CN113974576B - Sleep quality monitoring system and monitoring method based on magnetocardiogram - Google Patents

Sleep quality monitoring system and monitoring method based on magnetocardiogram Download PDF

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CN113974576B
CN113974576B CN202111589528.3A CN202111589528A CN113974576B CN 113974576 B CN113974576 B CN 113974576B CN 202111589528 A CN202111589528 A CN 202111589528A CN 113974576 B CN113974576 B CN 113974576B
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magnetocardiogram
signals
blood oxygen
characteristic
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CN113974576A (en
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宁晓琳
贾一凡
高阳
向岷
梁晓钰
吴焕琦
马宇宇
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Hangzhou Innovation Research Institute of Beihang University
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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
    • A61B5/48—Other medical applications
    • A61B5/4806—Sleep evaluation
    • A61B5/4815—Sleep quality
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring blood gases
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/242—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents
    • A61B5/243—Detecting biomagnetic fields, e.g. magnetic fields produced by bioelectric currents specially adapted for magnetocardiographic [MCG] signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems

Abstract

The invention relates to a sleep quality monitoring system and method based on magnetocardiogram, the monitoring method comprises: s10, acquiring magnetocardiogram signals and blood oxygen signals of the sleep time of the target user by means of a monitoring system; s20, preprocessing and denoising the magnetocardiogram signals, screening available magnetocardiogram signal time periods and obtaining intermediate magnetocardiogram signals; s30, extracting the middle magnetocardiogram signal by adopting a QRS wave detection method and a cardiopulmonary coupling algorithm, and acquiring a characteristic signal for reflecting the sleep quality of a target user; s40, preprocessing the blood oxygen signal, extracting the characteristic value, and screening a time period capable of reflecting the respiratory event of the target user by adopting a pre-trained machine learning model; and S50, acquiring a sleep quality evaluation result of the target user based on the characteristic value and the characteristic signal of the blood oxygen signal in the screened time period. The method can monitor the respiratory events in a time sequence manner, and improves the detection accuracy and the reliability.

Description

Sleep quality monitoring system and monitoring method based on magnetocardiogram
Technical Field
The invention relates to the technical field of biomedicine, in particular to a sleep quality monitoring system and a sleep quality monitoring method based on magnetocardiogram.
Background
Sleep Disordered Breathing (SDB) is a disease which is mainly characterized by snoring in sleep at night and sleepiness in daytime, and as a patient can repeatedly stop breathing during the snoring, the patient can repeatedly arouse the cerebral cortex, the oxygen content in blood is reduced, and the important organs of the brain, the heart and the like are subjected to chronic hypoxia. When the human body is chronically anoxic for a long time, the attack of hypertension, arrhythmia, myocardial infarction and angina pectoris can be induced, and more seriously, sudden death can be caused. If the Sleep Disordered Breathing (SDB) of the children cannot be diagnosed and effectively intervened in time, a series of serious complications can be caused, such as maxillofacial dysplasia (adenoid face-face appearance), abnormal behaviors, learning disorder, backward growth and development, neurocognitive injury, endocrine and metabolic disorders, hypertension and pulmonary hypertension, and even the risk of cardiovascular events in adult stages is increased.
In the sleep quality monitoring process, in order to ensure the low-load sleep process, the respiratory signal is not directly acquired generally, and other signals capable of reflecting the sleep state are used for replacing, such as electrocardiosignals, blood oxygen signals, body position information measured by an accelerometer and the like. The prior art provides a method which adopts an accelerometer to monitor the movement of a user, judges the sleeping depth according to the micro movement of the user, and approximately records the time of falling asleep and waking of the user. Due to the problem of diagnosis precision, all the sensors cannot be compared with the gold standard PSG (sleep monitor guidance), and especially for sleep monitor of children Sleep Disordered Breathing (SDB), all portable devices cannot meet the diagnosis standard.
The PSG with the gold standard has high requirements on technicians, is complex to operate, is expensive, and cannot be popularized and used, so that a sleep quality monitoring system with low cost and simple and convenient operation needs to be provided, and the sleep state of a user can be monitored in real time, and the sleep quality of the user can be scored.
Disclosure of Invention
Technical problem to be solved
In view of the above disadvantages and shortcomings of the prior art, the present invention provides a sleep quality monitoring system and method based on magnetocardiogram.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
in a first aspect, an embodiment of the present invention provides a sleep quality monitoring method based on a magnetocardiogram, including:
s10, acquiring magnetocardiogram signals and blood oxygen signals of the sleep time of the target user by means of a monitoring system;
s20, preprocessing and denoising the magnetocardiogram signals, screening available magnetocardiogram signal time periods and obtaining intermediate magnetocardiogram signals;
s30, extracting the middle magnetocardiogram signal by adopting a QRS wave detection method and a cardiopulmonary coupling algorithm, and acquiring a characteristic signal for reflecting the sleep quality of a target user;
s40, preprocessing the blood oxygen signal, extracting a characteristic value, and screening a time period capable of reflecting the respiratory event of the target user based on the characteristic value by adopting a pre-trained machine learning model;
and S50, acquiring a sleep quality evaluation result of the target user based on the characteristic value of the blood oxygen signal in the screened time period and the characteristic signal in the screened time period.
Optionally, the method further comprises:
s60, acquiring sound signals and/or image signals of the sleeping time of the target user by means of a monitoring system;
processing the sound signals by adopting another machine learning model trained in advance, screening effective sound signals, and pairing time periods corresponding to the effective sound signals with the time periods of the characteristic signals/the screened time periods;
if the time is overlapped, analyzing the characteristic signal, the blood oxygen signal and the sound signal of the overlapped time period to obtain a sleep quality evaluation result of the target user;
or analyzing the characteristic signals, the blood oxygen signals, the sound signals and the image signals of the overlapped time periods to obtain the sleep quality evaluation result of the target user.
Optionally, the S20 includes:
and optimizing the magnetocardiogram signals by adopting the quality factors of the magnetocardiogram signals, and performing noise reduction processing on the optimized magnetocardiogram signals by using a soft thresholding processing method based on empirical mode decomposition to obtain intermediate magnetocardiogram signals.
