CN114767064B - Child sleep monitoring method, system and electronic device - Google Patents
Child sleep monitoring method, system and electronic device Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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Abstract
The invention discloses a child sleep monitoring method, which belongs to the field of medical signal processing, and comprises the steps of installing three-axis acceleration sensors, collecting information, processing respiratory signals of each three-axis acceleration sensor, processing heart rate signals of each three-axis acceleration sensor, comprehensively judging sleep conditions and the like, wherein the sensors are arranged at four positions of two upper arms, a chest and an abdomen, and when sleeping posture changes to press one sensor, other sensors can work continuously to prevent false alarms; by arranging the sensors on the clothes corresponding to the chest and the abdomen, the respiratory problem caused by sleep blockage can be monitored; early warning is carried out on life-threatening events, and general negative events are reminded and recorded; has good resolving power to sleeping gesture and low false alarm. The invention also relates to a child sleep monitoring system and a device for implementing the child sleep monitoring method.
Description
Technical Field
The invention relates to the field of medical monitoring, in particular to a method and a system for monitoring sleep of children and an electronic device.
Background
Sleep is very important for physiological and psychological health, especially for children in the growing stage. However, current sleep monitoring of children has a great problem. Because the wrist of the child is slim, the wearable wristwatch is easy to slip and leak light; the heart power of the child is small, the heart rate is high, the sleeping posture is not fixed, the mattress type sleep monitoring system is easy to miss signals, the child often has adult sleep, the heart beating signal of the child is submerged, and the child is interfered by the adult; the camera\radar sleep monitoring system is also affected by sleeping gestures, and the accuracy is limited when the quilt is covered.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the purposes of the invention is to provide a child sleep monitoring method with high measurement accuracy, which is not influenced by sleeping postures and environments.
In order to overcome the defects of the prior art, the second aim of the invention is to provide a children sleep monitoring system which is not influenced by sleeping postures and environment and has high measurement precision.
In order to overcome the defects of the prior art, the third purpose of the invention is to provide a child sleep monitoring device with high measurement accuracy, which is not influenced by sleeping postures and environments.
One of the purposes of the invention is realized by adopting the following technical scheme:
a method for monitoring sleep of a child, comprising the steps of:
and (3) installing a triaxial acceleration sensor: the method comprises the steps that 4 triaxial acceleration sensors are respectively arranged outside clothes of two upper arms, a chest and an abdomen;
collecting information: each of the three-axis acceleration sensors acquires respiration and heart rate signals:
processing the respiration signal of each triaxial acceleration sensor: filtering the respiratory signals of each triaxial acceleration sensor, carrying out Fourier transform on the filtered signals, acquiring respiratory waveforms through meyer wavelet transform and recording when respiratory waves exist in the transformed signals, and judging whether the signals are in a pressed state, a respiratory disorder or a body movement state through the strength and comparison of the total signals when the respiratory waves do not exist in the transformed signals;
processing heart rate signals of each triaxial acceleration sensor: filtering heart rate of each triaxial acceleration sensor, carrying out Fourier transform on the filtered signals, extracting periodic heart rate when heart rate period exists in the transformed signals, and judging whether the signals are in a pressed state or not according to intensity of overall signals or resolving heart beat according to energy density to calculate periodicity when heart rate period does not exist in the transformed signals;
comprehensively judging sleeping conditions: discarding the breathing signals and heart rate signals of the four triaxial acceleration sensors in the pressed state, comprehensively analyzing the residual signals, judging that the child is in an irregular body movement state, a breathing obstruction or a normal state, respectively carrying out early warning on the irregular body movement state and the breathing obstruction, and recording the normal state.
Further, in the step of comprehensively judging the sleeping condition, the respiratory obstruction is judged by a triaxial acceleration sensor of the chest and the abdomen, and the respiratory obstruction is judged when the chest expands and the abdomen contracts.
Further, in the step of processing the heart rate signals of each triaxial acceleration sensor, whether a heart rate period exists or not is judged according to whether a significant peak exists in the transformed signals.
