WO2020143161A1 - Méthode et dispositif de détection de fréquence cardiaque - Google Patents

Méthode et dispositif de détection de fréquence cardiaque Download PDF

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Publication number
WO2020143161A1
WO2020143161A1 PCT/CN2019/091102 CN2019091102W WO2020143161A1 WO 2020143161 A1 WO2020143161 A1 WO 2020143161A1 CN 2019091102 W CN2019091102 W CN 2019091102W WO 2020143161 A1 WO2020143161 A1 WO 2020143161A1
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Prior art keywords
signal
heart rate
local mean
segment
area
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PCT/CN2019/091102
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English (en)
Chinese (zh)
Inventor
张旺
庄伯金
王少军
肖京
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平安科技（深圳）有限公司
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Publication of WO2020143161A1 publication Critical patent/WO2020143161A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • 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
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate

Definitions

  • the present application relates to the field of signal processing technology, and in particular, to a heart rate detection method and device.
  • the vibration of the heartbeat is very weak, and the human body is often accompanied by complicated conditions such as large shaking of the limbs, coughing, leg shaking, hand shaking, etc. during the measurement process, and these An abnormal interference signal is much stronger than a heartbeat, and it is easy to overwhelm the heartbeat signal, resulting in low accuracy of heart rate detection.
  • the embodiments of the present application provide a heart rate detection method and device to solve the problem of low accuracy of heart rate detection in the prior art.
  • an embodiment of the present application provides a heart rate detection method.
  • the method includes: measuring the heart rate of a user to be detected through a sensor built in the terminal to obtain a signal to be detected; setting a sliding window of a preset duration; according to the sliding window and Split the signal to be detected with a preset step size to obtain N segments, where N is a natural number greater than 1; determine whether the signal in each segment of the N segments belongs to strong noise; if the If the signal belongs to strong noise, the i-th segment is determined to be the target segment, i is a natural number, and i is sequentially taken from 1 to N; all target segments are combined to obtain a strong noise region; the strong noise in the signal to be detected The area is deleted to obtain an effective signal area; the heart rate signal in the effective signal area is detected.
  • an embodiment of the present application provides a heart rate detection device.
  • the device includes: a measurement unit for measuring the heart rate of a user to be detected through a sensor built in the terminal to obtain a signal to be detected; and a setting unit for setting a preset A sliding window with a set duration; a segmentation unit for segmenting the signal to be detected according to the sliding window and a predetermined step size to obtain N segments, where N is a natural number greater than 1; a determination unit is used to determine the N Whether the signal in each segment in the segment belongs to strong noise; the determining unit is used to determine that the i-th segment is the target segment if the signal in the i-th segment belongs to strong noise, i is a natural number, and i takes 1 in turn To N; the merging unit is used to merge all the target fragments to obtain a strong noise region; the deletion unit is used to delete the strong noise region in the signal to be detected to obtain a valid signal region; the detection unit is used to The heart rate signal in the effective signal
  • an embodiment of the present application provides a storage medium, the storage medium includes a stored program, wherein, when the program is running, the device where the storage medium is located is controlled to execute the above-mentioned heart rate detection method.
  • an embodiment of the present application provides a computer device, including a memory and a processor, where the memory is used to store information including program instructions, and the processor is used to control execution of the program instructions, and the program instructions are processed by the processor When loading and executing, the steps of the above-mentioned heart rate detection method are realized.
  • the signal to be detected is divided according to the sliding window and the preset step size to obtain N segments, and it is determined whether the signal in each segment of the N segments belongs to strong noise, and if the signal in a segment belongs to If there is strong noise, the segment is determined as the target segment, and all target segments are merged to obtain a strong noise region, and the strong noise region in the signal to be detected is deleted to obtain an effective signal region, and the heart rate signal in the effective signal region is detected , Because the strong noise area is deleted, thus avoiding the strong noise interference caused by large limb shaking, coughing, leg shaking, and hand shaking, improving the accuracy of detecting heart rate, and solving the low accuracy of detecting heart rate in the prior art The problem.
  • FIG. 1 is a flowchart of an optional heart rate detection method provided by an embodiment of the present application
  • FIG. 2 is a schematic diagram of an optional heart rate detection device provided by an embodiment of the present application.
