Pulse wave signal preprocessing method and forehead physiological signal monitoring system

Abstract

The application discloses a pulse wave signal preprocessing method and a forehead physiological signal monitoring system. The method comprises the following steps: obtaining an original pulse wave signal, eliminating noise of the original pulse wave signal, and performing time-frequency conversion on the original pulse wave signal by using a short-time Fourier transform to obtain a first time-frequency graph; performing time-frequency rearrangement on the first time-frequency graph according to a frequency axis to obtain a second time-frequency graph; constructing a cost function based on time-frequency energy and continuity constraints of the second time-frequency graph, extracting a time-frequency ridge by minimizing the cost function; and screening components for reconstruction in the pulse wave signal based on the time-frequency ridge, and then performing signal reconstruction. The method converts the signal to a time-frequency domain with sparse distribution characteristics for processing, and adopts a simple rule to complete screening and reconstruction of signal components, thereby improving the signal quality while reducing the calculation amount. Integrating the method into the forehead physiological signal monitoring system helps to improve the signal anti-interference capability and output the physiological index calculation results in real time.

Description

Technical Field

[0001] The present invention belongs to the technical field of health monitoring systems, and in particular relates to a pulse wave signal preprocessing method and a forehead physiological signal monitoring system. Background Art

[0002] With the popularity of wearable medical devices, non-invasive physiological monitoring technology based on photoplethysmography (PPG) (such as heart rate, blood oxygen, and blood pressure detection) has become an important tool for health management. However, the original pulse wave signal is easily affected by multiple factors such as motion artifacts, environmental noise, and individual differences. In addition, ordinary users lack professional medical experience and the daily application scenarios are complex and changeable. The PPG signals collected by wearable devices are often mixed with a lot of noise and interference, which in turn undermines the reliability of subsequent analysis. Therefore, in recent years, research related to PPG signal preprocessing has gradually become a hot topic. The mainstream technical routes include adaptive filtering, modal decomposition methods, sparse decomposition methods, deep learning methods, etc. In the process of realizing the present invention, the inventors found that there are at least the following problems in the existing technology:

[0003] The implementation effect of methods such as adaptive filtering depends on the correlation between the reference signal and noise. In current related research, the reference signal mainly comes from auxiliary sensors such as accelerometers. On the one hand, it increases the system hardware cost, and on the other hand, it cannot fully characterize the complex characteristics of interference components such as motion artifacts; the computational complexity of methods such as sparse decomposition and deep learning is relatively high and cannot meet the real-time application requirements of products; the defects of modal decomposition methods are that, on the one hand, methods represented by EMD (Empirical Mode Decomposition) and its improved versions lack a theoretical basis, and on the other hand, due to the influence of factors such as modal aliasing and false components, the actual preprocessing effect is usually not ideal. Summary of the Invention

[0004] Based on the problems existing in the prior art, the purpose of the embodiments of the present application is to provide a pulse wave signal preprocessing method and a forehead physiological signal monitoring system.

[0005] According to a first aspect of an embodiment of the present application, a pulse wave signal preprocessing method is provided, comprising:

[0006] Time-frequency transformation: obtaining an original pulse wave signal, eliminating noise from the original pulse wave signal, and performing time-frequency transformation using short-time Fourier transform to obtain a first time-frequency graph;

[0007] Time-frequency post-processing: performing time-frequency rearrangement on the first time-frequency graph according to the frequency axis to obtain a second time-frequency graph;

[0008] Time-frequency ridge extraction: constructing a cost function based on the time-frequency energy and continuity constraint of the second time-frequency graph, and extracting the time-frequency ridge by minimizing the cost function;

[0009] Signal reconstruction: Based on the time-frequency ridge, the components used for reconstruction in the pulse wave signal are screened, and then signal reconstruction is performed.

[0010] Furthermore, in the time-frequency transformation step, a short-time Fourier transform represented by the following formula is performed:

[0011]

[0012] Where X(t,ω) is the first time-frequency graph obtained after time-frequency transformation, s(t) is the input original pulse wave signal, w(t) is the window function, j is the imaginary unit, t is the time variable, ω is the frequency variable, and the window function uses the Gaussian window shown in the following formula:

[0013]

[0014] where σ is the standard deviation parameter.

[0015] Furthermore, in the time-frequency post-processing step, the following formula is used to estimate the signal at a given time t and frequency ω ′ The instantaneous frequency is:

[0016]

[0017] The energy of the short-time Fourier transform is redistributed to the estimated instantaneous frequency position according to the following formula to make the energy more concentrated:

[0018]

[0019] Where T(t,ω) is the second time-frequency graph obtained after post-processing, δ is the Dirac function, and θ is the frequency variable in the first time-frequency graph before post-processing.

