和PPG还需要划分窗口,进行分段处理;
所述特征提取单元对收到的脉搏波信号和心电信号进行特征提取,并将提取到的特征传输给特征的预处理单元;提取的特征包括:
特征1:ECG信号的幅度调制信号(EAM),指ECG信号每一个周期内,ECG的R波幅度值差异变化;
特征2:ECG信号的频率调制信号(EFM),指ECG信号相邻两个周期内,相邻两个R波之间的时间间隔;
特征3:ECG信号的基线漂移(EBW),指ECG同一个周期内,ECG的R波和Q波幅度值之和的一半;
特征4:PPG信号的幅度调制信号(PAM),指PPG每一个周期内,收缩期峰值的幅度差异;
特征5:PPG信号的频率调制信号(PFM),相邻两个PPG周期峰值点之间的宽度;
特征6:PPG信号的基线漂移(PBW),指同一PPG周期内收缩期的开始和结束时,两者幅度值之和的一半;
所述特征提取单元的具体计算方法分别为:
ECG的特征信号计算方法为:
步骤E1:其中ECG的R波提取是先利用基于数学形态学的方法检测到R波;
步骤E2:将步骤E1检测到的R波横坐标值,带入仅滤波处理的ECG波形中,选择每个R波前后10个点内最大值点,作为最终的R波峰值点;
步骤E3:然后通过R波峰值点定位ECG的Q波位置;
步骤E4:计算ECG的幅度调制、频率调制和基线漂移信号;
EAM=filtecg(R_peak(i))-filtecg(Q_valley(i))
EFM=R_peak(i+1)-R_peak(i)
EBW=1/2(filtecg(R_peak(i))+filtecg(Q_valley(i)))
PPG的特征信号计算方法为:
步骤P1:PPG信号每个周期内,找到收缩期斜坡上斜率最大的点;
步骤P2:通过PPG每个周期收缩期斜坡上斜率最大的点,定位找到PPG收缩期的峰值点;
步骤P3:通过PPG的峰值点,定位找到PPG的每个周期内收缩期开始的谷值点;
步骤P4:通过PPG收缩期的峰值点和谷值点,计算PPG的幅度调制、频率调制和基线漂移信号;
PAM=filtppg(S_peak(i))-filtppg(S_valley(i))
PFM=S_peak(i+1)-S_peak(i)
其中filtecg表示滤波后的ECG信号,R_peak(i)表示ECG的第i个R波峰,Q_valley表示ECG的R波位置;filtppg表示滤波后的PPG信号,S_peak表示PPG的收缩期的峰值点,S_valley表示PPG收缩期的谷值点;
将特征提取单元得到的6个特征一同输入特征预处理单元,具体操作步骤如下:
步骤1:利用z_score方法分别对6个特征进行规范化,使其均值为0,方差为1;
步骤2:对z_score方法规范化后6个特征再进行mapminmax归一化,使得信号都在-1和1之间波动;
步骤3:对步骤2归一化后的信号进行重采样到指定频率;
步骤4:对步骤3插值后的信号,利用巴通沃斯带通滤波进行滤波,截止频带为[0.1,1]赫兹;
将特征预处理单元处理的6个特征,输入到融合单元,融合单元包括:信号筛选部分和信号融合部分,步骤为:
步骤F1:6个特征预处理后的信号分别利用傅里叶变换进行呼吸信号质量评价指标,计算出每个信号在当前窗口内的RQI;
步骤F2:比较6个信号的RQI,从中选出3个RQI最大的信号;
步骤F3:对RQI最大的信号利用主成分分析方法进行融合,取第一主要成分作为三者融合后的呼吸信号;
最后,将融合后的呼吸信号输入到呼吸率估测单元,利用功率谱密度估计呼吸率。
2.如权利要求1所述的一种基于多时间序列融合的可穿戴动态呼吸率估测系统,其特征在于,所述RQI的计算方法为:
对信号进行功率谱密度分析,找到(0.04,1)范围内的峰值最大的点和临近两个点的FT系数,三者的FT系数相加作为Pmax,整个合理呼吸频率范围内的FT系数相加作为PALL,计算出每个信号的RQI为:
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