Optionally, the S20 includes:
s21, segmenting the magnetocardiogram signals based on the sliding window of 1 minute, adopting 'harr' wavelet basis to carry out 7-layer wavelet packet decomposition on the magnetocardiogram signals in each window, and carrying out 7-layer wavelet packet decomposition on the magnetocardiogram signals with the sampling rate of 256Hz
Figure 699440DEST_PATH_IMAGE001
Decomposing in 6 sub-bands, calculating
Figure 714801DEST_PATH_IMAGE002
Layer 7 in minutes
Figure 153872DEST_PATH_IMAGE003
The proportion of wavelet in the energy of the layer
Figure 430133DEST_PATH_IMAGE004
，
Wherein the content of the first and second substances,
Figure 804614DEST_PATH_IMAGE005
is a magnetocardiogram signal
Figure 166325DEST_PATH_IMAGE006
Within minute is
Figure 284454DEST_PATH_IMAGE007
The energy of the individual subspaces;
Figure 946379DEST_PATH_IMAGE008
Figure 350816DEST_PATH_IMAGE009
is the largest integer that is less than the number of sample points and that can be divided exactly by 256,
Figure 343698DEST_PATH_IMAGE010
；
s22, extracting magnetocardiogram signals respectively
Figure 124572DEST_PATH_IMAGE011
Wavelet energy ratio of 6 sub-bands in minutes
Figure 516370DEST_PATH_IMAGE012
，
Figure 357288DEST_PATH_IMAGE013
Entropy of energy
Figure 303378DEST_PATH_IMAGE014
And kurtosis of magnetocardiogram signals
Figure 887943DEST_PATH_IMAGE015
A total of 13 eigenvalues are used as quality factors for evaluating the quality of magnetocardiogram signals,
Figure 134248DEST_PATH_IMAGE016
is as follows
Figure 146066DEST_PATH_IMAGE017
The average of the sampled points in minutes,
Figure 969666DEST_PATH_IMAGE018
is as follows
Figure 967709DEST_PATH_IMAGE019
Standard deviation of sampling points in minutes;
s23, inputting the extracted 13 quality factors into a trained support vector machine to sequentially judge the quality of the magnetocardiogram signals per minute, and screening time periods for sleep quality evaluation to obtain quality labels of the magnetocardiogram signals
Figure 193154DEST_PATH_IMAGE020
And quality optimized magnetocardiogram signal
Figure 251239DEST_PATH_IMAGE021
；
S24, decomposing the quality-optimized magnetocardiogram signals into a plurality of intrinsic mode functions IMFs in a self-adaptive manner based on signal local characteristics by using an EMD method, using multiples of a Donoho threshold as a threshold, soft-thresholding the obtained IMFs, superposing and reconstructing the soft-thresholded IMFs, removing noise of the magnetocardiogram signals, and obtaining intermediate magnetocardiogram signals
Figure 296556DEST_PATH_IMAGE022
。
Optionally, the S30 includes:
s31, performing square operation on each item of the middle magnetocardiogram signal, increasing the intensity value and increasing the high-frequency component to obtain a first signal
Figure 222924DEST_PATH_IMAGE023
For the first signal
Figure 178241DEST_PATH_IMAGE024
Carrying out three-time moving average filtering to respectively extract QRS characteristics
Figure 531862DEST_PATH_IMAGE025
QRS threshold
Figure 205420DEST_PATH_IMAGE026
And baseline noise level in the signal
Figure 669899DEST_PATH_IMAGE027
(ii) a Calculating a QRS threshold that includes an offset,
Figure 604357DEST_PATH_IMAGE028
；
s32, obtaining the positions of all points with QRS characteristics larger than the QRS threshold value according to the threshold value method, marking the positions of the points with 0 and 1,
Figure 1316DEST_PATH_IMAGE029
；
the duration of the QRS characteristic at the QRS wave position being larger than the threshold value is larger than
Figure 286804DEST_PATH_IMAGE030
Removing T wave, P wave and noise as criterion to obtain position function representing QRS waveT（n）；
Figure 554974DEST_PATH_IMAGE031
；
S33, extracting RR interval sequence of intermediate signal according to extracted QRS wave
Figure 16043DEST_PATH_IMAGE032
And respiratory signals
Figure 321253DEST_PATH_IMAGE033
；
S34, using cubic spline interpolation method to sequence RR intervals
Figure 94037DEST_PATH_IMAGE032
And respiratory signals
Figure 41265DEST_PATH_IMAGE033
Resampling to 4Hz, then segmenting the resampled respiratory signal using a 3min sliding window with an overlap rate of 25%, calculating the cardiopulmonary coupling strength in each window
Figure 684735DEST_PATH_IMAGE034
；
Extracting the cardiorespiratory coupling strength in each window
Figure 816640DEST_PATH_IMAGE034
The normalized low-frequency power LFC-N and the ratio LVHC of the low-frequency power to the high-frequency power of LFC-N are used as the characteristic signal of the magnetocardiogram signal.
Optionally, the S40 includes:
s41, preprocessing the blood oxygen signal and extracting a characteristic value;
specifically, removing the artifact of the blood oxygen signal, resampling the signal to 25Hz, and keeping two decimal places;
segmenting the signal by using a 3min sliding window with the overlapping rate of 25%, extracting a characteristic value of the blood oxygen signal, and extracting the ODI3 of the blood oxygen signal and the frequency spectrum skewness of the interested frequency band as the characteristic value capable of reflecting the respiratory event;
and S42, inputting the extracted characteristic values into a machine learning model trained in advance, and screening time periods capable of reflecting the respiratory events.
Optionally, S50 includes:
quality label based on magnetocardiogram signals
Figure 420927DEST_PATH_IMAGE020
Judging whether the characteristic signals in the time period accord with a preset available standard or not, and if so, acquiring a sleep quality evaluation result of the target user according to the characteristic value of the blood oxygen signals in the screened time period and the characteristic signals in the screened time period;
otherwise, acquiring a sleep quality evaluation result of the target user according to the characteristic value of the blood oxygen signal of the screened time period.
Optionally, the S60 includes:
respectively denoising the sound signal and the image signal by using a Butterworth filter and dividing the signals by using a 3min sliding window with an overlapping rate of 25%;
for the image signal of each segmented window, extracting dynamic features by using a convolutional neural network;
and for the sound signals of each divided window, judging whether the period of time exceeds the sound signals of the threshold value by using a fully-connected neural network.