  • FIG. 3 is a schematic diagram of a computer device provided by an embodiment of the present application.
  • FIG. 1 is a flowchart of an optional heart rate detection method provided by an embodiment of the present application. As shown in FIG. 1, the method includes the following steps:
  • Step 102 The heart rate of the user to be detected is measured by a sensor built in the terminal to obtain a signal to be detected.
  • Step 104 Set a sliding window with a preset duration.
  • Step 106 Divide the detection signal according to the sliding window and the preset step size to obtain N segments, where N is a natural number greater than 1.
  • Step 108 Determine whether the signal in each of the N segments belongs to strong noise.
  • step 110 if the signal in the i-th segment belongs to strong noise, it is determined that the i-th segment is the target segment, i is a natural number, and i is sequentially taken from 1 to N.
  • step 112 all target segments are combined to obtain a strong noise region.
  • Step 114 the strong noise area in the signal to be detected is deleted to obtain an effective signal area.
  • Step 116 Detect the heart rate signal in the effective signal area.
  • the signal to be detected includes a pulse wave signal.
  • the signal to be detected is divided according to the sliding window and the preset step size to obtain N segments, and it is determined whether the signal in each segment of the N segments belongs to strong noise, and if the signal in a segment belongs to If there is strong noise, the segment is determined as the target segment, and all target segments are merged to obtain a strong noise region, and the strong noise region in the signal to be detected is deleted to obtain an effective signal region, and the heart rate signal in the effective signal region is detected , Because the strong noise area is deleted, thus avoiding the strong noise interference caused by large limb shaking, coughing, leg shaking, and hand shaking, improving the accuracy of detecting heart rate, and solving the low accuracy of detecting heart rate in the prior art The problem.
  • determining whether the signal in each segment of the N segments belongs to strong noise includes: determining whether the variance of the signal amplitude in the i-th segment is greater than a preset variance threshold; if the signal amplitude in the i-th segment is If the variance is greater than the preset variance threshold, it is determined that the signal in the i-th segment belongs to strong noise.
  • the variance of the signal amplitude in a certain segment is large, it means that the signal in the segment is likely to be strong noise caused by large limb shaking, coughing, leg shaking, and hand shaking. In this case, determine the segment
  • the signal inside belongs to strong noise and deletes the strong noise, to avoid the influence of strong noise on the accuracy of heart rate detection, and to improve the accuracy of heart rate detection.
  • EMG interference There are three main types of noise in the detection of pulse wave signals: EMG interference, baseline drift and power frequency interference. Among them, the most significant one is EMG interference.
  • the so-called myoelectric interference refers to a mixture of various electrical phenomena in the human body. A certain physiological quantity is sometimes a signal. In another occasion, it may be noise, that is, noise caused by electrical phenomena other than the measured physiological variable.
  • EMG interference is caused by human muscle tremor and occurs randomly, with a frequency range between 5 and 2,000 Hz.
  • Power frequency interference the frequency of the mains voltage is 50Hz, it will cause interference to people's daily life in the form of electromagnetic wave radiation, and this interference is called power frequency interference.
  • the strong noise area in the signal to be detected is deleted to obtain an effective signal area.
  • the effective signal area may still contain EMG interference, baseline drift and power frequency interference. To ensure the accuracy of detection, the effective signal area needs to be
  • the noise reduction processing of the heart rate signal is as follows:
  • performing non-local mean noise reduction on the heart rate signal in the effective signal area to obtain a non-local mean pre-filtering result includes: calculating the attenuation coefficient of the i-th signal point of the heart rate signal in the effective signal area; according to the i The attenuation coefficient of the signal points is subjected to non-local mean denoising to obtain the i-th signal point of the non-local mean pre-filtering result, where the attenuation coefficient of the i-th signal point is calculated according to the following formula: Where h i is the attenuation coefficient of the i-th signal point, h 0 is a fixed attenuation coefficient, g i is the gradient value of the i-th signal point, g max is the preset maximum gradient value, and T is the preset gradient threshold.
  • g max can be set to a value between 15 and 20.
  • T can be set to a value between 10 and 13.