[0020] Furthermore, in the time-frequency ridge extraction step, the cost function C(t,ω) shown in the following formula is defined, which is composed of the time-frequency energy and continuity constraint of the signal:

[0021]

[0022] where λ is the equilibrium parameter;

[0023] Using the dynamic programming algorithm, starting from time t = 0, by minimizing the global cost, we gradually search for the optimal path shown in the following formula along the time-frequency plane, that is, the time-frequency ridge line ω r (t):

[0024]

[0025] where γ(ω,ω ′ ) represents the smoothness penalty term.

[0026] Furthermore, the signal reconstruction step includes the following sub-steps:

[0027] Determine the fundamental wave candidate component set: Based on the reasonable frequency range corresponding to the heart rate, set a screening threshold, and consider the components whose time-frequency points fall within the reasonable frequency range and whose proportion exceeds the screening threshold as fundamental wave candidate components and include them in the fundamental wave candidate component set;

[0028] For each fundamental candidate component C a (t,ω), calculate it and any other component C b The similarity between (t,ω);

[0029] For each fundamental candidate component C a (t,ω), select the first two components with the highest similarity as its harmonic candidate components C ah(i) (t,ω), i=1,2, calculate each fundamental candidate component C based on the similarity between the fundamental candidate component and its harmonic candidate classification a Quality index of (t,ω);

[0030] The component with the highest quality index in the fundamental candidate component set is selected as the fundamental wave of the valid signal. The harmonic candidate component corresponding to this component is the harmonic of the valid signal. Then, the fundamental wave and the corresponding harmonic are reconstructed according to the following formula:

[0031]

[0032] Where real(·) means taking the real part of the signal, is the reconstructed signal, ω r (i, t) is the time-frequency ridge of the i-th component.

[0033] Furthermore, for each fundamental candidate component C a (t,ω), calculate it and any other component C according to the following rules b The similarity between (t,ω):

[0034] Definition ω a (t),ω b (t) are the fundamental candidate components C a (t,ω),C b The instantaneous frequency sequence corresponding to (t,ω) uses the coefficient of variation to measure the difference in the instantaneous frequency characteristics between the two components. The calculation formula is as follows:

[0035]

[0036] CVfreq (a, b) is the instantaneous frequency variation coefficient, ω a / b (t) = ω a (t) / ω b (t), Yes a / b (t), N is the length of the instantaneous frequency sequence;

[0037] Definition A a (t), a b (t) are the fundamental candidate components C a (t,ω),C b The instantaneous spectrum amplitude sequence corresponding to (t,ω) uses the coefficient of variation index to measure the difference in the instantaneous spectrum amplitude characteristics between the two components. The calculation formula is as follows:

[0038]

[0039] CV amp (a, b) is the coefficient of variation of the instantaneous spectrum amplitude, A a / b (t) = A a (t) / A b (t), It's A a / b The mean of (t), N is the data length;

[0040] Based on the instantaneous frequency variation coefficient and the instantaneous spectrum amplitude variation coefficient, the fundamental candidate component C is calculated using the Euclidean distance method. a (t,ω),C b The similarity R(a,b) between (t,ω) is as follows:

[0041]

[0042] According to a second aspect of an embodiment of the present application, there is provided a forehead physiological signal monitoring system, comprising a physiological signal acquisition device, a transmission network, and a host computer;

[0043] The physiological signal acquisition device is used to acquire physiological signals, and the physiological signals include forehead temperature and pulse wave;

[0044] The transmission network is used to forward the physiological signal to a host computer;

[0045] The host computer preprocesses the physiological signal based on the method according to any one of claims 1 to 6, and calculates relevant health indicators based on the preprocessed physiological signal and dynamically outputs and displays them.

[0046] Furthermore, in the system:

[0047] The physiological signal acquisition device is built into the headband and includes a pulse wave acquisition unit, a forehead temperature acquisition unit, and a microcontroller. The pulse wave acquisition unit includes an LED light source and a photoelectric sensor, and acquires the original pulse wave signal through a reflective PPG method, which is filtered, amplified, and transmitted to the microcontroller after analog-to-digital conversion. The forehead temperature acquisition unit uses a non-contact infrared measurement method to acquire the forehead temperature signal, which is filtered, amplified, and transmitted to the microcontroller after analog-to-digital conversion. The microcontroller is used to organize and encapsulate the received collected data into data packets and forward them to the transmission network.

