WO2021184818A1 - 一种基于粒子滤波的人体逐拍心率测量装置及方法 - Google Patents
一种基于粒子滤波的人体逐拍心率测量装置及方法 Download PDFInfo
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- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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Definitions
- the invention relates to a human body beat-by-beat heart rate measurement device and method based on particle filtering, and belongs to the technical field of heart rate measurement.
- Respiration rate (Respiration rate, abbreviated RESP), heart rate (Heart Rate, abbreviated HR) and heart rate variability (Heart Rate Variability, abbreviated HRV) are important physiological parameters of the human body, which can reflect the health of the human body.
- Daily monitoring helps to detect cardiovascular diseases, so as to prevent them early and prevent more dangerous situations from appearing.
- the current method of measuring beat-by-beat heart rate is mainly based on electrocardiogram (ECG) signal and photoplethysmogram (PPG) signal.
- ECG electrocardiogram
- PPG photoplethysmogram
- single-channel signals can only provide limited information related to human physiological parameters.
- the present invention proposes a human body beat-by-beat heart rate measurement device based on particle filtering, which can enhance the accuracy of human body beat-by-beat heart rate monitoring under sports and noise environments, and can further accurately extract the same beat-by-beat heart rate.
- a human body beat-by-beat heart rate measurement device based on particle filtering, which can enhance the accuracy of human body beat-by-beat heart rate monitoring under sports and noise environments, and can further accurately extract the same beat-by-beat heart rate.
- a human body beat-by-beat heart rate measurement device based on particle filtering of the present invention includes a patch module and an electronic module.
- the patch module is in close contact with the surface of the human skin and collects signals.
- the electronic module is located away from the human body. On one side of the skin surface, the electronic module is used to process the signals collected by the patch module.
- the patch module is attached to the human skin with four ECG electrode patches, the four ECG electrode patches are located at the four corners of the patch module, and the center of the patch module is provided with an optical probe.
- the probe includes an LED light source transmitter and a photoelectric receiving sensor, and the optical probe performs data communication with the electronic module through a serial port.
- the electronic module includes a power supply module for power supply, a storage module for storing signals and calculation parameters, a sensor module group, a microprocessor equipped with a particle filter-based heart rate calculation program and a wireless communication module.
- the sensor module group includes an ECG sensor, a three-axis acceleration sensor, a three-axis angular velocity sensor, and a photoplethysmographic signal acquisition sensor.
- the ECG sensor collection interface is exposed and attached to the surface of the human skin through an ECG electrode patch.
- Electrical signal acquisition, a three-axis acceleration sensor and a three-axis angular velocity sensor are set inside the sensor module and record the seismocardiographic signals on the surface of the human thoracic cavity in real time.
- the photoplethysmographic signal acquisition sensor collects the human photoplethysmographic signal through an optical probe.
- the X-axis, Y-axis and Z-axis of the three-axis acceleration sensor and the three-axis angular velocity sensor respectively correspond to the left-right direction, the up-down direction and the front-rear direction of the surface of the thoracic cavity.
- the present invention provides a method for measuring human body beat-by-beat heart rate based on particle filtering, which includes the following steps:
- S1 Collect sensor data in real time, including ECG signals, photoplethysmographic signals and seismocardiography signals;
- S2 Preprocess the signal, first use wavelet decomposition to process the signal, and then use the morphological filtering method to eliminate errors and improve signal quality;
- S4 Model the beat-by-beat heart rate as a state estimation problem, establish an equation for data analysis, use particle filter algorithm for probability fusion, obtain a stable beat-by-beat heart rate estimate, and obtain derived parameters, including respiration rate and heart rate variability related frequency Time domain and heart rate variability related time domain indicators.
- the morphological filtering method includes erosion and expansion operations.
- the calculation formula of the erosion operation is:
- the calculation formula of the expansion operation is:
- B(m) is a structural element designed according to the characteristics of the waveform shape.
- the extracted signal feature points include the R point of the electrocardiogram signal, the AO point of the seismocardiography signal, and the FOOT point of the photoplethysmographic signal.