Further, in the step of processing the heart rate signal of each triaxial acceleration sensor, the energy density resolution heart beat calculation periodicity is specifically: then an attempt is made to obtain the heart rate by means of the entropy of the information, the calculation formula of the entropy being:wherein I is e For signal strength at a certain moment, p i To appear I e Probability of H s For the entropy value of a specific time interval, calculating the entropy value in a fixed time through a sliding window, drawing an entropy fluctuation curve, and evaluating the periodicity (frequency domain transformation and searching for a significant peak), if the obvious periodicity is found, obtaining the heart rate by adopting an entropy mode.
Further, in the step of comprehensively judging the sleep condition, the recording of the normal state is specifically: the final respiration signal is obtained by weighting the data of the four triaxial acceleration sensors.
Further, in the step of comprehensively judging the sleep condition, the recording of the normal state is specifically: and finally weighting the data of the four triaxial acceleration sensors of the heart rate signal after Kalman filtering.
Further, in the step of comprehensively judging the sleeping condition, when the child is in a respiratory obstruction state, reminding the child of changing the body position when respiratory obstruction is frequent but the duration time is short; early warning is given when respiratory obstruction continues unrelieved and heart rate decreases.
Further, in the process of filtering the respiratory signal of each triaxial acceleration sensor, the reserved signal is between 0.13 and 0.66Hz, namely 7.8 and 39.6bpm; in the process of filtering the heart rate signal of each triaxial acceleration sensor, the first step separates 4-11Hz signals, and the second step retains signals at 0.8-3.5Hz, namely 48-210bpm.
The second purpose of the invention is realized by adopting the following technical scheme:
a child sleep monitoring system for implementing any one of the child sleep monitoring methods described above.
The third purpose of the invention is realized by adopting the following technical scheme:
a child sleep monitoring arrangement comprising
Four triaxial acceleration sensors which are respectively arranged outside clothes of two upper arms, the chest and the abdomen of the child;
a processor;
a memory communicatively coupled to the processor;
the memory stores data collected by the four triaxial acceleration sensors and instructions executable by the processor to implement any of the above mentioned methods of child sleep monitoring.
Compared with the prior art, the children sleep monitoring method has the advantages that the sensors are arranged at four positions of the two upper arms, the chest and the abdomen, and when one sensor is pressed by the sleeping posture change, the other sensors can work continuously, so that false alarms are prevented; by arranging the sensors on the clothes corresponding to the chest and the abdomen, the respiratory problem caused by sleep blockage can be monitored; has better resolution capability for apnea, tachypnea and respiratory obstruction; early warning is carried out on life-threatening events, and general negative events are reminded and recorded; the sleeping posture is well resolved (left and right side positions, prone position and the like), and false alarms are low (means such as radar waves and the like); the heart rate and pulse intensity can be measured well, and the measurement can be corrected automatically through sleeping posture; by analyzing body movement, respiration and heart rate, sleep quality related data are obtained, and due to the fact that the weight of children is light, the signal to noise ratio is too low due to the technical mode of the pressure sensor, but the measurement of the acceleration sensor is not affected.