Optionally, the S23 includes:
s23-1, initializationi=1First, the quality of the 1 st minute signal is evaluated;
s23-2, the first stepiInputting 13 feature values of minutes into the trained support vector machine model, and outputting the result asqu i =1Orqu i =0Signal quality label
Figure 296479DEST_PATH_IMAGE035
If outputqu i =1Then represents the firstiThe minute signal is available, execute S23-3; if outputqu i =0Then represents the firstiThe signal of minute is not available, the signal quality of the next minute is continuously judged until the available signal of the first section is found, and the order is giveni=i+1Repeating S23-2;
s23-3, ifiIf minute signal is available, determining the first available signal, assigning the value of the first available signal to each unavailable signal, and optimizing magnetocardiogram signal
Figure 794457DEST_PATH_IMAGE036
，
Wherein
Figure 972628DEST_PATH_IMAGE037
Continuing to judge the next section of signal, order
Figure 188846DEST_PATH_IMAGE038
；
S23-4, the first step
Figure 477876DEST_PATH_IMAGE039
Inputting 13 characteristic values of minutes into the trained support vector machine model to obtain
Figure 95939DEST_PATH_IMAGE040
Value of (2), signal quality label
Figure 569646DEST_PATH_IMAGE041
(ii) a If it is
Figure 148526DEST_PATH_IMAGE042
Then represents the first
Figure 365881DEST_PATH_IMAGE043
The minute signal can be used, the optimized magnetocardiogram signal is the same as the original signal,
Figure 838450DEST_PATH_IMAGE044
(ii) a If it is
Figure 349635DEST_PATH_IMAGE042
Then represents the firstiMinute signal is not available, willi-1Assigning the minute signal to the current signal, and optimizing the magnetocardiogram signal
Figure 540445DEST_PATH_IMAGE045
(ii) a To obtain the first
Figure 171278DEST_PATH_IMAGE039
After minute optimization of the magnetocardiogram signal, the next section of signal is optimized continuously to order
Figure 763933DEST_PATH_IMAGE046
Repeating S23-4; when in use
Figure 313863DEST_PATH_IMAGE047
When so, the cycle terminates; obtaining a quality label for a signal
Figure 867336DEST_PATH_IMAGE020
And quality optimized magnetocardiogram signal
Figure 426493DEST_PATH_IMAGE048
；
Alternatively, the S24 includes:
inputting the quality-optimized magnetocardiogram signal
Figure 873655DEST_PATH_IMAGE049
Performing EMD decomposition and initializing iteration times
Figure 735432DEST_PATH_IMAGE050
And residual terms
Figure 900834DEST_PATH_IMAGE051
And c represents the number of iterative decompositions,
Figure 607890DEST_PATH_IMAGE052
residual term determination before starting of the c-th decomposition
Figure 440716DEST_PATH_IMAGE053
All poles, all maximum points of
Figure 332449DEST_PATH_IMAGE054
Upper envelope curve of
Figure 860514DEST_PATH_IMAGE055
All minimum points forming the lower envelope
Figure 495894DEST_PATH_IMAGE056
The mean line of the signal is obtained according to the upper and lower envelope lines
Figure 58594DEST_PATH_IMAGE057
(ii) a Subtracting the envelope mean line from the residual term
Figure 121228DEST_PATH_IMAGE058
(ii) a If it is
Figure 261222DEST_PATH_IMAGE059
The zero point and the extreme point have equal number or differ by at most 1, and the average value of the envelope curve formed by the extreme points at any time is zero, then
Figure 310081DEST_PATH_IMAGE060
As an IMF value to
Figure 851920DEST_PATH_IMAGE061
And outputting and updating the number of iterations
Figure 85456DEST_PATH_IMAGE062
Up to
Figure 585182DEST_PATH_IMAGE063
Satisfying the criterion of iteration termination, outputting residual terms
Figure 562366DEST_PATH_IMAGE064
Completing EMD decomposition;
after EMD decomposition
Figure 834078DEST_PATH_IMAGE065
Wherein
Figure 238515DEST_PATH_IMAGE066
The intrinsic mode component IMF is represented,
Figure 353101DEST_PATH_IMAGE067
representing a residual term or a trend term;
using a multiple of the Donoho threshold as the threshold:
Figure 9342DEST_PATH_IMAGE068
where C is a constant and N is the data length; different IMFs have different energies, different scale factors C are selected for the different IMFs, and 0.6-1.2 are sequentially selected;
each intrinsic mode is subjected to soft thresholding, and the w mode after the soft thresholding
Figure 260194DEST_PATH_IMAGE069
Overlapping and reconstructing the IMF subjected to soft thresholding to obtain a denoised magnetocardiogram signal
Figure 835532DEST_PATH_IMAGE070
。
In a second aspect, an embodiment of the present invention further provides a sleep quality monitoring system based on magnetocardiogram, which includes:
a magnetocardiogram monitoring structure for acquiring magnetocardiogram signals;
a blood oxygen collecting component for collecting blood oxygen signals;
the signal processing device is connected with the magnetocardiogram monitoring structure and the blood oxygen acquisition assembly;
the magnetocardiogram monitoring structure comprises: the magnetocardiogram acquisition waistcoat is internally provided with a magnetocardiogram sensor and is worn on the upper body of a target user; the signal processing device executes the sleep quality monitoring method based on the magnetocardiogram according to any one of the first aspect;
alternatively, the first and second electrodes may be,
the sleep quality monitoring system comprises:
a magnetocardiogram monitoring structure for acquiring magnetocardiogram signals;
a blood oxygen collecting component for collecting blood oxygen signals;
a video acquisition component for acquiring image signals;
a sound collection assembly for collecting sound signals;
the signal processing device is connected with the magnetocardiogram monitoring structure and the blood oxygen acquisition assembly; the video acquisition assembly and the sound acquisition assembly are both connected with the signal processing device;
the signal processing device executes the sleep quality monitoring method based on the magnetocardiogram according to any one of the first aspect.
(III) advantageous effects
According to the method, the portable magnetocardiogram sensor is used for obtaining the magnetocardiogram signals of the target user sleeping all night, then the magnetocardiogram signals are optimized according to the quality factor of the magnetocardiogram signals, the magnetocardiogram signals with poor quality are marked, after the noise of the magnetocardiogram signals is removed, the long-time magnetocardiogram signal QRS wave detection method is adopted for carrying out QRS wave detection on the denoised magnetocardiogram signals, the QRS wave detection accuracy is improved, the respiratory events can be monitored in a time sequence, and the detection accuracy and the reliability are improved.
Further, in the method of the present invention, the trained machine learning model can be used to detect the sleep quality and respiratory events of each period of time in combination with the blood oxygen signal and the image signal, and then the sleep quality is scored and the sleep disorder is assisted to be evaluated.
In an automated signal processing process: the cardiopulmonary coupling is combined with the blood oxygen signal, the image signal and the sound signal, a trained machine learning model is used for detecting the sleep quality and the respiratory event in each period of time, and the sleep quality is scored and the sleep disorder is assisted to be evaluated on the basis of the detection, so that a sleep quality report is generated.
The signal acquisition is more targeted: the target user can select the required sensor according to the requirement, and the daily sleep quality monitoring only needs the magnetocardiogram sensor; if the user suspects that the user has the Sleep Disordered Breathing (SDB) or recommends further Sleep Disordered Breathing (SDB) examination after daily sleep quality monitoring, the user can add an oximeter to accurately detect whether the user has the Sleep Disordered Breathing (SDB) and the severity of the Sleep Disordered Breathing (SDB); if the user wants to record the detailed information of the user during the sleep period and carry out fine sleep monitoring, the image acquisition system can be continuously added to obtain a detailed sleep monitoring report.
Drawings
Fig. 1 is a schematic structural diagram of a sleep quality monitoring system based on a magnetocardiogram according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a sleep quality monitoring method of a sleep quality monitoring system based on a magnetocardiogram according to an embodiment;
fig. 3 is a schematic diagram of a sleep quality monitoring method of a sleep quality monitoring system based on a magnetocardiogram according to another embodiment.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
The magnetocardiogram signal contains more comprehensive physiological information than the electrocardio signal, and can reflect the physiological state of the heart. Compared with electrocardiogram, the magnetic signal is more stable than the electric signal, because the human tissue belongs to non-magnetic substance, the magnetic conductivity is approximately the same as that in vacuum, so the magnetic signal is not interfered by the medium of lung, chest wall, rib, etc., and the obtained result is more reliable. In addition, for the current with the same annular current and the same magnitude and the opposite direction, the electric effects are mutually counteracted and cannot be displayed on the electrocardiogram, but the obvious magnetic effect is generated and displayed on the electrocardiogram. Therefore, the study on sleep quality by using a magnetic field precision measurement method to measure magnetocardiogram instead of traditional electrocardio is a future development trend. In addition, the accuracy of monitoring the body movement in the sleep by using the accelerometer is greatly controversial, and the body movement and the snore in the sleep process can be better monitored by adopting image acquisition equipment comprising a camera and a microphone.
That is, the magnetocardiogram signal contains more comprehensive physiological information than the electrocardiographic signal, and can reflect the physiological state of the heart. Compared with electrocardiogram, the magnetic signal is more stable than the electric signal, because the human tissue belongs to non-magnetic substance, the magnetic conductivity is approximately the same as that in vacuum, so the magnetic signal is not interfered by the medium of lung, chest wall, rib, etc., and the obtained result is more reliable. In addition, for the current with the same annular current and the same magnitude and the opposite direction, the electric effects are mutually counteracted and cannot be displayed on the electrocardiogram, but the obvious magnetic effect is generated and displayed on the electrocardiogram. Combining magnetocardiogram signals with a CPC algorithm will improve the accuracy of the diagnosis of Sleep Disordered Breathing (SDB) in children.