- step S4 also includes:
- x k is the state value at time k
- z k is the observation vector at time k
- v k-1 and u k correspond to state transition noise and observation noise respectively and are independent of each other
- step S405 From the joint distribution obtained in step S404, update the weight of the i-th particle and normalize the weight value:
- the beat-by-beat heart rate estimation is modeled as a state estimation problem, and the particle filter algorithm is used to probabilistically fuse the observation and state values, which effectively suppresses the artifacts and noise caused by body movement on the beat-by-beat heart rate.
- the influence of the measurement results provides users with a reliable and stable way of measuring the heart rate of the human body.
- FIG. 1 is a side view of a portable and wearable beat-by-beat heart rate measuring device according to the preferred embodiment of the present invention
- FIG. 2 is a top view of a portable and wearable beat-by-beat heart rate measuring device according to the preferred embodiment of the present invention
- Fig. 3 is a bottom view of a portable and wearable beat-by-beat heart rate measuring device according to the preferred embodiment of the present invention.
- FIG. 4 is a schematic diagram of a portable and wearable beat-by-beat heart rate measuring device attached to the surface of the human thoracic cavity according to the preferred embodiment of the present invention
- FIG. 5 is a flowchart of a portable and wearable beat-by-beat heart rate measurement method according to the preferred embodiment of the present invention.
- Fig. 6 is a schematic diagram of extracting signal feature points in a portable and wearable beat-by-beat heart rate measurement method according to a preferred embodiment of the present invention.
- a particle filter-based human body beat-by-beat heart rate measurement device includes a patch module 102 and an electronic module 101.
- One side of the patch module 102 can be close to the surface of the human skin. Contact, the other side is connected to the electronic module 101.
- the entire device can be attached to the surface of the thoracic cavity above the human heart through a magic band.
- the patch module 102 is attached to the human skin on one side with four ECG electrode patches 110.
- the four ECG electrode patches 110 are located at the four corners of the patch module 102.
- An optical probe is provided in the center, and the optical probe includes an LED light source transmitter 108 and a photoelectric receiving sensor 109. The optical probe performs data communication with the electronic module 101 through a serial port.
- the electronic module 101 includes a power supply module 103 for power supply, a storage module 104 for storing signals and calculation parameters, a sensor module group 105, and a microprocessor 106 equipped with a particle filter-based beat-by-beat heart rate calculation program. And wireless communication module 107.
- the power module 103 uses a rechargeable lithium battery with a capacity of 300 mAh, and provides a reliable power supply for other modules of the system through a 3V/2.1V linear voltage regulator circuit.
- the storage module 104 can save the collected original signals and various calculated parameters, such as breathing rate, heart rate, heart rate variability index, etc., in real time.
- the sensor module group 105 includes an ECG sensor, a three-axis acceleration sensor, a three-axis angular velocity sensor, and a photoplethysmographic signal acquisition sensor,
- the ECG sensor collection interface is exposed and attached to the surface of the human skin through the ECG electrode patch 110 for ECG signal collection.
- TI ultra-low power integrated analog front end AFE4900 is used.
- the AFE4900 device supports synchronous collection1 ECG signals and 3 PPG signals.
- the three-axis acceleration sensor and the three-axis angular velocity sensor are arranged inside the sensor module and record the seismocardiography (SCG) signal on the surface of the human thoracic cavity in real time.
- the X-axis, The Y axis and Z axis correspond to the left and right, up and down, and front and back directions of the thoracic cavity surface, respectively.
- InvenSense’s MPU9250 device is used to obtain three-axis acceleration and three-axis angular velocity motion signals.
- the MPU9250 has three axial 16-bits acceleration AD outputs and three axial 16-bits angular velocity AD outputs to ensure precise slowness. Speed and fast motion tracking function.
- the collected three sets of acceleration and three sets of angular velocity values record the weak acceleration and angular velocity changes of the chest wall surface caused by ejection and myocardial contraction, reflecting the diastole and contraction of the left ventricle, the opening and closing of the aortic valve, and other hearts Related mechanical activities.
- the actual test results show that the X, Y, and Z axes can provide useful information for calculating the beat-by-beat heart rate. Usually, the signal amplitude in the Z-axis direction is the strongest and the signal-to-noise ratio is the highest.
- the photoplethysmographic signal acquisition sensor collects the human body's photoplethysmographic signal through an optical probe.
