CN111248876B - 基于压电薄膜传感信号的心率和呼吸率的计算方法 - Google Patents
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Abstract
本发明公开了一种基于压电薄膜传感信号的心率和呼吸率的计算方法,包括:以f*aHz的采样率采集原始信号,从中截取信号S并进行重采样、去噪和6层小波分解,小波分解后的第5层和第6层的细节系数,并分别进行重构相加,获取峰值点,对峰值点对应的时间间隔进行筛选后求其平均值,计算心率=60/平均值;提取小波分解后近似系数,并进行重构、滤波后获取峰值点,并对时间间隔求平均值;计算呼吸率=60/平均值。本发明采用时频结合的方法,在时域针选用了f*a的采样率和去噪方法,有利于抑制噪声,突出有用信号特征;在频域针对性的减小小波分解的失真,并对频率范围进行匹配,减少了无用信号的影响。
Description
技术领域
本发明涉及信号处理技术领域,具体的说,是基于压电薄膜传感信号的心率和呼吸率的计算方法。
背景技术
心率和呼吸率是人体最基础的生命体征指标,具有重要的生理意义。新生儿由于发育不完善,力量不足,又无法表达,加之家庭照料缺乏专业监护设备,睡眠中发生口鼻遮挡、吐奶阻塞气管、疾病等状态下出现心率或呼吸的异常,不易被及时觉察,极易延误救治,导致新生儿死亡等严重后果,具有极大的危害性。
在家庭环境下通过传感器连续实时监测新生儿心率和呼吸率具有重要意义。目前已有的方案主要基于接触式传感器检测呼吸心率,这些传感器通常置于腕带、衣物、睡袋、尿不湿、床垫等与身体近距离接触的物体表面,需要定制,价格昂贵,舒适性低,清洗不便,难以在家庭监护领域推广使用。
近年来出现的压电薄膜传感器,放置于人体下方的床垫下方也可采集到身体振动信号,传感器柔软无感且无需和身体直接接触,在新生儿的家庭监护上有良好的应用前景。但新生儿心跳呼吸表浅,传递到床垫下方的身体振动信号十分微弱,干扰多,信噪比极低。此外,新生儿的心率和呼吸率波动范围很大,心率可达60-240次/分,呼吸率可达15-120次/分,两者在频率上难以区分,常规信号处理方法无法准确计算心率和呼吸率。因此,需要开发一种可分离出心率和呼吸率的新型计算方法。
发明内容
本发明的目的在于提供基于压电薄膜传感信号的心率和呼吸率的计算方法,用于解决现有技术中压电播磨传感器采集的信息在心率和呼吸率的频率上难以区分无法准确计算心率和呼吸率的问题。
本发明通过下述技术方案解决上述问题:
一种基于压电薄膜传感信号的心率和呼吸率的计算方法,包括:
步骤S1:以f*aHz的采样率从压电薄膜传感器采集M分钟的原始信号;其中60<f<70,f∈N+,a>1且a∈N,M大于1且M∈N+,从中截取至少1分钟的信号S,且截取的信号的长度是f的整数倍,信号长度指信号的点数,为了简化后续重采样步骤,截取的信号长度为重采样后频率的倍数,新信号S’中的一个点是由原信号S中的a个点共同得到;所述压电薄膜传感器用于采集新生儿身体振动信号;
步骤S3:对信号s′进行6层小波分解,其中小波基使用sym8;
步骤S4:计算实时心率和实时呼吸率,具体包括:
步骤S411:获取小波分解后的第5层和第6层的细节系数,并分别进行重构;
步骤S412:将重构后得到的信号相加,并提取相加后信号的所有峰值点的时间点,记作th1、th2、…、thn;
步骤S413:求取所有相邻峰值点之间的时间间隔,组成序列[dth1,dth2,…,dthi,…,dthn-1],dthi=thi+1-thi,i=1,2,3,…,n-1;
步骤S414:基于先验知识,生理信号应有一个合理的范围,即去除明显过长和过短的间隔,因此从序列中去除大于1秒以及从序列中去除小于0.24秒的时间间隔,对剩余的时间间隔求取均值,记为mean_dth;
步骤S415:计算得到:心率=60/(mean_dth)次/分;
步骤S421:提取小波分解后的第5层的近似系数,并进行重构;
步骤S422:对重构后得到的信号进行高通滤波,通带截止频率为0.25Hz;
步骤S423:对高通滤波后的信号提取所有峰值点的时间点,记为tb1、tb2、…、tbm;
步骤S424:求取所有相邻峰值点之间的时间间隔,组成序列[dtb1,dtb2,…,dtbj,…,dtbm-1],dtbj=tbj+1-tbj,j=1,2,3,…,m-1;
步骤S425:从序列中去除大于4秒的时间间隔以及去除小于0.5秒的时间间隔,对剩余的时间间隔求取均值,记为mean_dtb;
步骤S426:计算得到:呼吸率=60/(mean_dtb)次/分。
本发明采用高采样率再进行降采样同步均值去噪的方法,可使信号中的随机噪声、工频噪声等互相抵消,消减短时高频噪音成分,平滑信号波形,且保留有用信号特征。采样率范围结合降采样,可减小小波分解层数,减少小波分解带来的信号失真。本方案采用sym8小波基,并针对心率和呼吸率计算分别选取不同的小波系数重构,可使小波分解后的频带与新生儿心率呼吸率频率范围更好匹配,减少无用信号的影响。