Example one
As shown in fig. 1, the present embodiment provides a schematic structural diagram of a sleep quality monitoring system based on a magnetocardiogram, and the sleep quality monitoring system based on a magnetocardiogram of the present embodiment may include: a magnetocardiogram monitoring structure for acquiring magnetocardiogram signals; a blood oxygen collecting component for collecting blood oxygen signals; a signal processing device.
The signal processing device 6 (which may be a computer, for example) is connected to the magnetocardiogram monitoring structure and the blood oxygen collection assembly, and may perform the magnetocardiogram-based sleep quality monitoring method described below with reference to fig. 2 and 3.
The magnetocardiogram monitoring structure of the present embodiment includes: the magnetocardiogram acquisition waistcoat 1 is used for being worn on the upper body of a target user and internally provided with a magnetocardiogram sensor 2;
the magnetocardiogram acquisition waistcoat can be made of non-magnetic materials, has certain elasticity, is suitable for target users with different body types, can wrap a body during wearing, is not easy to displace, and is used as a magnetocardiogram acquisition channel for measuring magnetic signals generated by the heart during sleeping; the magnetocardiogram sensor fixing base is arranged on the magnetocardiogram acquisition waistcoat, and a user inserts the probe into the array fixing base 3 at different positions according to different heart positions and can select the number of the inserted sensors. That is to say, the magnetocardiogram sensor of this embodiment can be flexibly arranged based on the sensor array base, and is laid out above the heart of the subject, and a plurality of magnetocardiogram sensors can be installed, and the number of corresponding acquisition channels is selected according to the number of sensors.
The magnetocardiogram monitoring structure further comprises an analog output device and an analog-to-digital conversion acquisition card for acquiring magnetocardiogram signals and background magnetic noise, the magnetocardiogram signals and the background magnetic noise are amplified and filtered by the analog output device and then output to the analog-to-digital conversion acquisition card, and the converted digital signals are recorded in the signal processing device in real time. The analog output device and the analog-to-digital conversion acquisition card are sequentially connected with the signal processing device.
In other embodiments, the magnetocardiogram-based sleep quality monitoring system may further include: the video acquisition assembly is used for acquiring image signals, and the sound acquisition assembly is used for acquiring sound signals; at the moment, the video acquisition assembly and the sound acquisition assembly are both connected with the signal processing device; that is, in practical applications, a user may configure a sound collection component such as a microphone or a video collection component such as a CCD image sensor according to his or her needs. That is, the blood oxygen collecting component, the sound collecting component and the video collecting component are all components which can be selected by the user to add or not.
A user can select required components according to the requirement, and the daily sleep quality monitoring only needs a magnetocardiogram monitoring structure; if the user suspects that the user has the Sleep Disordered Breathing (SDB) or suggests to further carry out the Sleep Disordered Breathing (SDB) examination after daily sleep quality monitoring, the user can add a blood oxygen collecting component to accurately detect whether the user has the Sleep Disordered Breathing (SDB) and the severity of the Sleep Disordered Breathing (SDB); if the user wants to record the detailed information of the sleep period of the user and carry out fine sleep monitoring, the image and sound acquisition assembly for acquiring the image signal and the sound signal can be continuously added to obtain a detailed sleep monitoring report.
If only daily sleep quality monitoring is needed and only a magnetocardiogram sensor is needed, if a user suspects that the user has Sleep Disordered Breathing (SDB) or suggests to further perform Sleep Disordered Breathing (SDB) examination after the daily sleep quality monitoring, the user can add an oximeter 4 to accurately detect whether the user has the Sleep Disordered Breathing (SDB) and the severity of the Sleep Disordered Breathing (SDB); if the user wishes to record the detailed information of the sleep period of the user and perform the fine sleep monitoring, the image acquisition component 5 comprising a camera and a microphone can be continuously added to obtain a detailed sleep monitoring report.
Example two
As shown in fig. 2 and fig. 3, an embodiment of the present invention provides a sleep quality monitoring method based on magnetocardiogram, the execution subject of the method may be a signal processing device in a monitoring system, and specifically, the monitoring may include the following steps:
s10, acquiring magnetocardiogram signals and blood oxygen signals of the sleep time of the target user by means of a monitoring system;
s20, preprocessing and denoising the magnetocardiogram signals, screening available magnetocardiogram signal time periods and obtaining intermediate magnetocardiogram signals.
For example, the magnetocardiogram signal quality factor can be adopted to optimize the magnetocardiogram signal, the available magnetocardiogram signal time is screened, and the optimized magnetocardiogram signal is subjected to noise reduction processing based on the soft thresholding processing method of empirical mode decomposition to obtain the intermediate magnetocardiogram signal.
And S30, extracting the middle magnetocardiogram signal by adopting a QRS wave detection method and a cardiopulmonary coupling algorithm, and acquiring a characteristic signal for reflecting the sleep quality of the target user.
And S40, preprocessing the blood oxygen signal, extracting characteristic values, and screening by adopting a pre-trained machine learning model based on the extracted characteristic values to screen out a time period capable of reflecting the breathing events of the target user.
For example, the blood oxygen signal of each time segment may be preprocessed and feature value extracted; for example, removing artifacts of the blood oxygen signal, resampling the signal to 25Hz, reserving two decimal places, segmenting the signal by using a 3min sliding window with an overlapping rate of 25%, extracting a characteristic value of the blood oxygen signal, extracting ODI3 of the blood oxygen signal and a frequency spectrum skewness of an interested frequency band as characteristic values capable of reflecting respiratory events, inputting the extracted characteristic values into a pre-trained machine learning model, and screening time periods capable of reflecting respiratory events.
And S50, obtaining the sleep quality assessment result of the target user based on the characteristic value of the blood oxygen signal in the screened time period and the characteristic signal in the screened time period.
In the specific implementation process, the quality label of the magnetocardiogram signal can be obtained according to the following steps
Figure 312781DEST_PATH_IMAGE020
Judging whether the characteristic signal in the time period is in accordance with (namely, in accordance with the available standard, namely, the preset available standard), if so, acquiring the sleep quality evaluation result of the target user according to the characteristic value of the blood oxygen signal in the screened time period and the characteristic signal; otherwise, acquiring a sleep quality evaluation result of the target user according to the characteristic value of the blood oxygen signal in the screened time period.
In an alternative implementation, when the user selects the sound collection assembly and the image collection assembly, the method may further include the following step S60 not shown in the figure:
s60, acquiring sound signals and/or image signals of the sleeping time of the target user by means of a monitoring system;
processing the sound signals by adopting another machine learning model trained in advance, screening effective sound signals, and pairing time periods corresponding to the effective sound signals with the time periods of the characteristic signals/the screened time periods;
if the time is overlapped, analyzing the characteristic signal, the blood oxygen signal and the sound signal of the overlapped time period to obtain a sleep quality evaluation result of the target user;
or analyzing the characteristic signals, the blood oxygen signals, the sound signals and the image signals of the overlapped time periods to obtain the sleep quality evaluation result of the target user.