- the AFE4900 analog front end is used to collect the photoplethysmographic signal.
- the optical probe of the sensor is exposed, including the LED light source transmitter 108 and the photoelectric receiving sensor 109.
- the absorption of light is a relatively stable value, and the dilation and contraction of the heart cause changes in the filling degree of human subcutaneous capillaries, and changes in blood flow make the absorption of light in the blood also show periodic changes.
- the sensor can convert this light intensity into an electrical signal output, which can be further processed by the program to obtain the PPG signal.
- the microprocessor 106 uses TI’s CC2640R2F chip, which is equipped with a particle filter-based beat-by-beat heart rate calculation program.
- the particle filter is a Bayesian filter. Compared with the Kalman filter, it can pass Generate multiple particles to approximate randomly distributed noise, which can be applied to nonlinear models with non-Gaussian noise.
- the wireless communication module 107 uses wireless communication technologies such as low-power Bluetooth, WIFI, and ZigBee to send the measurement results to clients with computing and storage functions such as smart phones, tablets, and computers.
- the 2.4GHz radio frequency part in this embodiment has been integrated into the microprocessor 106CC2640R2F device.
- the radio frequency part complies with the Bluetooth Low Energy (BLE) protocol, and can be further developed based on TI's Bluetooth low energy software protocol stack to achieve data transmission.
- BLE Bluetooth Low Energy
- a particle filter-based human body beat-by-beat heart rate measurement method includes the following steps:
- S1 Collect sensor data in real time, including 1 channel of ECG signal, 1 channel of photoplethysmography signal, 3 channel of acceleration signal and 3 channel of angular velocity signal. All signals are collected synchronously, and the sampling frequency is 250 Hz.
- S2 Preprocess the signal, select the sym6 wavelet base to decompose the original signal into signal components with different resolutions at different scales, and obtain a wavelet decomposition tree composed of approximation and detail coefficients of each level, which is selected in this embodiment
- the number of decomposition layers is 6 layers.
- the "Soft Thresholding" function is selected to determine and discard the detail coefficients with small amplitude, so as to achieve the purpose of removing the baseline offset and high-frequency noise components.
- the morphological filtering method is used to perform Erosion and Dilation operations on the signal to further improve the signal quality.
- Morphological filtering is a non-linear filter, which can effectively avoid the problem of non-linear phase, that is, on the basis of maintaining the original signal phase, it can deal with the morphological "convex" and "concave” structure of the signal.
- the morphological filtering algorithm has the advantages of stability, simplicity, and low computational complexity.
- the calculation formula of the corrosion operation is:
- the calculation formula of the expansion operation is:
- B(m) is a structural element (Structural element) designed according to the morphological characteristics of the waveform, which is conducive to obtaining the optimal filtering effect.
- the extracted signal feature points include the R point of the ECG signal, the AO point of the seismocardiography signal, and the FOOT point of the photoplethysmographic signal. Use these The characteristic points can be calculated to obtain the corresponding heart rate observations and form observation vectors.
- S4 Model the beat-by-beat heart rate as a state estimation problem, establish an equation for data analysis, use particle filter algorithm for probability fusion, obtain a stable beat-by-beat heart rate estimate, and obtain derived parameters, including respiration rate and heart rate variability related frequency Time domain and heart rate variability related time domain indicators.
- x k is the state value at time k
- z k is the observation vector at time k
- v k-1 and u k correspond to state transition noise and observation noise respectively and are independent of each other
- step S405 From the joint distribution obtained in step S404, update the weight of the i-th particle and normalize the weight value:
- the human body beat-by-beat heart rate measurement device based on particle filter of the present invention has the advantages of multi-parameter integration, small size, light weight, low power consumption, suitable for long-term monitoring, etc., based on the particle filter algorithm for probabilistic observations and state values Fusion provides users with a reliable and stable way of measuring the heart rate of the human body, which can be used as a feasible method for family or community health monitoring.