[0048] The transmission network includes a host module and a slave module, the slave module is connected to the physiological signal acquisition device, and the host module is connected to the host computer, so as to forward the physiological signals from the signal acquisition device to the host computer;

[0049] The host computer receives the physiological signal data from the host module, unpacks and parses it, puts the parsed data into a data stream queue, performs signal preprocessing on it to suppress interference and noise, calculates heart rate and blood oxygen saturation indicators, and outputs the analysis results and processed signal waveforms to the UI interface to realize dynamic display of data;

[0050] The heart rate is calculated using the maximum peak value of the amplitude spectrum:

[0051] Calculate the pulse wave signal amplitude spectrum as shown below:

[0052]

[0053] in is a pulse wave signal preprocessed by the method of any one of claims 1 to 6, S(ω) is the corresponding spectrum, real(·) represents the real part, and imag(·) represents the imaginary part;

[0054] S12: Find the maximum peak of the amplitude spectrum and its index frequency f within the frequency range corresponding to the reasonable heart rate range peak , then heart rate HR=f peak ×60;

[0055] The numerical calculation method of blood oxygen saturation is derived from the Lambert-Beer law, and the calculation formula is as follows:

[0056]

[0057] SpO2=aR 2 +bR+c

[0058] Where R is the perfusion index, SpO2 is the blood oxygen saturation, λ1 corresponds to the red light wavelength, λ2 corresponds to the infrared light wavelength, and I AC Represents the AC component in the signal, IDC It represents the DC component in the signal. The coefficients a, b, and c are determined by experimental calibration.

[0059] Furthermore, in the physiological signal acquisition device, the threshold value for switching from the normal acquisition state to the stop acquisition state is set to V normal_limit The threshold for switching from the acquisition state to the normal acquisition state is V stop_limit In the normal acquisition state, all LED light sources work normally. When the device is far away from the target, the received signal strength decreases to V normal_limit Next, turn off the red LED and reduce the infrared LED intensity to a low level to reduce power consumption. The physiological signal acquisition device switches from the normal acquisition state to the stop acquisition state. When the device approaches the target to be detected, the received signal strength increases to V stop_limit After the above, all LEDs resume normal operation, and the physiological signal acquisition device switches from the stop acquisition state to the normal acquisition state.

[0060] Furthermore, in the transmission network, the host module automatically identifies and connects to the slave module:

[0061] The host module's working states are divided into connected state and idle state. When it is in the idle state, it periodically scans for connectable devices. When a matching slave is identified, it initiates and establishes a connection. The identification basis is the custom name of the target slave.

[0062] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0063] The pulse wave preprocessing method described in this application is based on a hemodynamic analysis of the pulse wave, modeling it as a harmonic model. Based on this, a signal preprocessing method based on time-frequency reconstruction is proposed. The main idea is to first analyze and obtain a time-frequency plot with high time-frequency aggregation, then extract time-frequency ridges to achieve signal decomposition in the time-frequency domain. Finally, based on harmonic characteristics, custom rules are defined to filter out appropriate signal components and complete signal reconstruction based on these components. Overall, this method does not require auxiliary sensors, and based on a thorough theoretical analysis of the signal, it converts the signal into a sparsely distributed time-frequency domain for processing. It then uses simple rules to filter and reconstruct signal components, improving signal quality while reducing computational complexity.

[0064] This application also proposes a forehead physiological signal monitoring system based on the above method, which integrates the above pulse wave signal preprocessing method and basic health indicator numerical calculation method in the host computer, thereby further improving the signal anti-interference ability at the software level and outputting the physiological indicator calculation results in real time, making it convenient for users to quickly understand the basic physical condition.

[0065] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0067] Figure 1 The figure is a flowchart of a pulse wave signal preprocessing method according to an exemplary embodiment.

[0068] Figure 2 3 is a comparison diagram of the effects before and after time-frequency post-processing according to an exemplary embodiment.

[0069] Figure 3 is a diagram showing instantaneous frequency estimation results according to an exemplary embodiment.

[0070] Figure 4 3 is a diagram comparing the effects of signal pre-processing before and after according to an exemplary embodiment.

[0071] Figure 5 The figure is a block diagram of a pulse wave signal preprocessing device according to an exemplary embodiment.

[0072] Figure 6 The figure is a schematic diagram of an electronic device according to an exemplary embodiment.

[0073] Figure 7 The figure is an overall hardware block diagram of a forehead physiological signal monitoring system according to an exemplary embodiment.

[0074] Figure 8 The figure is an overall software block diagram of a forehead physiological signal monitoring system according to an exemplary embodiment.