Drawings
FIG. 1 is a flow chart of a method of child sleep monitoring of the present invention;
FIG. 2 is a flow chart of processing the respiration signal of each triaxial acceleration sensor;
FIG. 3 is a flow chart of processing the heart rate signal of each of the tri-axial acceleration sensors;
FIG. 4 is a flow chart for comprehensively judging sleep conditions;
FIG. 5 is a schematic diagram of a child sleep monitoring method implementation of the present invention;
fig. 6 is a perspective view of a patch;
FIG. 7 is raw data of respiratory signals;
fig. 8 is a respiratory waveform of a respiratory signal after meyer wavelet transform.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It will be understood that when an element is referred to as being "fixed to" another element, it can be directly on the other element or be present as another intermediate element through which the element is fixed. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. When an element is referred to as being "disposed on" another element, it can be directly on the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Fig. 1 to 4 are flowcharts of a method for monitoring sleep of a child according to the present invention, which includes the steps of:
and (3) installing a triaxial acceleration sensor: the method comprises the steps that 4 triaxial acceleration sensors are respectively arranged outside clothes of two upper arms, a chest and an abdomen;
collecting information: each tri-axial acceleration sensor acquires respiration and heart rate signals:
processing the respiration signal of each triaxial acceleration sensor: filtering the respiratory signals of each triaxial acceleration sensor, carrying out Fourier transform on the filtered signals, acquiring respiratory waveforms through meyer wavelet transform and recording the respiratory waveforms when respiratory waves exist in the transformed signals (see fig. 7-8, which are comparison graphs before and after the signal meyer wavelet transform), and judging whether the signals are in a compressed state, in a respiratory disorder or in a body movement state according to the strength of the overall signals when respiratory waves do not exist in the transformed signals;
processing heart rate signals of each triaxial acceleration sensor: filtering heart rate of each triaxial acceleration sensor, carrying out Fourier transform on the filtered signals, extracting periodic heart rate when heart rate period exists in the transformed signals, and judging whether the signals are in a pressed state or not according to intensity of overall signals or resolving heart beat according to energy density to calculate periodicity when heart rate period does not exist in the transformed signals;
comprehensively judging sleeping conditions: discarding the breathing signals and heart rate signals of the four triaxial acceleration sensors in the pressed state, comprehensively analyzing the residual signals, judging that the child is in an irregular body movement state, a breathing obstruction or a normal state, respectively carrying out early warning on the irregular body movement state and the breathing obstruction, and recording the normal state.
The step of installing the triaxial acceleration sensor is shown in fig. 5, and specifically comprises the following steps: the triaxial acceleration sensor adopts a patch (shown in fig. 6), the x-axis refers to the direction of the dial surface perpendicular to the arm, the y-axis refers to the direction of the patch parallel to the arm, and the z-axis refers to the direction perpendicular to the patch surface. Each patch contains 1 triaxial acceleration sensor, can sense the azimuth and vibration of the patch, has a sampling rate of 50Hz, and has a measuring range of +/-1 g, and is set to be 14-bit precision.
The 4 triaxial acceleration sensors are respectively mounted on the outer parts of the clothes of the two upper arms, the chest and the abdomen. This application installs 4 triaxial acceleration sensor in two upper arms, chest and the clothing outsidely of belly respectively, compares the wrist, and the paster is more suitable in chest and upper arm. If the patch is placed on the wrist, such as a smart wristwatch, the breathing data measurement becomes worse. The optimal measurement point of respiration is the thoracic region. There are some prior art techniques in which a pressure sensor (e.g., PVDF, pressure membrane) is placed in front of the chest, but because the pressure sensor needs to be against the skin, it is uncomfortable to tighten and affects sleep. The acceleration sensor is not limited by the method, the clothes do not need to be tightly fitted, and the sensor is placed on the chest, the upper arm and the abdomen and does not limit the chest movement during breathing. The current acceleration sensor hardware technology for the intelligent wrist watch and the wrist ring can reach very high precision, but as the severe sports scene and the data storage quantity are required to be considered in daily activities, the measurement range of most product designs is as high as +/-16 g, so that the final resolution is only 1mg-16mg, the heart beat intensity cannot be finely measured, and the signal to noise ratio is low under the non-close condition. The patch is only applied to sleeping, the measuring range can be limited to +/-1 g, the acceleration of 0.1mg can be measured at the minimum by using general 14-bit data acquisition, the limitation on the texture of the night suit can be reduced, and the cost of the whole system is reduced (only the patch is needed to be provided, and special underwear materials are not needed).