In this embodiment, the butterworth filters may be used to denoise the image signals separately and segment the signals with a 3min sliding window with an overlap rate of 25%; and then, extracting dynamic characteristics or dynamic change information such as turning over, body movement and the like by using a convolutional neural network for the image signal of each divided window, further effectively screening the image signal, and correspondingly, performing subsequent sleep quality evaluation results of the target user according to the effectively screened image signal.
The method comprises the steps of denoising sound signals by using a Butterworth filter, dividing the sound signals by using a 3min sliding window with the overlapping rate of 25%, judging whether the sound signals exceed a threshold value in the period of time such as snoring, dreaming and teeth grinding by using a fully-connected neural network for the sound signals of each divided window, and taking the sound signals exceeding the threshold value as effective sound signals for screening, thereby carrying out the subsequent sleep quality evaluation result of a target user according to the effectively screened sound signals or carrying out the subsequent sleep quality evaluation result of the target user by combining other blood oxygen signals and magnetocardiogram signals.
The method of the embodiment carries out sleep monitoring in all directions by means of the magnetocardiogram signal, the blood oxygen signal, the sound signal and the image signal, better analyzes the sleep quality of the target user, and obtains a sleep quality evaluation result with higher accuracy.
EXAMPLE III
The embodiment of the invention provides a sleep quality monitoring method based on a magnetocardiogram, which carries out detailed description on the processing of each signal.
The preprocessing and noise reduction processing on the magnetocardiogram signal in the step S20, and the screening of the available magnetocardiogram signal time period and the obtaining of the intermediate magnetocardiogram signal may include the following sub-steps:
the substeps S21,Segmenting the magnetocardiogram signals based on a sliding window of 1 minute, adopting a harr wavelet basis to carry out 7-layer wavelet packet decomposition on the magnetocardiogram signals in each window, and carrying out magnetocardiogram signals with a sampling rate of 256Hz
Figure 631767DEST_PATH_IMAGE071
Decomposing in 6 sub-bands (such as 0-1Hz, 1-5Hz, 5-15Hz, 15-50Hz, 15-100Hz and 100-120 Hz), and calculating the second sub-band
Figure 878072DEST_PATH_IMAGE002
Layer 7 in minutes
Figure 624311DEST_PATH_IMAGE003
The proportion of wavelet in the energy of the layer
Figure 713490DEST_PATH_IMAGE072
Wherein, in the step (A),
Figure 445953DEST_PATH_IMAGE073
is a magnetocardiogram signal
Figure 671398DEST_PATH_IMAGE006
Within minute is
Figure 995063DEST_PATH_IMAGE007
The energy of the individual subspaces;
Figure 40380DEST_PATH_IMAGE008
Figure 966748DEST_PATH_IMAGE009
is less than the number of sampling points and can be usedThe largest integer of the full division of 256,
Figure 922065DEST_PATH_IMAGE010
。
the substeps S22,Respectively extracting magnetocardiogram signals at
Figure 10107DEST_PATH_IMAGE011
Wavelet energy ratio of 6 sub-bands in minutes
Figure 808299DEST_PATH_IMAGE074
，
Figure 416653DEST_PATH_IMAGE075
Entropy of energy
Figure 351111DEST_PATH_IMAGE076
And kurtosis of magnetocardiogram signals
Figure 751000DEST_PATH_IMAGE077
A total of 13 eigenvalues are used as quality factors for evaluating the quality of magnetocardiogram signals,
Figure 770908DEST_PATH_IMAGE016
is as follows
Figure 39078DEST_PATH_IMAGE017
The average of the sampled points in minutes,
Figure 703409DEST_PATH_IMAGE018
is as follows
Figure 398833DEST_PATH_IMAGE019
Standard deviation of sampling points in minutes;
the substeps S23,Inputting the extracted 13 quality factors into a trained support vector machine to sequentially judge the quality of magnetocardiogram signals per minute, screening available magnetocardiogram signals, and obtaining a quality label of the magnetocardiogram signals
Figure 171617DEST_PATH_IMAGE020
And quality optimized magnetocardiogram signal
Figure 853265DEST_PATH_IMAGE021
。
In a specific implementation process, the sub-step S23 may include the following processes:
s23-1, initializationi=1First, the quality of the 1 st minute signal is evaluated;
s23-2, the first stepiInputting 13 feature values of minutes into the trained support vector machine model, and outputting the result asqu i =1Orqu i =0Signal quality label
Figure 762315DEST_PATH_IMAGE035
If outputqu i =1Then represents the firstiThe minute signal is available, execute S23-3; if outputqu i =0Then represents the firstiThe signal of minute is not available, the signal quality of the next minute is continuously judged until the available signal of the first section is found, and the order is giveni=i+1Repeating S23-2;
s23-3, ifiIf the minute signal is available, a first segment of available signal can be determined, the value of the segment of available signal is assigned to each segment of unavailable signal, so that the detection precision of QSR wave is not reduced due to sudden change of the optimized signal caused by baseline drift, the length of the signal is unchanged, the number of respiratory events per minute can be conveniently judged by subsequently combining the blood oxygen signal, and the optimized magnetocardiogram signal
Figure 504006DEST_PATH_IMAGE078
，
Wherein
Figure 498507DEST_PATH_IMAGE037
Continuing to judge the next section of signal, orderi =i+1；
S23-4, the first stepiInputting 13 characteristic values of minutes into the trained support vector machine model to obtain
Figure 108480DEST_PATH_IMAGE040
Value of (2), signal quality label
Figure 747403DEST_PATH_IMAGE079
(ii) a If it is
Figure 50208DEST_PATH_IMAGE042
Then represents the firstiThe minute signal can be used, the optimized magnetocardiogram signal is the same as the original signal,
Figure 266426DEST_PATH_IMAGE044
(ii) a If it is
Figure 555456DEST_PATH_IMAGE042
Then represents the firstiMinute signal is not available, willi-1 minute signal assignment to current signal, optimized magnetocardiogram signal
Figure 173519DEST_PATH_IMAGE080
(ii) a To obtain the firstiAfter the optimized magnetocardiogram signal is obtained in minutes, continuing optimizing the next section of signal, and repeating S23-4 with i = i + 1; when in use
Figure 522592DEST_PATH_IMAGE047
When so, the cycle terminates; obtaining a quality label for a signal
Figure 226106DEST_PATH_IMAGE020
And quality optimized magnetocardiogram signal
Figure 177881DEST_PATH_IMAGE048
；
The substeps S24,Adaptively decomposing the quality-optimized magnetocardiogram signal into a plurality of intrinsic mode functions IMFs (intrinsic mode functions) by using an EMD (empirical mode decomposition) method based on signal local characteristics, soft thresholding the IMFs by using multiples of a Donoho threshold as the threshold to obtain the IMFs, and superposing and reconstructing the soft thresholded IMFs to obtain the denoised magnetocardiogram signal
Figure 788466DEST_PATH_IMAGE022
。
For better understanding, this substep is explained in detail:
inputting the quality-optimized magnetocardiogram signal
Figure 433074DEST_PATH_IMAGE021
Performing EMD decomposition and initializing iteration times