  • h 0 can be set to a value between 0.2 and 20.
  • the size of the search window can be set to 7 ⁇ 7, and the signal block has the same size as the similar window, which can be set to 3 ⁇ 3.
  • the current non-local mean noise reduction method of heart rate signals does not take into account that the heart rate signal has obvious periodicity and regional characteristics.
  • the same attenuation coefficient is sampled in different wave band regions of the heart rate signal, which makes it difficult to take into account the smoothness of the uniform region And the protection of detailed information, the filtering effect is poor.
  • the core parameter of non-local mean noise reduction that is, the attenuation coefficient
  • the attenuation coefficient is adaptively adjusted, which can effectively suppress the noise while better protecting the details of the signal to achieve A better filtering effect.
  • An embodiment of the present application further provides a heart rate detection device, which is used to perform the above heart rate detection method.
  • the device includes: a measurement unit 10, a setting unit 20, a division unit 30, a judgment unit 40, and a determination Unit 50, merge unit 60, delete unit 70, detection unit 80.
  • the measuring unit 10 is configured to measure the heart rate of the user to be detected through a sensor built in the terminal to obtain a signal to be detected.
  • the setting unit 20 is used to set a sliding window with a preset duration.
  • the dividing unit 30 is configured to divide the detection signal according to the sliding window and the preset step size to obtain N segments, where N is a natural number greater than 1.
  • the judging unit 40 is used to judge whether the signal in each of the N segments belongs to strong noise.
  • the determining unit 50 is configured to determine that the i-th segment is a target segment, i is a natural number if the signal in the i-th segment belongs to strong noise, and i takes 1 to N in sequence.
  • the merging unit 60 is used for merging all target segments to obtain strong noise regions.
  • the deleting unit 70 is configured to delete the strong noise area in the signal to be detected to obtain an effective signal area.
  • the detection unit 80 is configured to detect the heart rate signal in the effective signal area.
  • the signal to be detected includes a pulse wave signal.
  • the signal to be detected is divided according to the sliding window and the preset step size to obtain N segments, and it is determined whether the signal in each segment of the N segments belongs to strong noise, and if the signal in a segment belongs to If there is strong noise, the segment is determined as the target segment, and all target segments are merged to obtain a strong noise region, and the strong noise region in the signal to be detected is deleted to obtain an effective signal region, and the heart rate signal in the effective signal region is detected , Because the strong noise area is deleted, thus avoiding the strong noise interference caused by large limb shaking, coughing, leg shaking, and hand shaking, improving the accuracy of detecting heart rate, and solving the low accuracy of detecting heart rate in the prior art The problem.
  • the judgment unit 40 includes: a judgment subunit and a determination subunit.
  • the judging subunit is used to judge whether the variance of the signal amplitude in the i-th segment is greater than a preset variance threshold.
  • the determination subunit is used to determine that the signal in the i-th segment belongs to strong noise if the variance of the signal amplitude in the i-th segment is greater than the preset variance threshold.
  • the detection unit 80 includes: a filtering subunit, a noise reduction subunit, a correction subunit, and a detection subunit.
  • the filtering subunit is used to perform average filtering on the heart rate signal in the effective signal area and calculate the gradient value of each signal point.
  • the noise reduction sub-unit is used for performing non-local mean denoising on the heart rate signal in the effective signal area to obtain a non-local mean pre-filtering result.
  • the correction subunit is used for correcting the non-local mean pre-filtering result according to the gradient value of each signal point.
  • the detection subunit is used for detecting the corrected non-local mean pre-filtering result.
  • the noise reduction subunit includes: a calculation module and a noise reduction module.
  • the calculation module is used to calculate the attenuation coefficient of the ith signal point of the heart rate signal in the effective signal area.
  • the noise reduction module is used to perform non-local mean denoising according to the attenuation coefficient of the i-th signal point to obtain the i-th signal point of the non-local mean pre-filtering result.
  • the attenuation coefficient of the i-th signal point is calculated according to the following formula: Where h i is the attenuation coefficient of the i-th signal point, h 0 is a fixed attenuation coefficient, g i is the gradient value of the i-th signal point, g max is the preset maximum gradient value, and T is the preset gradient threshold.