The sleeping posture change can press the patch, and at the moment, the respiration measurement accuracy is reduced, so that the patch needs to be placed at four positions to prevent false alarms. The significance of the respiratory peak can be determined by analyzing the frequency spectrum when the chest is compressed, and the respiratory wave amplitude is obviously suppressed and jumps to the sensor of the upper arm, and the data processing mode of the upper arm is the same as that of the chest. In the sleeping state, the chest, the abdomen and the arms at the two sides are extremely unlikely to be pressed at the same time, so that the invention can avoid abnormal breathing data and false early warning caused by sleeping postures. Another advantage of this design is that when the chest respiratory wave is not periodic, the other three patches can be used to determine whether a body movement or a synchronized irregular respiratory event is currently occurring.
The abdomen is provided with a patch which is mainly used for monitoring respiratory problems caused by sleep obstruction. The respiratory gold standard is oronasal airflow, typically accompanied by periodic thoracic movement. However, in some patients with respiratory obstruction, while breathing (chest relief) is attempted, the airflow is small, resulting in reduced blood oxygen. One method of distinguishing respiratory obstruction is to measure chest and abdomen movements, such as synchronous expansion of the chest and abdomen, and normal respiration, such as expansion of the chest and contraction of the abdomen, and respiratory obstruction. Because the abdomen patch is only used for distinguishing whether breathing is blocked or not, the abdomen patch is not required to be closely attached to the skin, and no resistance is generated to breathing. Abdomen measurement also has an advantage. Namely, large blood vessels exist in the abdomen, ribs are not blocked, and when the blood vessels beat, vibration perpendicular to the abdomen exists, so that the vibration can complement heart vibration transmitted by the body, and false alarms are reduced. The amplitude of the vascular pulsation is larger when the vascular pulsation is slightly compressed (referring to an upper arm sphygmomanometer, when the external pressure reaches the average blood pressure, the vascular pulsation amplitude is maximum), so that even in prone position, whether the risk of sudden cardiac arrest exists can be distinguished, and the false alarm rate of a single chest patch is greatly reduced.
The information acquisition step specifically comprises the following steps: the respiration rate of each patch is measured independently because the sleep respiration component is concentrated at 0.13-0.66Hz, i.e. 7.8-39.6bpm, and signals outside the frequency band are considered noise. Each patch is used for independently measuring the heart rate, and the heart rate of the child is faster than that of an adult, so that the heart rate window is set to be 0.8-3.5Hz, namely 48-210bpm, the heart rate window can be individually adjusted according to the age of a user, and because the body elastic vibration signal caused by each heart beat is between 4 and 11Hz, the external interference is eliminated by the band-pass filtering of 4-11Hz before the heart rate is calculated, and then the second band-pass filtering is carried out through the heart rate window (0.8-3.5 Hz), so that the heart rate signal with high stability can be effectively obtained
The processing steps of the respiratory signal of each triaxial acceleration sensor specifically include: during the filtering process, the reserved signal is between 0.13 and 0.66Hz, namely between 7.8 and 39.6bpm; the window of the fourier transform is 30s one window. Whether the respiratory wave exists or not is judged by whether a significant peak exists or not. The intensity of the overall signal is judged by whether the maximum amplitude is less than 25 mg.
The heart rate signal processing step of each triaxial acceleration sensor specifically comprises the following steps: in the filtering process, the 4-11Hz signals are separated in the first step, the external interference is eliminated through 4-11Hz band-pass filtering, and the signals reserved in the second step are in 0.8-3.5Hz, namely 48-210bpm. The window of the fourier transform is 10s one window. Whether a heart rate cycle exists is judged by whether a significant peak exists. The intensity of the overall signal is judged by whether the maximum amplitude is less than 2 mg. The periodicity of the heart beat calculation by energy density resolution is specifically: then an attempt is made to obtain the heart rate by means of the entropy of the information, the calculation formula of the entropy being:
calculating entropy in fixed time through a sliding window, drawing an entropy fluctuation curve, and evaluating the periodicity of the entropy fluctuation curve, if obvious periodicity can be found, obtaining the heart rate in an entropy mode.
Comprehensively judging the sleeping situation further comprises the following steps: the four-patch respiration and heart rate data integration step comprises the following steps: the heart rate and the breath output by each sensor have certain phase difference, and because of different data quality, the errors of breath and heart rate extraction are also different, and in order to reduce the measurement error to the maximum extent, kalman filtering and data quality weight modes are adopted for integration.