[0075] Figure 9 is a flowchart of physiological indicator calculation according to an exemplary embodiment. DETAILED DESCRIPTION

[0076] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0077] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0078] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0079] Figure 1 FIG. 1 is a flow chart showing a pulse wave signal preprocessing method according to an exemplary embodiment. Figure 1 As shown, the method may include the following steps:

[0080] (1) Time-frequency transformation: obtaining an original pulse wave signal, eliminating noise from the original pulse wave signal, and performing time-frequency transformation using short-time Fourier transform to obtain a first time-frequency graph;

[0081] For the original pulse wave signal to be processed, the effective frequency band of the pulse wave signal is first analyzed to be within 15 Hz, so a low-pass filter is performed to eliminate out-of-band noise, and then the STFT represented by the following formula is used to realize time-frequency transformation:

[0082]

[0083] Where X(t,ω) is the first time-frequency graph obtained after time-frequency transformation, s(t) is the input original pulse wave signal, w(t) is the window function, j is the imaginary unit, t is the time variable, and ω is the frequency variable. Here, the Gaussian window with the best time-frequency aggregation is used as shown in the following formula:

[0084]

[0085] In this formula, σ is the standard deviation parameter, which controls the width of the Gaussian function. It determines the extent of the window function w(t). The larger the value, the wider the window.

[0086] (2) Time-frequency post-processing: performing time-frequency rearrangement on the first time-frequency graph according to the frequency axis to obtain a second time-frequency graph;

[0087] First, for any time t and frequency ω in the time-frequency domain, the true instantaneous frequency ω is re-estimated by the phase derivative ′ :

[0088]

[0089] Then, the energy of the time-frequency graph obtained in (1) is redistributed according to the following formula to further improve the time-frequency aggregation:

[0090]

[0091] Where T(t,ω) is the second time-frequency graph obtained after post-processing, δ is the Dirac function, and θ is the frequency variable in the first time-frequency graph before post-processing.

[0092] Comparison of effects before and after post-processing Figure 2 As shown in the figure, (a) is the first time-frequency diagram, and (b) is the second time-frequency diagram. The details are locally magnified in the figure. It can be seen that the time-frequency aggregation is improved, and the energy of the time-frequency surface is concentrated on several ridges. Each ridge corresponds to a signal component, and the distance between some adjacent ridges is close, which has significant harmonic characteristics.

[0093] (3) Time-frequency ridge extraction: constructing a cost function based on the time-frequency energy and continuity constraint of the second time-frequency graph, and extracting the time-frequency ridge by minimizing the cost function;

[0094] First, define the cost function C(t,ω) shown in the following formula, which is composed of the time-frequency energy and continuity constraints of the signal:

[0095]

[0096] Where λ is a balance parameter that controls the importance of ridge smoothness.

[0097] Then, a dynamic programming algorithm is used to start from time t = 0 and gradually find the optimal path shown in the following formula along the time-frequency plane by minimizing the global cost, that is, the time-frequency ridge line ω r (t):

[0098]

[0099] where γ(ω,ω ′ ) represents the smoothness penalty term, which encourages the frequency changes of adjacent time steps to be small. The time-frequency ridges finally extracted are as follows Figure 3 shown.

[0100] (4) Signal reconstruction: Based on the time-frequency ridge, the components in the pulse wave signal for reconstruction are screened, and then the signal is reconstructed;

[0101] First, the pulse wave is modeled as a harmonic model, and the modeling basis is as follows:

[0102] According to hemodynamic analysis, each time the human heart contracts, pressure within the arteries fluctuates periodically. This fluctuation causes arterial oscillations, commonly referred to as the arterial pulse. The pulse wave propagates along the arterial walls and can propagate to peripheral blood vessels. This spatially propagating pressure fluctuation is the arterial pulse wave. The arterial pulse wave consists of two main components: a forward wave generated by left ventricular contraction and a reflected wave reflected from the end of the blood vessels. Therefore, the pulse wave can be simplified as a fundamental wave and its corresponding multiple harmonic components.

[0103] The following are the specific implementation steps:

[0104] S1: Determine the fundamental wave candidate component set: The reasonable frequency range corresponding to the heart rate is 0.5-3.3 Hz. The screening threshold is set to 60%. The components whose time-frequency points fall within this range and whose proportion exceeds the screening threshold are considered as fundamental wave candidate components and included in the fundamental wave candidate component set;

[0105] S2: For each fundamental candidate component C a (t,ω), calculate it and any other component C according to the following rules b The similarity between (t,ω):

[0106] S2.1: Define ω a (t),ω b (t) are the fundamental candidate components C a (t,ω),C b The instantaneous frequency sequence corresponding to (t,ω) uses the coefficient of variation to measure the difference in the instantaneous frequency characteristics between the two components. The calculation formula is as follows:

[0107]

[0108] CV freq (a, b) is the instantaneous frequency variation coefficient, ω a / b (t) = ω a (t) / ω b (t), Yes a / b (t), N is the length of the instantaneous frequency sequence.