  • the noise reduction module includes: a first calculation submodule and a second calculation submodule.
  • the first calculation submodule is used according to the formula Calculate the signal block and similar window weights, where ⁇ (i,j) represents the weight of the i-th signal block and the jth similar window, C i represents the normalization parameter, G represents the Gaussian kernel function, and * represents the convolution operation, O(N i ) represents the i-th signal block, O(N j ) represents the j-th similar window, Denotes the L2 norm, h i denotes the attenuation coefficient of the i-th signal point.
  • ⁇ the search window
  • ⁇ (i, j) the weight of the i-th signal block and the j-th similar window
  • O j the j-th signal point of the heart rate signal in the effective signal area.
  • An embodiment of the present application provides a computer nonvolatile readable storage medium, where the storage medium includes a stored program, wherein, when the program is running, the device where the computer nonvolatile readable storage medium is located is controlled to perform the following steps: The sensor of the user to measure the heart rate of the user to be detected, get the signal to be detected; set the sliding window of the preset duration; segment the signal to be detected according to the sliding window and the preset step size, get N segments, N is a natural number greater than 1; judge Whether the signal in each segment of the N segments belongs to strong noise; if the signal in the i segment belongs to strong noise, determine the i segment as the target segment, i is a natural number, i takes 1 to N in turn; The target fragments are combined to obtain a strong noise area; the strong noise area in the signal to be detected is deleted to obtain an effective signal area; and the heart rate signal in the effective signal area is detected.
  • the device where the computer non-volatile readable storage medium is located also performs the following steps: determine whether the variance of the signal amplitude in the i-th segment is greater than a preset variance threshold; if the If the variance of the signal amplitude is greater than the preset variance threshold, it is determined that the signal in the i-th segment belongs to strong noise.
  • the device where the computer non-volatile readable storage medium is located also performs the following steps: performing an average filtering on the heart rate signal in the effective signal area, calculating the gradient value of each signal point; The heart rate signal in is subjected to non-local mean denoising to obtain the non-local mean pre-filtering result; the non-local mean pre-filtering result is modified according to the gradient value of each signal point; the corrected non-local mean pre-filtering result is detected.
  • the device where the computer non-volatile readable storage medium is located also performs the following steps: calculating the attenuation coefficient of the i-th signal point of the heart rate signal in the effective signal area; according to the i-th signal point The attenuation coefficient is used to reduce the noise of the non-local mean to obtain the i-th signal point of the non-local mean pre-filtering result, where the attenuation coefficient of the i-th signal point is calculated according to the following formula: Where h i is the attenuation coefficient of the i-th signal point, h 0 is a fixed attenuation coefficient, g i is the gradient value of the i-th signal point, g max is the preset maximum gradient value, and T is the preset gradient threshold.
  • controlling the device where the computer non-volatile readable storage medium is located while the program is running also performs the following steps: Calculate the signal block and similar window weights, where ⁇ (i,j) represents the weight of the i-th signal block and the jth similar window, C i represents the normalization parameter, G represents the Gaussian kernel function, and * represents the convolution operation, O(N i ) represents the i-th signal block, O(N j ) represents the j-th similar window, Represents the L2 norm, h i represents the attenuation coefficient of the ith signal point; according to the formula Calculate the i-th signal point of the non-local mean pre-filtering result, M i represents the i-th signal point of the non-local mean pre-filtering result, ⁇ represents the search window, and ⁇ (i, j) represents the i-th signal block and j-th Similar window weights, O j represents the jth signal point of the heart rate signal in the effective signal
  • An embodiment of the present application provides a computer device including a memory and a processor.
  • the memory is used to store information including program instructions.
  • the processor is used to control the execution of the program instructions.
  • the built-in sensor of the terminal measures the heart rate of the user to be detected to obtain the signal to be detected; sets the sliding window of the preset duration; divides the detection signal according to the sliding window and the preset step size to obtain N segments, N is a natural number greater than 1
  • the following steps are also implemented: determine whether the variance of the signal amplitude in the i-th segment is greater than the preset variance threshold; if the variance of the signal amplitude in the i-th segment is greater than the preset The variance threshold determines that the signal in the ith segment belongs to strong noise.