By comparison with gold standard, we can pre-judge the data quality, i.e. how much the error of a single measurement is in the case of certain noise and spectral spread. The larger the error, the more unreliable the data, and the lower the weight. Since breathing may be abrupt (e.g. suddenly reduced to 0, breath hold), kalman filtering based on time series is not applicable and the final measurement of the breath is weighted by four sensors.
Since the heart rate amplitude is weaker and the variation amplitude is limited, a Kalman filtering step can be added. In the time series, the heart rate data output by each sensor is combined from the previous data and the change trend, and the change trend generally accords with Gaussian distribution, namely, the heart rate cannot be greatly suddenly changed. For example, if the previous data (t-1) is abnormally reliable, and the current data (t) is less reliable, the method can be used for processing the data
HR(t-1)+k t *(HR m (t)-HR m (t-1)) (2)
To predict the current heart rate and to match the HR m (t) comparing to reduce measurement error, wherein HR (t-1) is the heart rate value (heart rate) of the previous measurement, HR m (t) is the heart rate value of the current independent measurement, and the subscript m represents measurement, meaning measurement. k (k) t The calculation mode of (2) is as follows: let P (t|t-1) represent the previous data as HR m (t-1) and the current data is HR m Probability of (t), r t Represents the current HR m Reliability of measurement, k t ＝P(t|t-1)/[P(t|t-1)+r t ]。r t The calculation mode of (2) is the ratio of the spectrum energy occupied by the peak with the highest amplitude after Fourier transformation to the whole spectrum energy. Heart rate final measurement data is weighted by four Kalman filtered heart rates.
Comprehensively judging sleep apnea events with little harm in the sleep condition, reminding parents through mobile phones, slowing down through intervention measures such as turning over and the like, and providing relevant data for the parents to assist in judging whether medical intervention is needed.
This application is through intelligent sleep paster, can low cost and unbound monitoring children sleep, does not receive the environment interference, uses comfortablely. Has better resolution capability for apnea, tachypnea and respiratory obstruction; early warning is carried out on life-threatening events, and general negative events are reminded and recorded; the sleeping posture is well resolved (left and right side positions, prone position and the like), and false alarms are low (means such as radar waves and the like); the heart rate and pulse intensity can be measured well, and the measurement can be corrected automatically through sleeping posture; and acquiring sleep quality related data through analysis of body movement, respiration and heart rate. Due to the light weight of the child, the technical mode of using a pressure sensor will result in a signal to noise ratio that is too low, but the acceleration sensor measurement is not affected.
The application also relates to a child sleep monitoring system for implementing the child sleep monitoring method.
The application also relates to a child sleep monitoring device for implementing the child sleep monitoring method. The child sleep monitoring device comprises four patches, a processor and a memory, wherein the memory is in communication connection with the processor, the memory stores instructions which can be executed by the processor and stores breathing and heart rate information acquired by the four patches, and the instructions are executed by the processor to perform the child sleep monitoring method.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, it is possible to make several modifications and improvements without departing from the concept of the present invention, which are equivalent to the above embodiments according to the essential technology of the present invention, and these are all included in the protection scope of the present invention.