[0109] S2.2: Definition A a (t), A b (t) are the fundamental candidate components C a (t,ω),C b The instantaneous spectrum amplitude sequence corresponding to (t,ω) uses the coefficient of variation index to measure the difference in the instantaneous spectrum amplitude characteristics between the two components. The calculation formula is as follows:

[0110]

[0111] CV amp (a, b) is the coefficient of variation of the instantaneous spectrum amplitude, A a / b (t) = A a (t) / A b (t), It's A a / b (t), N is the data length.

[0112] S2.3: Based on the instantaneous frequency variation coefficient and the instantaneous spectrum amplitude variation coefficient, calculate the fundamental candidate component C using the Euclidean distance method a (t,ω),C b The similarity R(a,b) between (t,ω) is as follows:

[0113]

[0114] S3: For each fundamental candidate component C a (t,ω), select the first two components with the highest similarity as its harmonic candidate components C ah(i) (t,ω), i=1,2, calculate each fundamental candidate component C based on the similarity between the fundamental candidate component and its harmonic candidate classification a Quality index of (t,ω):

[0115]

[0116] S4: Select the component with the highest quality index from the fundamental candidate component set as the fundamental wave of the valid signal. The harmonic candidate components corresponding to this component are the harmonics of the valid signal. Then, the fundamental wave and the corresponding harmonics are reconstructed according to the following formula:

[0117]

[0118] Where real(·) means taking the real part of the signal, is the reconstructed signal, ω r (i, t) is the time-frequency ridge of the i-th component, that is, the signal is reconstructed based on the video ridge of the three components of the fundamental wave and the corresponding harmonics.

[0119] At this point, the signal reconstruction process is completed. The effect before and after reconstruction is compared as follows: Figure 4 As shown in the figure, the amplitude envelope of the reconstructed signal is more balanced and the periodic characteristics of the signal are more prominent, which is convenient for the subsequent application of related parameters such as heart rate detection.

[0120] As can be seen from the above examples, this application models the pulse wave as a harmonic model based on the hemodynamic analysis of the pulse wave. Based on this, a signal preprocessing method based on time-frequency reconstruction is proposed. The main idea is to first analyze and obtain a time-frequency diagram with high time-frequency aggregation, then extract time-frequency ridges to achieve signal decomposition in the time-frequency domain. Finally, based on the harmonic characteristics, custom rules are defined to screen appropriate signal components and complete signal reconstruction based on these components. Overall, this method does not require auxiliary sensors, and based on a thorough theoretical analysis of the signal, it converts the signal into a sparsely distributed time-frequency domain for processing. It also uses simple rules to complete the screening and reconstruction of signal components, thereby improving signal quality while reducing the amount of computation.

[0121] Corresponding to the aforementioned embodiment of the pulse wave signal preprocessing method, the present application also provides an embodiment of a pulse wave signal preprocessing device.

[0122] Figure 5 FIG. 1 is a block diagram of a pulse wave signal preprocessing device according to an exemplary embodiment. Figure 5 , the apparatus may include:

[0123] A time-frequency conversion module 21 is configured to obtain an original pulse wave signal, eliminate noise from the original pulse wave signal, and perform time-frequency conversion using a short-time Fourier transform to obtain a first time-frequency graph;

[0124] a time-frequency post-processing module 22, configured to perform time-frequency rearrangement on the first time-frequency graph according to the frequency axis to obtain a second time-frequency graph;

[0125] a time-frequency ridge extraction module 23, configured to construct a cost function based on the time-frequency energy and continuity constraint of the second time-frequency graph, and extract the time-frequency ridge by minimizing the cost function;

[0126] The signal reconstruction module 24 is used to screen the components used for reconstruction in the pulse wave signal based on the time-frequency ridge, and then perform signal reconstruction.

[0127] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0128] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0129] Accordingly, the present application also provides a computer program product, including a computer program / instruction, which implements the above-mentioned pulse wave signal preprocessing method when executed by a processor.

[0130] Accordingly, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the pulse wave signal preprocessing method as described above. Figure 6 As shown in FIG. 1 , a hardware structure diagram of a pulse wave signal preprocessing device provided by an embodiment of the present invention is provided for any device with data processing capability, except Figure 6 In addition to the processor, memory, and network interface shown, any device with data processing capabilities in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capabilities, which will not be described in detail.