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Abstract
一种基于粒子滤波的人体逐拍心率测量装置，包括贴片模块(102)和电子模块(101)，贴片模块(102)与人体皮肤表面紧密接触并采集信号，电子模块(101)位于贴片模块(102)背离人体皮肤表面的一侧，电子模块(101)用于处理贴片模块(102)采集的信号。装置具有多参数集成、体积小巧、重量轻、功耗低、适用于长时监测等优点，基于粒子滤波算法对观测值和状态值进行概率融合，为用户提供了一种可靠、稳定的人体逐拍心率测量方式，可以作为家庭或社区健康监护的一种可行手段。还提出一种基于粒子滤波的人体逐拍心率测量方法。
Description
本发明涉及一种基于粒子滤波的人体逐拍心率测量装置及方法，属于心率测量技术领域。
目前，心血管疾病的死亡率居首位，全球范围内心血管疾病造成的死亡人数占总死亡人数的比例接近30％，在中国，这一比例大约为40％。根据《中国心血管病报告2018》的最新数据，我国心血管病患病率处于持续上升阶段。目前，估计全国有心血管病患者2.9亿。而且我国的人口老龄化现象日趋严重，60岁以上老年人在总人口中所占的比例逐年上升，截至2018年底，我国60周岁及以上人口为24949万人，占比17.9％。由于老年人身体机能下降，这一类人群往往正是心血管类疾病的高发人群。
呼吸率(Respiration rate,缩写RESP)、心率(Heart Rate,缩写HR)和心率变异性(Heart Rate Variability,缩写HRV)是人体重要的生理参数指标，能够侧面反映人体健康状况。日常的监测有助于发现心血管等方面的疾病，从而及早预防，防止更加危险的情况出现。目前测量逐拍心率的方法主要是基于心电(Electrocardiogram,缩写ECG)信号和光电容积脉搏波(Photoplethysmogram,缩写PPG)信号。然而，一方面单通道信号往往只能提供有限的和人体生理参数指标相关信息，一旦原始信号质量较差，这些信息可能完全丢失，给逐拍心率计算造成较高的错误率；另一方面在人体日常监测场景下，难免出现较大幅度的波动，会给可穿戴式监测设备采集的原始信号引入伪迹和噪声，导致信号质量差甚至严重失真。而伪迹和噪声的频谱分布在较广的频率范围内，设计固定频率范围的有限冲激响应(FIR)滤波器和无限冲激响应(IIR)滤波器无法有效地滤除。上述问题增加了漏诊的风险，也可能造成不必要的虚警，使得可穿戴式设备的使用场景受限，不利于大范围地推广和普及。
发明内容
为克服现有技术的不足，本发明提出一种基于粒子滤波的人体逐拍心率测量装置，可增强运动、噪声环境下人体逐拍心率监测的准确性，并可进一步准确地提取同 逐拍心率相关的派生参数。
为实现上述目的，本发明的一种基于粒子滤波的人体逐拍心率测量装置，包括贴片模块和电子模块，贴片模块与人体皮肤表面紧密接触并采集信号，电子模块位于贴片模块背离人体皮肤表面的一侧，电子模块用于处理贴片模块采集的信号。
进一步地，贴片模块贴附在人体皮肤的一面上设有四个心电电极贴片，四个心电电极贴片位于贴片模块的四角，贴片模块的中心处设有光学探头，光学探头包括LED光源发射器和光电接收传感器，光学探头通过串口与电子模块进行数据通信。
进一步地，电子模块包括用于供电的电源模块、用于存储信号和计算参数的存储模块、传感器模块组、搭载基于粒子滤波的逐拍心率计算程序的微处理器和无线通讯模块。
进一步地，传感器模块组包括心电传感器、三轴加速度传感器、三轴角速度传感器和光电容积脉搏波信号采集传感器，心电传感器采集接口外露并通过心电电极贴片贴附在人体皮肤表面进行心电信号采集，三轴加速度传感器和三轴角速度传感器设置在传感器模块内部并实时记载人体胸腔表面的心震描记信号，光电容积脉搏波信号采集传感器通过光学探头采集人体光电容积脉搏波信号。