  • the heart rate signal in the effective signal area is average filtered to calculate the gradient value of each signal point; and the heart rate signal in the effective signal area is non-local Mean value noise reduction to obtain non-local mean pre-filtering results; correct non-local mean pre-filtering results according to the gradient value of each signal point; detect the corrected non-local mean pre-filtering results.
  • the following steps are also implemented: calculating the attenuation coefficient of the i-th signal point of the heart rate signal in the effective signal area; performing non-local mean reduction according to the attenuation coefficient of the i-th signal point Noise, the i-th signal point of the non-local mean pre-filtering result is obtained, where the attenuation coefficient of the i-th signal point is calculated according to the following formula: Where h i is the attenuation coefficient of the i-th signal point, h 0 is a fixed attenuation coefficient, g i is the gradient value of the i-th signal point, g max is the preset maximum gradient value, and T is the preset gradient threshold.
  • FIG. 3 is a schematic diagram of a computer device provided by an embodiment of the present application.
  • the computer device 50 of this embodiment includes a processor 51, a memory 52, and a computer program 53 stored in the memory 52 and executable on the processor 51.
  • the computer program 53 is executed by the processor 51
  • the functions of each model/unit in the center rate detection device of the embodiment are implemented. To avoid repetition, details are not described here one by one.
  • the computer device 50 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the computer equipment may include, but is not limited to, the processor 51 and the memory 52.
  • FIG. 3 is only an example of the computer device 50, and does not constitute a limitation on the computer device 50, and may include more or less components than shown, or combine some components, or different components.
  • computer equipment may also include input and output devices, network access devices, buses, and so on.
  • the so-called processor 51 may be a central processing unit (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application-specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the memory 52 may be an internal storage unit of the computer device 50, such as a hard disk or a memory of the computer device 50.
  • the memory 52 may also be an external storage device of the computer device 50, for example, a plug-in hard disk equipped on the computer device 50, a smart memory card (Smart Media (SMC), a secure digital (SD) card, and a flash memory card (Flash Card) etc.
  • the memory 52 may also include both the internal storage unit of the computer device 50 and the external storage device.
  • the memory 52 is used to store computer programs and other programs and data required by computer devices.
  • the memory 52 may also be used to temporarily store data that has been or will be output.
  • the disclosed system, device, and method may be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the unit is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined Or it can be integrated into another system, or some features can be ignored or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical, or other forms.
  • 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 may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware, or in the form of hardware plus software functional units.
  • the above integrated unit implemented in the form of a software functional unit may be stored in a computer-readable storage medium.
  • the above software functional unit is stored in a storage medium, and includes several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) or processor (Processor) to perform the methods described in the embodiments of the present application Partial steps.
  • the foregoing storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program code .

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Abstract

La présente invention concerne un procédé et un dispositif de détection de fréquence cardiaque. Le procédé consiste : à mesurer la fréquence cardiaque d'un utilisateur à détecter à l'aide d'un capteur intégré d'un terminal afin d'obtenir un signal à détecter (S102) ; à configurer une fenêtre glissante d'une durée prédéfinie (S104) ; à segmenter le signal à détecter en fonction de la fenêtre glissante et d'une taille d'étape prédéfinie afin d'obtenir N segments, N étant un nombre naturel supérieur à 1 (S106) ; à déterminer le fait que le signal à l'intérieur de chaque segment parmi les N segments est un bruit fort (S108) ; si le signal à l'intérieur d'un i-ième segment est un bruit fort, à déterminer que le i-ième segment est un segment cible, i étant un nombre naturel, et i étant 1 à N séquentiellement (S110) ; à combiner tous les segments cibles afin d'obtenir une région de bruit fort (S112) ; à effacer la région de bruit fort dans le signal à détecter afin d'obtenir une région de signal efficace (S114) ; et à détecter un signal de fréquence cardiaque dans la région de signal efficace (S116). La solution technique peut résoudre le problème dans la technologie existante dans laquelle la précision de détection de la fréquence cardiaque est faible.
PCT/CN2019/091102 2019-01-09 2019-06-13 Méthode et dispositif de détection de fréquence cardiaque WO2020143161A1 (fr)

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