[0131] Accordingly, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the pulse wave signal preprocessing method as described above. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities as described in any of the aforementioned embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card, etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capabilities. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capabilities, and may also be used to temporarily store data that has been output or is to be output.

[0132] Based on the embodiment of the aforementioned pulse wave signal preprocessing method, the present application also provides a forehead physiological signal monitoring system, including a physiological signal acquisition device, a transmission network and a host computer. The physiological signal acquisition device is used to collect physiological signals (including forehead temperature and pulse waves), and the transmission network (a BLE transmission network is used in one embodiment) is used to forward the physiological signals to the host computer. The host computer preprocesses the physiological signals based on the aforementioned pulse wave signal preprocessing method, and calculates relevant health indicators based on the preprocessed physiological signals and dynamically outputs and displays them.

[0133] In a specific implementation, the physiological signal acquisition device is built into the headband for easy wearing, while performing low power consumption and miniaturization. The lower computer acquisition device implementation includes two parts: hardware design and software design. Figure 7 As shown, the hardware design of the device includes a pulse wave acquisition unit, a forehead temperature acquisition unit, and an MCU (Micro Controller Unit). The pulse wave acquisition unit includes an LED light source and a photoelectric sensor, that is, it collects the original pulse wave signal by means of reflective PPG, and then transmits it to the MCU after filtering, amplification, and analog-to-digital conversion. There are two channels of original pulse wave signals, corresponding to the collection results under red light and infrared light respectively; the forehead temperature acquisition unit uses a non-contact infrared measurement method to collect forehead temperature signals, and transmits them to the MCU after filtering, amplification, and analog-to-digital conversion; the MCU is used to organize and encapsulate the received collected data into data packets and forward them to the BLE transmission network. As shown Figure 8 As shown in the figure, the device software design realizes the acquisition of pulse wave and forehead temperature raw data streams through the I2C interface protocol, and then divides and assembles these raw data streams into data packets and forwards them to the BLE slave module via UART. The contents of the data packet are, from the beginning, the packet header identifier, data type identifier, raw data length, and raw data. The software design of the physiological signal acquisition device also realizes the low-power operation logic of pulse wave detection, and sets the threshold for switching from the normal acquisition state to the stop acquisition state to V normal_limit The threshold for switching from the acquisition state to the normal acquisition state is V stop_limit In the normal acquisition state, all LED light sources work normally. When the device is far away from the target, the received signal strength decreases to V normal_limit Next, turn off the red LED and reduce the infrared LED intensity to a low level to reduce power consumption. The physiological signal acquisition device switches from the normal acquisition state to the stop acquisition state. When the device approaches the target to be detected, the received signal strength increases to V stop_limit After the above, all LEDs resume normal operation, and the physiological signal acquisition device switches from the stop acquisition state to the normal acquisition state.

[0134] The BLE transmission network implementation includes two parts: hardware design and software design. Figure 7 As shown, the hardware design of the BLE transmission network includes a slave module and a host module. The slave module is connected to the lower computer hardware through a soft line, and the host module is connected to the PC device through a USB interface. Both use the UART interface protocol to communicate with the device end. Figure 3 As shown, the software design of the BLE transmission network enables the BLE host to automatically identify and connect to the target BLE slave, as well as stable data transmission. Specifically, the BLE host's operating states are divided into connected and idle states. When in the idle state, it periodically scans for connectable BLE devices. When a matching BLE slave is identified, it initiates and establishes a connection, using the target slave's custom name as the identification basis. When in the connected state, data can be transparently transmitted between the BLE master and slave. To ensure transmission quality, BLE parameters such as connection interval, slave device latency, timeout period, and transmit power are optimized.

[0135] like Figure 8 As shown, the host computer software unpacks and parses the data after receiving it from the BLE host, and then puts the parsed data into the data stream queue. It first preprocesses the signal to suppress interference and noise, then calculates the heart rate and blood oxygen saturation indicators, and finally outputs the analysis results and the processed signal waveform to the UI interface to realize dynamic display of data, wherein the UI interface is implemented based on the QT framework.

[0136] The process of calculating the physiological indicators of heart rate and blood oxygen saturation in the host computer is as follows: Figure 9 Forehead temperature can be directly obtained by reading the raw data, while heart rate and blood oxygen saturation need to be numerically calculated based on the pre-processed pulse wave data.

[0137] The heart rate is calculated using the maximum peak value method of the amplitude spectrum. The specific steps are as follows:

[0138] S11: Calculate the pulse wave signal amplitude spectrum, as shown below:

[0139]

[0140] in is the preprocessed pulse wave signal, S(ω) is the corresponding spectrum, real(·) means taking the real part, and imag(·) means taking the imaginary part.