进一步地，三轴加速度传感器和三轴角速度传感器的X轴、Y轴和Z轴分别对应胸腔表面的左右方向、上下方向和前后方向。
本发明提出一种基于粒子滤波的人体逐拍心率测量方法，包括如下步骤：
S1：实时采集传感器数据，包括心电信号、光电容积脉搏波信号和心震描记信号；
S2：对信号进行预处理，先利用小波分解对信号进行处理，然后使用形态学滤波方法，消除误差，改进信号质量；
S3：基于局部最优原理提取信号特征点，构造观测向量；
S4：将逐拍心率建模为状态估计问题，建立方程进行数据分析，利用粒子滤波算法进行概率融合，得到稳定的逐拍心率估计值，并得到派生参数，包括呼吸率、心率变异性相关频域和心率变异性相关时域指标。
进一步地，在步骤S2中，形态学滤波方法包括腐蚀和膨胀操作，对于一维信号f(n)，腐蚀运算的计算式为：
膨胀运算的计算式为：
其中，B(m)为依据波形形态特征设计的结构元素。
进一步地，在步骤S3中，提取的信号特征点包括心电信号的R点、心震描记信 号的AO点和光电容积脉搏波信号的FOOT点。
进一步地，步骤S4还包括：
S401：将逐拍心率建模为状态估计问题，建立观测方程和转移方程：
x
k＝f
k(x
k-1，v
k-1)
z
k＝h
k(x
k，u
k)
其中，x
k是k时刻的状态值，z
k是k时刻的观测向量，v
k-1和u
k分别对应状态转移噪声和观测噪声且相互独立；
S402：逐拍心率估计的转移方程为：x
k＝f
k(x
k-1，v
k-1)＝x
k-1+v
k-1，定义STD
HR为心率标准差，以多路观测心率的均值作为替代，将v
k-1定义为高斯白噪声v
k-1～N(0，STD
2
HR)；
S403：从中采样得到N个粒子，
若是首次采样，采用以心率均值HR
mean所决定的均匀分布作为第一步中粒子的先验分布：
S404：将第m个观测值建模为服从高斯分布：
其中，σ
k为观测噪声标准差，对于来自全部M个传感器的心率观测
定义其联合分布为：
S405：由步骤S404得到的联合分布，更新第i个粒子的权重并对权重值归一化：
S406：计算得到第k步的逐拍心率：
S407：根据重要性权重对当前的N个粒子进行重采样，即增加权重较大的粒子而减少权重较小的粒子，以解决粒子退化问题，得到N个新的粒子后，将它们的权重重置为
S408：上述步骤将按照所定义的观测方程和转移方程递归进行，直到所有数据全部处理完成。
本发明的一种便携可穿戴式逐拍心率测量装置具有以下优点：
可以同步采集ECG、PPG和SCG等信号，多参数集成，为逐拍心率估计提供了多维度的信息，避免了单一测量手段鲁棒性差的问题；
体积小巧、重量轻、功耗低，不影响用户日常活动，适用于长时监测，可以作为家庭或社区健康监护的一种可行手段；
在采集得到的原始信号基础上，将逐拍心率估计建模为状态估计问题，利用粒子滤波算法对观测值和状态值进行概率融合，有效抑制了体动造成的伪迹和噪声对逐拍心率测量结果的影响，为用户提供了一种可靠、稳定的人体逐拍心率测量方式。
下面结合附图对本发明作进一步描写和阐述。
图1是本发明首选实施方式的一种便携可穿戴式逐拍心率测量装置的侧视图；
图2是本发明首选实施方式的一种便携可穿戴式逐拍心率测量装置的顶视图；
图3是本发明首选实施方式的一种便携可穿戴式逐拍心率测量装置的底视图；
图4是本发明首选实施方式的一种便携可穿戴式逐拍心率测量装置贴附于人体胸腔表面位置的示意图；
图5是本发明首选实施方式的一种便携可穿戴式逐拍心率测量方法的流程图；
图6是本发明首选实施方式的一种便携可穿戴式逐拍心率测量方法中提取信号特征点的点位的示意图。
附图标记：101、电子模块；102、贴片模块；103、电源模块；104、存储模块；105、传感器模块组；106、微处理器；107、无线通讯模块；108、LED光源发射器；109、光电接收传感器；110、心电电极贴片。