Claims (10)
1. A method for monitoring sleep of a child, comprising the steps of:
and (3) installing a triaxial acceleration sensor: the method comprises the steps that 4 triaxial acceleration sensors are respectively arranged outside clothes of two upper arms, a chest and an abdomen;
collecting information: each of the three-axis acceleration sensors acquires respiration and heart rate signals:
processing the respiration signal of each triaxial acceleration sensor: filtering the respiratory signals of each triaxial acceleration sensor, carrying out Fourier transform on the filtered signals, acquiring respiratory waveforms through meyer wavelet transform and recording when respiratory waves exist in the transformed signals, and judging whether the signals are in a pressed state, a respiratory disorder or a body movement state through the strength and comparison of the total signals when the respiratory waves do not exist in the transformed signals;
processing heart rate signals of each triaxial acceleration sensor: filtering heart rate of each triaxial acceleration sensor, carrying out Fourier transform on the filtered signals, extracting periodic heart rate when heart rate period exists in the transformed signals, and judging whether the signals are in a pressed state or the heart rate is resolved through energy density to calculate the periodicity through strength and contrast of the overall signals when the heart rate period does not exist in the transformed signals;
comprehensively judging sleeping conditions: discarding the breathing signals and heart rate signals of the four triaxial acceleration sensors in the pressed state, comprehensively analyzing the residual signals, judging that the child is in an irregular body movement state, a breathing obstruction or a normal state, respectively carrying out early warning on the irregular body movement state and the breathing obstruction, and recording the normal state.
2. The method for monitoring sleep of a child according to claim 1, wherein: in the step of comprehensively judging the sleeping condition, the respiratory obstruction is judged by a triaxial acceleration sensor of the chest and the abdomen, and the respiratory obstruction is judged when the chest expands and the abdomen contracts.
3. The method for monitoring sleep of a child according to claim 1, wherein: and in the step of processing the heart rate signals of each triaxial acceleration sensor, judging whether a heart rate period exists or not according to whether a significant peak exists in the signals after frequency domain transformation.
4. A method of monitoring sleep in a child according to claim 3, wherein: in the step of processing the heart rate signal of each triaxial acceleration sensor, if the frequency domain transformation fails to find a significant peak value, heart rate fitting is tried through energy density, and periodicity is calculated through energy density resolution heart beat specifically as follows: attempting to obtain the heart rate by means of information entropy, wherein the calculation formula of the entropy is as follows:whereinI e For the signal strength at a certain moment in time,p i to appear to occurI e Is a function of the probability of (1),H s for the entropy value of a specific time interval, calculating the entropy value in a fixed time through a sliding window, drawing an entropy fluctuation curve, and evaluating the periodicity of the entropy fluctuation curve in a mode of searching for a significant peak by adopting frequency domain transformation, if the periodicity is obvious, obtaining the heart rate in an entropy mode.
5. The method for monitoring sleep of a child according to claim 1, wherein: in the step of comprehensively judging the sleeping condition, the recording of the normal state is specifically as follows: the final respiratory signal is obtained by data weighting of four triaxial acceleration sensors, wherein the data weight is a signal quality index and is calculated by the proportion of the frequency domain peak to the whole frequency domain energy.
6. The method for monitoring sleep of a child according to claim 1, wherein: in the step of comprehensively judging the sleeping condition, the recording of the normal state is specifically as follows: and finally weighting the data of the four triaxial acceleration sensors of the heart rate signal after Kalman filtering.
7. The method for monitoring sleep of a child according to claim 1, wherein: in the step of comprehensively judging the sleeping situation, when the child is in a respiratory obstruction state, reminding the child of changing the body position when respiratory obstruction is frequent but the duration time is short; early warning is given when respiratory obstruction continues unrelieved and heart rate decreases.
8. The method for monitoring sleep of a child according to claim 1, wherein: in the process of filtering the respiratory signals of each triaxial acceleration sensor, the reserved signals are in the range of 0.13-0.66Hz, namely 7.8-39.6bpm; in the process of filtering the heart rate signal of each triaxial acceleration sensor, the first step separates 4-11Hz signals, and the second step retains signals at 0.8-3.5Hz, namely 48-210bpm.
9. A child sleep monitoring system, characterized by: the child sleep monitoring system for implementing the child sleep monitoring method of any one of claims 1-8.
10. A child sleep monitoring arrangement, characterized by: comprising
Four triaxial acceleration sensors which are respectively arranged outside clothes of two upper arms, the chest and the abdomen of the child;
a processor;
a memory communicatively coupled to the processor;
the memory stores data collected by the four tri-axial acceleration sensors and instructions executable by the processor to implement the method of child sleep monitoring of any one of claims 1-8.
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