[0141] S12: The reasonable range of heart rate is 30-200 bpm, and the corresponding frequency range is 0.5-3.3 Hz. Find the maximum peak of the amplitude spectrum and its index frequency f within this range. peak , then heart rate HR=f peak ×60.

[0142] The numerical calculation method of blood oxygen saturation is derived from the Lambert-Beer law, and the calculation formula is as follows:

[0143]

[0144] SpO2=aR 2 +bR+c

[0145] Where R is the perfusion index, SpO2 is the blood oxygen saturation, λ1 corresponds to the red light wavelength, λ2 corresponds to the infrared light wavelength, and I AC Represents the AC component in the signal, I DC Represents the DC component in the signal. The coefficients a, b, and c are determined by experimental calibration and their values ​​vary in different devices.

[0146] This system is designed for sports assistance scenarios and can also meet the daily health monitoring needs of a variety of groups, including those recovering from surgery, patients with chronic diseases, and the elderly. The system's equipment is positioned on the forehead to ensure signal quality while minimizing user impact and inconvenience. The system utilizes affordable hardware solutions, while software optimization further reduces overall costs. Specifically, the system comprises acquisition equipment, preprocessing and numerical calculation algorithms, and supporting software development, completing the complete hardware and software chain from signal acquisition, transmission, analysis, and output. Focusing on sports scenarios, it optimizes the user experience from multiple perspectives. First, the acquisition equipment was designed with miniaturization, lightweight, and low power consumption in mind. The device is embedded in a retractable headband for easy wear. Second, the device connects to the PC via BLE wireless communication, minimizing power consumption while optimizing connection parameters to ensure stable transmission. The PC software integrates the aforementioned pulse wave signal preprocessing methods and basic health indicator numerical calculation methods, further enhancing signal interference immunity and providing real-time output of physiological indicator calculations, allowing users to quickly understand their basic health status.

[0147] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the contents disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed in this application.

[0148] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A pulse wave signal preprocessing method, characterized in that: include: Time-frequency transformation: obtaining an original pulse wave signal, eliminating noise from the original pulse wave signal, and performing time-frequency transformation using short-time Fourier transform to obtain a first time-frequency graph; Time-frequency post-processing: performing time-frequency rearrangement on the first time-frequency graph according to the frequency axis to obtain a second time-frequency graph; Time-frequency ridge extraction: constructing a cost function based on the time-frequency energy and continuity constraint of the second time-frequency graph, and extracting the time-frequency ridge by minimizing the cost function; Signal reconstruction: Based on the time-frequency ridge, the components used for reconstruction in the pulse wave signal are screened, and then the signal is reconstructed; The signal reconstruction step includes the following sub-steps: Determine the fundamental wave candidate component set: Based on the reasonable frequency range corresponding to the heart rate, set a screening threshold, and consider the components whose time-frequency points fall within the reasonable frequency range and whose proportion exceeds the screening threshold as fundamental wave candidate components and include them in the fundamental wave candidate component set; For each fundamental candidate component , calculate it and any other component similarity between For each fundamental candidate component , select the first two components with the highest similarity as their harmonic candidate components , based on the similarity between the fundamental candidate component and its harmonic candidate classification, calculate each fundamental candidate component Quality index; The component with the highest quality index in the fundamental candidate component set is selected as the fundamental wave of the valid signal. The harmonic candidate component corresponding to this component is the harmonic of the valid signal. Then, the fundamental wave and the corresponding harmonic are reconstructed according to the following formula: ， in Indicates taking the real part of the signal, is the reconstructed signal, is the time-frequency ridge of the i-th component; Among them, for each fundamental candidate component , calculate it and any other component according to the following rules Similarity between: definition 、 They are fundamental candidate components 、 The corresponding instantaneous frequency sequence uses the coefficient of variation to measure the difference in the instantaneous frequency characteristics between the two components. The calculation formula is as follows: ， in is the instantaneous frequency variation coefficient, , yes The mean of , N is the length of the instantaneous frequency sequence; definition 、 They are fundamental candidate components 、 The corresponding instantaneous spectrum amplitude sequence uses the coefficient of variation index to measure the difference in the instantaneous spectrum amplitude characteristics between the two components. The calculation formula is as follows: ， in is the coefficient of variation of the instantaneous spectrum amplitude, , yes The mean of , N is the data length; Based on the instantaneous frequency variation coefficient and the instantaneous spectrum amplitude variation coefficient, the fundamental candidate component is calculated using the Euclidean distance method. 、 The similarity between ,as follows: 。 2. The method according to claim 1, characterized in that In the time-frequency transformation step, a short-time Fourier transform is performed as shown in the following formula: ， in is the first time-frequency diagram obtained after time-frequency transformation, is the input raw pulse wave signal, is the window function, is the imaginary unit, is the time variable, is a frequency variable, and the window function adopts the Gaussian window shown in the following formula: ， in is the standard deviation parameter.