下面将结合附图、通过对本发明的优选实施方式的描述，更加清楚、完整地阐述本发明的技术方案。
如图1和图4所示，本发明首选实施方式的一种基于粒子滤波的人体逐拍心率测量装置，包括贴片模块102和电子模块101，贴片模块102的一面能够与人体皮肤表面紧密接触，另一面与电子模块101连接。可通过魔术带将本装置整体附着在人体心脏上方的胸腔表面位置处。
如图3所示，贴片模块102贴附在人体皮肤的一面上设有四个心电电极贴片110，四个心电电极贴片110位于贴片模块102的四角，贴片模块102的中心处设有光学探头，光学探头包括LED光源发射器108和光电接收传感器109，光学探头通过串口与电子模块101进行数据通信。
如图2所示，电子模块101包括用于供电的电源模块103、用于存储信号和计算参数的存储模块104、传感器模块组105、搭载基于粒子滤波的逐拍心率计算程序的微处理器106和无线通讯模块107。
电源模块103采用可重复充电的300mAh容量大小的锂电池，经过3V/2.1V线性稳压电路为系统其他各个模块提供可靠的电源。
存储模块104可对采集的原始信号以及计算得到的各项参数，例如呼吸率、心率、心率变异性指标等进行实时保存。
传感器模块组105包括心电传感器、三轴加速度传感器、三轴角速度传感器和光电容积脉搏波信号采集传感器，
心电传感器采集接口外露并通过心电电极贴片110贴附在人体皮肤表面进行心电信号采集，本实施例中采用TI公司的超低功耗集成型模拟前端AFE4900，AFE4900器件支持同步采集1路ECG信号和3路PPG信号。
如图4所示，三轴加速度传感器和三轴角速度传感器设置在传感器模块内部并实时记载人体胸腔表面的心震描记信号(Seismocardiography,SCG)，三轴加速度传感器和三轴角速度传感器的X轴、Y轴和Z轴分别对应胸腔表面的左右方向、上下方向和前后方向。本实施例中采用InvenSense公司的MPU9250器件获取三轴加速度、三轴角速度运动信号，MPU9250具有三个轴向16-bits加速度AD输出，三个轴向16-bits角速度AD输出，保证实现精密的慢速和快速运动跟踪功能。采集得到的三组加速度和三组角速度值，记录了由于射血和心肌收缩引起的胸壁表面的微弱加速度和角速度变化，反映了左心室的舒张和收缩，主动脉瓣的开放和闭合以及其他心脏相关的机械活动。实际测试结果表明，X，Y和Z轴均可为计算逐拍心率提供有用信息，通常Z轴方 向的信号幅度最强，信噪比最高。
光电容积脉搏波信号采集传感器通过光学探头采集人体光电容积脉搏波信号。本实施例中采用AFE4900模拟前端采集光电容积脉搏波信号，该传感器的光学探头外露，包含LED光源发射器108和光电接收传感器109，当光源发出的光照射到人体组织的时候，肌肉、皮肤等对光的吸收度是一个相对稳定的值，而由于心脏的舒张与收缩，引起人体皮下毛细血管充盈程度的变化，血流量的变化使得光在血液中的吸收量也呈现周期性的变化，光电传感器可以将这种光强度转换成电信号输出，由程序进一步处理后即可获得PPG信号。
本实施例中微处理器106采用TI公司的CC2640R2F芯片，搭载了基于粒子滤波的逐拍心率计算程序，粒子滤波器是一种贝叶斯滤波器，与卡尔曼滤波器相比，它可以通过生成多个粒子来近似任意分布的噪声，从而应用于具有非高斯噪声的非线性模型。
无线通讯模块107利用低功耗蓝牙、WIFI、ZigBee等无线通信技术将测量结果发送到智能手机、平板、电脑等具备计算和存储功能的客户端。本实施例中的2.4GHz射频部分已经集成到微处理器106CC2640R2F器件内部。该射频部分符合Bluetooth低功耗(BLE)协议，可基于TI公司的低功耗蓝牙软件协议栈做进一步开发，从而实现数据发送。
如图5所示，本发明首选实施方式的一种基于粒子滤波的人体逐拍心率测量方法，包括如下步骤：
S1：实时采集传感器数据，包括1通道心电信号、1通道光电容积脉搏波信号、3通道加速度信号和3通道角速度信号，所有信号均同步采集，采样频率为250Hz。