Figure 623884DEST_PATH_IMAGE050
And residual terms
Figure 254717DEST_PATH_IMAGE081
And c represents the number of iterative decompositions,
Figure 581793DEST_PATH_IMAGE052
residual term determination before starting of the c-th decomposition
Figure 272668DEST_PATH_IMAGE053
All poles, all maximum points of
Figure 950774DEST_PATH_IMAGE054
Upper envelope curve of
Figure 509932DEST_PATH_IMAGE055
All minimum points forming the lower envelope
Figure 832460DEST_PATH_IMAGE082
The mean line of the signal is obtained according to the upper and lower envelope lines
Figure 553291DEST_PATH_IMAGE057
(ii) a Subtracting the envelope mean line from the residual term
Figure 718693DEST_PATH_IMAGE058
(ii) a If it is
Figure 691329DEST_PATH_IMAGE058
Number of zero and extreme pointsEqual or at most 1 difference and the mean value of the envelope lines formed by the extreme points at any time is zero, then
Figure 258576DEST_PATH_IMAGE060
As an IMF value to
Figure 25675DEST_PATH_IMAGE061
And outputting and updating the number of iterations
Figure 943952DEST_PATH_IMAGE062
Up to
Figure 579333DEST_PATH_IMAGE063
Satisfying the criterion of iteration termination, outputting residual terms
Figure 876453DEST_PATH_IMAGE064
Completing EMD decomposition;
after EMD decomposition
Figure 939087DEST_PATH_IMAGE065
Wherein
Figure 954448DEST_PATH_IMAGE066
The intrinsic mode component IMF is represented,
Figure 393519DEST_PATH_IMAGE063
representing a residual term or a trend term;
using a multiple of the Donoho threshold as the threshold:
Figure 669780DEST_PATH_IMAGE068
where C is a constant and N is the data length; different IMFs have different energies, different scale factors C are selected for the different IMFs, and 0.6-1.2 are sequentially selected;
each intrinsic mode is subjected to soft thresholding, and the w mode after the soft thresholding
Figure 58909DEST_PATH_IMAGE083
Overlapping and reconstructing the IMF subjected to soft thresholding to obtain a denoised magnetocardiogram signal
Figure 686199DEST_PATH_IMAGE070
。
In another possible implementation manner, the extracting the magnetocardiogram signal by using the QRS wave detection method and the cardiopulmonary coupling algorithm in step S30 to obtain the characteristic signal reflecting the respiratory event of the target user and the corresponding time period of the characteristic signal may include the following sub-steps:
the substeps S31,Performing square operation on each item of the intermediate magnetocardiogram signal (i.e. denoised magnetocardiogram signal) to increase the intensity and increase the high-frequency component to obtain a first signal
Figure 663383DEST_PATH_IMAGE023
，
Figure 935095DEST_PATH_IMAGE084
；
For the first signal
Figure 339532DEST_PATH_IMAGE023
Carrying out three-time moving average filtering to respectively extract QRS characteristics
Figure 595064DEST_PATH_IMAGE085
QRS threshold
Figure 375938DEST_PATH_IMAGE086
And baseline noise level in the signal
Figure 502157DEST_PATH_IMAGE027
(ii) a Calculating a QRS threshold that includes an offset,
Figure 343074DEST_PATH_IMAGE028
；
for example, cubic filtering is illustrated as follows:
for the first signal
Figure 820323DEST_PATH_IMAGE023
Performing moving average filtering, and optimizing the width of the filter
Figure 139309DEST_PATH_IMAGE030
25, filtered signal
Figure 651193DEST_PATH_IMAGE087
。
In order to adapt the detection of QRS waves to changes in heart rate, the heart rate is locally estimated by short-time Fourier transformation, and the filter width is estimated locally by these
Figure 663011DEST_PATH_IMAGE088
。
At time t
Figure 221031DEST_PATH_IMAGE089
Average value of (2)
Figure 750233DEST_PATH_IMAGE090
Wherein t represents any integer time,
Figure 710098DEST_PATH_IMAGE091
. Amplitude at frequency b at time t
Figure 768184DEST_PATH_IMAGE092
b is any integer frequency between 1 and 50. Maximum frequency at time t
Figure 79080DEST_PATH_IMAGE093
Figure 877884DEST_PATH_IMAGE094
Wherein
Figure 692256DEST_PATH_IMAGE095
To prevent large frequency jumps between successive time points. Heart rate estimate at time t
Figure 311457DEST_PATH_IMAGE096
And then calculating the heart rate estimated value at each sampling point by a proximity interpolation method
Figure 719435DEST_PATH_IMAGE097
Calculating the width of the filter
Figure 449494DEST_PATH_IMAGE098
。
For the first signal
Figure 259318DEST_PATH_IMAGE099
Performing moving average filtering with a filter width of
Figure 783840DEST_PATH_IMAGE100
Calculating QRS threshold
Figure 69328DEST_PATH_IMAGE101
。
For the first signal
Figure 212865DEST_PATH_IMAGE099
Moving average filtering to locally estimate baseline noise level in a signal
Figure 1829DEST_PATH_IMAGE102
。
Figure 838198DEST_PATH_IMAGE103
Wherein the width of the filter
Figure 79824DEST_PATH_IMAGE104
1281, offset factor
Figure 151685DEST_PATH_IMAGE105
. Calculating a QRS threshold that includes an offset,
Figure 936101DEST_PATH_IMAGE106
。
the substeps S32,Obtaining the positions of all points with QRS characteristics larger than the QRS threshold value according to a threshold value method, marking the positions of the points with 0 and 1,
Figure 802426DEST_PATH_IMAGE107
；
secondly, the calculation is centered on each sample point,
Figure 796927DEST_PATH_IMAGE030
is the sum of the labels of the bandwidths:
Figure 282266DEST_PATH_IMAGE108
then, whether a positive integer exists at each sample point n is judged one by one
Figure 311402DEST_PATH_IMAGE109
So that the following conditions are all true;
Figure 958415DEST_PATH_IMAGE110
if the QRS wave exists, the QRS wave is marked here, and the position function of the QRS wave is obtainedT（n）；
Figure 705791DEST_PATH_IMAGE111
。
That is, the duration of time that the QRS feature is greater than the threshold at the QRS wave is greater than
Figure 997751DEST_PATH_IMAGE030
Removing T wave, P wave and noise as criterion to obtain position function representing QRS waveT（n）。
The substeps S33,Extraction of RR interval sequence of intermediate signal from extracted QRS wave
Figure 615814DEST_PATH_IMAGE032
And respiratory signals
Figure 89521DEST_PATH_IMAGE033
；
Specifically, the abscissa corresponding to each QRS wave peak is the position of the R wave, and is recorded as
Figure 933980DEST_PATH_IMAGE112
，
Figure 620176DEST_PATH_IMAGE113
Is as follows
Figure 499270DEST_PATH_IMAGE114
Sampling points corresponding to the R waves
Figure 612720DEST_PATH_IMAGE114
Amplitude of a sequence of RR intervals
Figure 69109DEST_PATH_IMAGE115
RR interval sequence
Figure 699942DEST_PATH_IMAGE116
. Calculating respiratory signals by area mapping
Figure 558176DEST_PATH_IMAGE114
The area of the QRS wave is
Figure 983472DEST_PATH_IMAGE117
Respiratory signal
Figure 927158DEST_PATH_IMAGE118
。
The substeps S34,Interpolating the RR interval sequence by cubic spline
Figure 96102DEST_PATH_IMAGE032
And respiratory signals
Figure 808843DEST_PATH_IMAGE033
Resampling to 4Hz to obtain new RR interval sequence
Figure 670620DEST_PATH_IMAGE119
And respiratory signals
Figure 836022DEST_PATH_IMAGE120
(ii) a The resampled respiratory signal was then segmented using a 3min sliding window with an overlap rate of 25%, and the cardiopulmonary coupling strength in each window was calculated
Figure 933291DEST_PATH_IMAGE034
。