3. The method according to claim 1, characterized in that In the time-frequency post-processing step, the following formula is used to estimate the signal at a given time and frequency The instantaneous frequency is: ， The energy of the short-time Fourier transform is redistributed to the estimated instantaneous frequency position according to the following formula to make the energy more concentrated: ， in, is the second time-frequency diagram obtained after post-processing. is the Dirac function, is the frequency variable in the first time-frequency diagram before post-processing.

4. The method according to claim 1, wherein In the time-frequency ridge extraction step, the cost function is defined as follows: , which consists of the time-frequency energy and continuity constraints of the signal: ， in is the equilibrium parameter; Using dynamic programming algorithm, from time At the beginning, by minimizing the global cost, we gradually search for the optimal path shown in the following formula along the time-frequency plane, that is, the time-frequency ridge line : ， in represents the smoothness penalty term.

5. A forehead physiological signal monitoring system, characterized in that: Including physiological signal acquisition equipment, transmission network and host computer; The physiological signal acquisition device is used to acquire physiological signals, and the physiological signals include forehead temperature and pulse wave; The transmission network is used to forward the physiological signal to a host computer; The host computer preprocesses the physiological signal based on the method according to any one of claims 1 to 4, and calculates relevant health indicators based on the preprocessed physiological signal and dynamically outputs and displays them.

6. The system according to claim 5, characterized in that In this system: The physiological signal acquisition device is built into the headband and includes a pulse wave acquisition unit, a forehead temperature acquisition unit, and a microcontroller. The pulse wave acquisition unit includes an LED light source and a photoelectric sensor, and acquires the original pulse wave signal through a reflective PPG method, which is filtered, amplified, and transmitted to the microcontroller after analog-to-digital conversion. The forehead temperature acquisition unit uses a non-contact infrared measurement method to acquire the forehead temperature signal, which is filtered, amplified, and transmitted to the microcontroller after analog-to-digital conversion. The microcontroller is used to organize and encapsulate the received collected data into data packets and forward them to the transmission network. The transmission network includes a host module and a slave module, the slave module is connected to the physiological signal acquisition device, and the host module is connected to the host computer, so as to forward the physiological signals from the signal acquisition device to the host computer; The host computer receives the physiological signal data from the host module, unpacks and parses it, puts the parsed data into a data stream queue, performs signal preprocessing on it to suppress interference and noise, calculates heart rate and blood oxygen saturation indicators, and outputs the analysis results and processed signal waveforms to the UI interface to realize dynamic display of data; The heart rate is calculated using the maximum peak value of the amplitude spectrum: Calculate the pulse wave signal amplitude spectrum as shown below: ， ， in is the preprocessed pulse wave signal, is the corresponding spectrum, represents the real part, Indicates taking the imaginary part; S12: Find the maximum peak of the amplitude spectrum and its index frequency within the frequency range corresponding to the reasonable heart rate range , then the heart rate ; The numerical calculation method of blood oxygen saturation is derived from the Lambert-Beer law, and the calculation formula is as follows: ， ， in is the perfusion index, is the blood oxygen saturation, Corresponding to the wavelength of red light, Corresponding to infrared wavelength, represents the AC component in the signal, Represents the DC component in the signal, the coefficient Determined by experimental calibration.

7. The system according to claim 5, characterized in that In the physiological signal acquisition device, the threshold value for switching from the normal acquisition state to the stop acquisition state is set to , the threshold for switching from the stop acquisition state to the normal acquisition state is In normal acquisition state, all LED light sources work normally. When the device is far away from the target, the received signal strength decreases to Next, turn off the red LED and reduce the infrared LED intensity to a low level to reduce power consumption. The physiological signal acquisition device switches from the normal acquisition state to the stop acquisition state. When the device approaches the target to be detected, the received signal strength increases to After the above, all LEDs resume normal operation, and the physiological signal acquisition device switches from the stop acquisition state to the normal acquisition state.

8. The system according to claim 5, wherein: In the transmission network, the host module automatically identifies and connects to the slave module: The host module's working states are divided into connected state and idle state. When it is in the idle state, it periodically scans for connectable devices. When a matching slave is identified, it initiates and establishes a connection. The identification basis is the custom name of the target slave.

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