S2：对信号进行预处理，选择sym6小波基将原始信号分解成不同尺度下具有不同分辨率的信号分量，获得由每个级别的近似和细节系数组成的小波分解树，本实施例中选择的分解层数为6层。为了抑制噪声部分，选择“软阈值”(Soft Thresholding)函数来决定和丢弃具有小幅度的细节系数，达到移除基线偏移和高频噪声成分的目的。
之后利用形态学滤波方法，对信号进行腐蚀(Erosion)和膨胀(Dilation)操作，进一步改进信号质量。形态学滤波是一种非线性滤波器，可以有效地避免非线性相位问题，即在保持原始信号相位的基础上，它可以处理信号在形态上的“凸起”和“凹陷”结构。形态滤波算法具有稳定、简单、计算复杂度低的优点对于一维信号f(n)，腐蚀运算的计算式为：
膨胀运算的计算式为：
其中，B(m)为依据波形形态特征设计的结构元素(Structural element)，有利于获取最优滤波效果。
S3：基于局部最优原理提取信号特征点，如图6所示，提取的信号特征点包括心电信号的R点、心震描记信号的AO点和光电容积脉搏波信号的FOOT点，利用这些特征点可以计算得到对应的心率观测值，并组成观测向量。
S4：将逐拍心率建模为状态估计问题，建立方程进行数据分析，利用粒子滤波算法进行概率融合，得到稳定的逐拍心率估计值，并得到派生参数，包括呼吸率、心率变异性相关频域和心率变异性相关时域指标。
具体包括：
S401：将逐拍心率建模为状态估计问题，建立观测方程和转移方程：
x
k＝f
k(x
k-1，v
k-1)
z
k＝h
k(x
k，u
k)
其中，x
k是k时刻的状态值，z
k是k时刻的观测向量，v
k-1和u
k分别对应状态转移噪声和观测噪声且相互独立；
S402：逐拍心率估计的转移方程为：x
k＝f
k(x
k-1，v
k-1)＝x
k-1+v
k-1，定义STD
HR为心率标准差，以多路观测心率的均值作为替代，将v
k-1定义为高斯白噪声v
k-1～N(0，STD
2
HR)；
S403：从中采样得到N个粒子，本实施例中取N＝500，
由于该过程的第一步(k＝0)无法得到粒子状态的先验分布，为了使粒子滤波算法可以更快地收敛，若是首次采样，采用以心率均值HR
mean所决定的均匀分布作为第一步中粒子的先验分布：
S404：将第m个观测值建模为服从高斯分布：
其中，σ
k为观测噪声标准差，对于来自全部M个传感器的心率观测
定义其联合分布为：
S405：由步骤S404得到的联合分布，更新第i个粒子的权重并对权重值归一化：
S406：计算得到第k步的逐拍心率：
S407：根据重要性权重对当前的N个粒子进行重采样，即增加权重较大的粒子而减少权重较小的粒子，以解决粒子退化问题，得到N个新的粒子后，将它们的权重重置为
S408：上述步骤将按照所定义的观测方程和转移方程递归进行，直到所有数据全部处理完成。
本发明的一种基于粒子滤波的人体逐拍心率测量装置具有多参数集成、体积小巧、重量轻、功耗低、适用于长时监测等优点，基于粒子滤波算法对观测值和状态值进行概率融合，为用户提供了一种可靠、稳定的人体逐拍心率测量方式，可以作为家庭或社区健康监护的一种可行手段。
上述具体实施方式仅仅对本发明的优选实施方式进行描述，而并非对本发明的保护范围进行限定。在不脱离本发明设计构思和精神范畴的前提下，本领域的普通技术人员根据本发明所提供的文字描述、附图对本发明的技术方案所作出的各种变形、替代和改进，均应属于本发明的保护范畴。本发明的保护范围由权利要求确定。
Claims (9)
- 一种基于粒子滤波的人体逐拍心率测量装置，其特征在于，包括贴片模块和电子模块，所述贴片模块与人体皮肤表面紧密接触并采集信号，所述电子模块位于贴片模块背离人体皮肤表面的一侧，所述电子模块用于处理贴片模块采集的信号。