本发明与现有技术相比,具有以下优点及有益效果:
本发明采用时频结合的方法,在时域针对新生儿心率和呼吸率特点,选用了f*a的采样率和去噪方法,有利于抑制噪声,突出有用信号特征;在频域针对性的减小小波分解的失真,并对频率范围进行匹配,减少了无用信号的影响,从而更加有利于从重构信号中提取到心率和呼吸率。
附图说明
图1为本发明中压电薄膜传感器采集的原始身体振动信号;
图2为计算心率时处理后的波形及峰值点;
图3为计算呼吸率时处理后的波形及峰值点。
具体实施方式
下面结合实施例对本发明作进一步地详细说明,但本发明的实施方式不限于此。
实施例1:
一种基于压电薄膜传感信号的心率和呼吸率的计算方法,包括:
(1)利用压电薄膜传感器放置于婴儿床床垫下以采样率4096Hz采集2分钟的身体振动信号S,如图1所示;
(2)将该信号重采样至64Hz的采样率并同时进行去噪,得到信号s′,
(3)使用sym8小波基对信号S′进行6层小波分解;
(4)计算实时心率:
(4.11)提取小波分解后第5层、第6层的细节系数;
(4.12)分别对第5层、第6层的细节系数进行重构;
(4.13)将第5层、第6层细节系数重构后得到的信号相加,并提取相加后信号的所有峰值点的时间点,记为th1、th2、…、thn,如图2所示;
(4.14)求取所有相邻峰值点之间的时间间隔,得到序列dthi=thi+1-thi,i=1,2,3,…,n-1;
(4.15)去除大于1秒或小于0.24秒的时间间隔,对剩余的时间间隔求取均值,记为mean_dth;
(4.16)心率=60/mean_dth次/分;
(5)计算实时呼吸率:
(5.1)提取步骤2小波分解后第5层的近似系数;
(5.2)对第5层的近似系数进行重构;
(5.3)对第5层近似系数重构后得到的信号进行高通滤波,其中通带截止频率为0.25Hz,阻带截止频率为0.2Hz,通带最大衰减3dB,阻带最小衰减20dB;
(5.4)对高通滤波后的信号提取所有峰值点的时间点,记为tb1、tb2、…、tbm;
(5.5)求取所有相邻峰值点之间的时间间隔,得到序列dtbj=tbj+1-tbj,j=1,2,3,…,m-1,如图3所示;
(5.6)去除大于4秒或小于0.5秒的时间间隔,对剩余的时间间隔求取均值,记为mean_dtb;
(5.7)呼吸率=60/mean_dtb次/分。
尽管这里参照本发明的解释性实施例对本发明进行了描述,上述实施例仅为本发明较佳的实施方式,本发明的实施方式并不受上述实施例的限制,应该理解,本领域技术人员可以设计出很多其他的修改和实施方式,这些修改和实施方式将落在本申请公开的原则范围和精神之内。
Claims (1)
1.基于压电薄膜传感信号的心率和呼吸率的计算方法,其特征在于,包括:
步骤S1:以f*aHz的采样率从压电薄膜传感器采集M分钟的原始信号,从中截取至少1分钟的信号S,且截取的信号的长度是f的整数倍,其中60<f<70,f∈N+,a>1且a∈N,M大于1且M∈N+;
步骤S2:将信号S重采样为采样率f Hz的信号并同时进行去噪,得到信号,=,其中t为采样时间点,为信号S第t个采样时间点对应的幅值,t为正整数;为信号的第k个点,k∈N+,且1≤k≤L/a,L为信号S的长度;
步骤S4:计算实时心率和实时呼吸率,具体包括:
步骤S411:获取小波分解后的第5层和第6层的细节系数,并分别进行重构;
步骤S412:将重构后得到的信号相加,并提取相加后信号的所有峰值点的时间点,记作th1、th2、…、thn;
步骤S413:求取所有相邻峰值点之间的时间间隔,组成序列[dth1,dth2,…,dthi,…,dthn-1],dthi=thi+1-thi,i=1,2,3,…,n-1;
步骤S414:从序列中去除大于1秒的时间间隔和小于0.24秒的时间间隔,对剩余的时间间隔求取均值,记为mean_dth;
步骤S415:计算得到:心率=60⁄(mean_dth)次/分;
步骤S421:提取小波分解后的第5层的近似系数,并进行重构;
步骤S422:对重构后得到的信号进行高通滤波,通带截止频率为0.25Hz;
步骤S423:对高通滤波后的信号提取所有峰值点的时间点,记为tb1、tb2、…、tbm;
步骤S424:求取所有相邻峰值点之间的时间间隔,组成序列[dtb1,dtb2,…,dtbj,…,dtbm-1],dtbj=tbj+1-tbj,j=1,2,3,…,m-1;
步骤S425:从序列中去除大于4秒的时间间隔和小于0.5秒的时间间隔,对剩余的时间间隔求取均值,记为mean_dtb;
步骤S426:计算得到:呼吸率=60⁄(mean_dtb)次/分。
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