The heart-lung coupling strength reflects the synchronous degree of the heart rate and the respiratory cycle, and the respiratory signal is multiplied by the RR interval sequence coherence and the cross power spectrum square to obtain the heart-lung coupling strength of the heart-lung coupling strength
Figure 375905DEST_PATH_IMAGE034
. The method comprises the following specific steps:
first, RR interval sequence is calculated
Figure 267637DEST_PATH_IMAGE119
And respiratory signals
Figure 58351DEST_PATH_IMAGE120
Cross correlation cross power spectral density of
Figure 693732DEST_PATH_IMAGE121
Wherein
Figure 256432DEST_PATH_IMAGE122
，
Figure 584645DEST_PATH_IMAGE123
。
Then calculating RR interval sequence
Figure 600005DEST_PATH_IMAGE119
Coherence with respiratory signalRR interval sequence
Figure 39077DEST_PATH_IMAGE119
And respiratory signals
Figure 315337DEST_PATH_IMAGE120
Cross correlation cross power spectral density of
Figure 689818DEST_PATH_IMAGE124
Wherein
Figure 317108DEST_PATH_IMAGE125
. Cardio-pulmonary coupling strength of electrocardio signal and respiration signal
Figure 28713DEST_PATH_IMAGE126
。
The degree of synchronization between the heart rate and the respiratory cycle varies significantly with the sleep stage, so that the heart-lung coupling strength in each window can be determined according to the heart-lung coupling strength
Figure 566004DEST_PATH_IMAGE034
The current sleep state is determined. Cardiopulmonary coupling strength when sleep is stable
Figure 236020DEST_PATH_IMAGE034
The high frequency power (0.1-0.5 Hz for adults and 0.2-0.45 Hz for children) is large, and the coupling strength of the heart and lung is high when respiratory events occur
Figure 225973DEST_PATH_IMAGE034
The low frequency power (0.04-0.1 Hz for adults and 0.04-0.2 Hz for children) is larger. Extracting each window
Figure 741268DEST_PATH_IMAGE034
The normalized low-frequency power LFC-N (ratio of low-frequency power to total power) and the ratio LVHC of low-frequency power to high-frequency power of the magnetocardiogram signal are used as eigenvalues/signatures of the magnetocardiogram signal.
By processing the magnetocardiogram signals, the blood oxygen signals, the images and the sound signals, sleep stage information, respiratory event information and body movement and snoring information can be obtained and input into the trained fully-connected neural network for sleep quality monitoring and auxiliary diagnosis of Sleep Disordered Breathing (SDB) of children and adults.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third and the like are for convenience only and do not denote any order. These words are to be understood as part of the name of the component.
Furthermore, it should be noted that in the description of the present specification, the description of the term "one embodiment", "some embodiments", "examples", "specific examples" or "some examples", etc., means that a specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the claims should be construed to include preferred embodiments and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include such modifications and variations.

Claims (9)

1. A sleep quality monitoring method based on magnetocardiogram is characterized by comprising the following steps:
s10, acquiring magnetocardiogram signals and blood oxygen signals of the sleep time of the target user by means of a monitoring system;
s20, preprocessing and denoising the magnetocardiogram signals, screening available magnetocardiogram signal time periods and obtaining intermediate magnetocardiogram signals;
s30, extracting the middle magnetocardiogram signal by adopting a QRS wave detection method and a cardiopulmonary coupling algorithm, and acquiring a characteristic signal for reflecting the sleep quality of a target user;
wherein S30 includes:
s31, performing square operation on each item of the middle magnetocardiogram signal, increasing the intensity value and increasing the high-frequency component to obtain a first signal
Figure 786994DEST_PATH_IMAGE001
For the first signal
Figure 386603DEST_PATH_IMAGE002
Carrying out three-time moving average filtering to respectively extract QRS characteristics
Figure 259881DEST_PATH_IMAGE003
QRS threshold
Figure 577730DEST_PATH_IMAGE004
And baseline noise level in the signal
Figure 561866DEST_PATH_IMAGE005
(ii) a Calculating QRS thresholds including offsets
Figure 281561DEST_PATH_IMAGE006
；
S32, obtaining the positions of all points with QRS characteristics larger than the QRS threshold value according to the threshold value method, marking the positions of the points with 0 and 1,
Figure 325740DEST_PATH_IMAGE007
；
the duration of the QRS characteristic at the QRS wave position being larger than the threshold value is larger than
Figure 130885DEST_PATH_IMAGE008
Removing T wave, P wave and noise as criterion to obtain position function representing QRS waveT（n）；
Figure 918712DEST_PATH_IMAGE009
；
S33, extracting RR interval sequence of intermediate signal according to extracted QRS wave
Figure 991448DEST_PATH_IMAGE010
And respiratory signals
Figure 206529DEST_PATH_IMAGE011
；
S34, using cubic spline interpolation method to sequence RR intervals
Figure 967812DEST_PATH_IMAGE010
And respiratory signals
Figure 559330DEST_PATH_IMAGE011
Resampling to 4Hz, then segmenting the resampled respiratory signal using a 3min sliding window with an overlap rate of 25%, calculating the cardiopulmonary coupling strength in each window
Figure 988037DEST_PATH_IMAGE012
；
Extracting the cardiorespiratory coupling strength in each window
Figure 374019DEST_PATH_IMAGE012
The normalized low-frequency power LFC-N and the ratio LVHC of the low-frequency power and the high-frequency power are used as characteristic signals of the magnetocardiogram signals;
s40, preprocessing the blood oxygen signal, extracting a characteristic value, and screening a time period capable of reflecting the respiratory event of the target user based on the characteristic value by adopting a pre-trained machine learning model;
and S50, acquiring a sleep quality evaluation result of the target user based on the characteristic value of the blood oxygen signal in the screened time period and the characteristic signal in the screened time period.
2. The monitoring method of claim 1, further comprising:
s60, acquiring a sound signal and an image signal of the sleeping time of the target user by means of a monitoring system;
processing the sound signals by adopting another machine learning model trained in advance, screening effective sound signals, and pairing time periods corresponding to the effective sound signals with the time periods of the characteristic signals/the screened time periods;
if the time is overlapped, analyzing the characteristic signal, the blood oxygen signal and the sound signal of the overlapped time period to obtain a sleep quality evaluation result of the target user;
or analyzing the characteristic signals, the blood oxygen signals, the sound signals and the image signals of the overlapped time periods to obtain the sleep quality evaluation result of the target user.
3. The monitoring method according to claim 1, wherein the S20 includes:
and optimizing the magnetocardiogram signals by adopting the quality factors of the magnetocardiogram signals, and performing noise reduction processing on the optimized magnetocardiogram signals by using a soft thresholding processing method based on empirical mode decomposition to obtain intermediate magnetocardiogram signals.