- 如权利要求1所述的一种基于粒子滤波的人体逐拍心率测量装置，其特征在于，所述贴片模块贴附在人体皮肤的一面上设有四个心电电极贴片，四个所述心电电极贴片位于贴片模块的四角，所述贴片模块的中心处设有光学探头，所述光学探头包括LED光源发射器和光电接收传感器，所述光学探头通过串口与电子模块进行数据通信。
- 如权利要求2所述的一种基于粒子滤波的人体逐拍心率测量装置，其特征在于，所述电子模块包括用于供电的电源模块、用于存储信号和计算参数的存储模块、传感器模块组、搭载基于粒子滤波的逐拍心率计算程序的微处理器和无线通讯模块。
- 如权利要求3所述的一种基于粒子滤波的人体逐拍心率测量装置，其特征在于，所述传感器模块组包括心电传感器、三轴加速度传感器、三轴角速度传感器和光电容积脉搏波信号采集传感器，所述心电传感器采集接口外露并通过心电电极贴片贴附在人体皮肤表面进行心电信号采集，所述三轴加速度传感器和三轴角速度传感器设置在传感器模块内部并实时记载人体胸腔表面的心震描记信号，所述光电容积脉搏波信号采集传感器通过光学探头采集人体光电容积脉搏波信号。
- 如权利要求4所述的一种基于粒子滤波的人体逐拍心率测量装置，其特征在于，所述三轴加速度传感器和三轴角速度传感器的X轴、Y轴和Z轴分别对应胸腔表面的左右方向、上下方向和前后方向。
- 一种基于粒子滤波的人体逐拍心率测量方法，其特征在于，包括如下步骤：S1：实时采集传感器数据，包括心电信号、光电容积脉搏波信号和心震描记信号；S2：对信号进行预处理，先利用小波分解对信号进行处理，然后使用形态学滤波方法，消除误差，改进信号质量；S3：基于局部最优原理提取信号特征点，构造观测向量；S4：将逐拍心率建模为状态估计问题，建立方程进行数据分析，利用粒子滤波算法进行概率融合，得到稳定的逐拍心率估计值，并得到派生参数，包括呼吸率、心率变异性相关频域和心率变异性相关时域指标。
- 如权利要求6所述的一种基于粒子滤波的人体逐拍心率测量方法，其特征在于，在步骤S2中，所述形态学滤波方法包括腐蚀和膨胀操作，对于一维信号f(n)，所述腐蚀运算的计算式为：
所述膨胀运算的计算式为：
其中，B(m)为依据波形形态特征设计的结构元素。 - 如权利要求6所述的一种基于粒子滤波的人体逐拍心率测量方法，其特征在于，在步骤S3中，提取的所述信号特征点包括心电信号的R点、心震描记信号的AO点和光电容积脉搏波信号的FOOT点。
- 如权利要求6所述一种基于粒子滤波的人体逐拍心率测量方法，其特征在于，步骤S4还包括：S401：将逐拍心率建模为状态估计问题，建立观测方程和转移方程：x k＝f k(x k-1，v k-1)z k＝h k(x k，u k)其中，x k是k时刻的状态值，z k是k时刻的观测向量，v k-1和u k分别对应状态转移噪声和观测噪声且相互独立；S402：逐拍心率估计的转移方程为：x k＝f k(x k-1，v k-1)＝x k-1+v k-1，定义STD HR为心率标准差，以多路观测心率的均值作为替代，将v k-1定义为高斯白噪声v k-1～N(0，STD 2 HR)；S403：从中采样得到N个粒子，若是首次采样，采用以心率均值HR mean所决定的均匀分布作为第一步中粒子的先验分布：
S404：将第m个观测值建模为服从高斯分布：其中，σ k为观测 噪声标准差，对于来自全部M个传感器的心率观测
定义其联合分布为：
S405：由步骤S404得到的联合分布，更新第i个粒子的权重并对权重值归一化：
S406：计算得到第k步的逐拍心率：
S407：根据重要性权重对当前的N个粒子进行重采样，即增加权重较大的粒子而减少权重较小的粒子，以解决粒子退化问题，得到N个新的粒子后，将它们的权重重置为
S408：上述步骤将按照所定义的观测方程和转移方程递归进行，直到所有数据全部处理完成。
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