4. The monitoring method according to claim 1, wherein the S20 includes:
s21, segmenting the magnetocardiogram signals based on the sliding window of 1 minute, performing wavelet packet decomposition on the magnetocardiogram signals by adopting 'harr' wavelet basis in each window, and dividing the magnetocardiogram signals with the sampling rate of 256Hz
Figure 153756DEST_PATH_IMAGE013
Decomposing in 6 sub-bands, calculating
Figure 283386DEST_PATH_IMAGE014
Within minute is
Figure 566600DEST_PATH_IMAGE015
The proportion of wavelet in the energy of the layer
Figure 389063DEST_PATH_IMAGE016
，
Wherein the content of the first and second substances,
Figure 390517DEST_PATH_IMAGE017
is a magnetocardiogram signal
Figure 323838DEST_PATH_IMAGE018
Within minute is
Figure 461558DEST_PATH_IMAGE019
The energy of the individual subspaces;
Figure 454922DEST_PATH_IMAGE020
Figure 943672DEST_PATH_IMAGE021
is the largest integer that is less than the number of sample points and that can be divided exactly by 256,
Figure 648061DEST_PATH_IMAGE022
；
s22, extracting magnetocardiogram signals respectively
Figure 905867DEST_PATH_IMAGE023
Wavelet energy ratio of 6 sub-bands in minutes
Figure 70132DEST_PATH_IMAGE024
，
Figure 780599DEST_PATH_IMAGE025
Entropy of energy
Figure 55722DEST_PATH_IMAGE026
And kurtosis of magnetocardiogram signals
Figure 433614DEST_PATH_IMAGE027
A total of 13 eigenvalues are used as quality factors for evaluating the quality of magnetocardiogram signals,
Figure 503201DEST_PATH_IMAGE028
is as follows
Figure 966543DEST_PATH_IMAGE029
The average of the sampled points in minutes,
Figure 779779DEST_PATH_IMAGE030
is as follows
Figure 12177DEST_PATH_IMAGE031
Standard deviation of sampling points in minutes;
s23, inputting the extracted 13 quality factors into a trained support vector machine to sequentially judge the quality of the magnetocardiogram signals per minute, and screening time periods for sleep quality evaluation to obtain quality labels of the magnetocardiogram signals
Figure 987086DEST_PATH_IMAGE032
And quality optimized magnetocardiogram signal
Figure 937725DEST_PATH_IMAGE033
；
S24, decomposing the quality-optimized magnetocardiogram signals into a plurality of intrinsic mode functions IMFs in a self-adaptive manner based on signal local characteristics by using an EMD method, using multiples of a Donoho threshold as a threshold, soft-thresholding the obtained IMFs, superposing and reconstructing the soft-thresholded IMFs, removing noise of the magnetocardiogram signals, and obtaining intermediate magnetocardiogram signals
Figure 554651DEST_PATH_IMAGE034
。
5. The monitoring method according to claim 1, wherein the S40 includes:
s41, preprocessing the blood oxygen signal and extracting a characteristic value;
specifically, removing the artifact of the blood oxygen signal, resampling the signal to 25Hz, and keeping two decimal places;
segmenting the signal by using a 3min sliding window with the overlapping rate of 25%, extracting a characteristic value of the blood oxygen signal, and extracting the ODI3 of the blood oxygen signal and the frequency spectrum skewness of the interested frequency band as the characteristic value capable of reflecting the respiratory event;
and S42, inputting the extracted characteristic values into a machine learning model trained in advance, and screening time periods capable of reflecting the respiratory events.
6. The monitoring method according to claim 4, wherein S50 includes:
quality label based on magnetocardiogram signals
Figure 641555DEST_PATH_IMAGE032
Judging whether the characteristic signal in the time period meets the preset available standard, if so, according to the characteristic value of the blood oxygen signal in the screening time period and the characteristic signal in the screening time periodAcquiring a sleep quality evaluation result of a target user;
otherwise, acquiring a sleep quality evaluation result of the target user according to the characteristic value of the blood oxygen signal of the screened time period.
7. The monitoring method according to claim 2, wherein the S60 includes:
respectively denoising the sound signal and the image signal by using a Butterworth filter and dividing the signals by using a 3min sliding window with an overlapping rate of 25%;
for the image signal of each segmented window, extracting dynamic features by using a convolutional neural network;
and for the sound signal of each divided window, judging whether the sound signal in the time is the sound signal exceeding a threshold value by using a fully connected neural network.
8. The monitoring method according to claim 4, wherein the S23 includes:
s23-1, initialization
Figure 817060DEST_PATH_IMAGE035
First, the quality of the 1 st minute signal is evaluated;
s23-2, the first step
Figure 989415DEST_PATH_IMAGE036
Inputting 13 feature values of minutes into the trained support vector machine model, and outputting the result as
Figure 144453DEST_PATH_IMAGE037
Signal quality label
Figure 351443DEST_PATH_IMAGE038
If output
Figure 933734DEST_PATH_IMAGE039
Then represents the first
Figure 593386DEST_PATH_IMAGE036
The minute signal is available, execute S23-3; if output
Figure 286535DEST_PATH_IMAGE040
Then represents the first
Figure 348032DEST_PATH_IMAGE036
The signal of minute is not available, the signal quality of the next minute is continuously judged until the available signal of the first section is found, and the order is given
Figure 366804DEST_PATH_IMAGE041
Repeating S23-2;
s23-3, if
Figure 513751DEST_PATH_IMAGE036
If minute signal is available, determining the first available signal, assigning the value of the first available signal to each unavailable signal, and optimizing magnetocardiogram signal
Figure 10592DEST_PATH_IMAGE042
，
Wherein
Figure 926595DEST_PATH_IMAGE043
Continuing to judge the next section of signal, order
Figure 116268DEST_PATH_IMAGE044
；
S23-4, the first step
Figure 750512DEST_PATH_IMAGE045
Inputting 13 characteristic values of minutes into the trained support vector machine model to obtain
Figure 51043DEST_PATH_IMAGE046
Value of, signalQuality label
Figure 320088DEST_PATH_IMAGE047
(ii) a If it is
Figure 415083DEST_PATH_IMAGE048
Then represents the first
Figure 536623DEST_PATH_IMAGE036
The minute signal can be used, the optimized magnetocardiogram signal is the same as the original signal,
Figure 375266DEST_PATH_IMAGE049
(ii) a If it is
Figure 265861DEST_PATH_IMAGE040
Then represents the first
Figure 797337DEST_PATH_IMAGE050
Minute signal is not available, will
Figure 140594DEST_PATH_IMAGE051
Assigning the minute signal to the current signal, and optimizing the magnetocardiogram signal
Figure 48507DEST_PATH_IMAGE052
(ii) a To obtain the first
Figure 793609DEST_PATH_IMAGE045
After minute optimization of the magnetocardiogram signal, the next section of signal is optimized continuously to order
Figure 495986DEST_PATH_IMAGE053
Repeating S23-4; when in use
Figure 326538DEST_PATH_IMAGE054
When so, the cycle terminates; obtaining a quality label for a signal
Figure 506984DEST_PATH_IMAGE032
And quality optimized magnetocardiogram signal
Figure 106593DEST_PATH_IMAGE055
；
Alternatively, the S24 includes:
inputting the quality-optimized magnetocardiogram signal
Figure 979871DEST_PATH_IMAGE056
Performing EMD decomposition and initializing iteration times
Figure 796255DEST_PATH_IMAGE057
And residual terms
Figure 780391DEST_PATH_IMAGE058
And c represents the number of iterative decompositions,
Figure 500086DEST_PATH_IMAGE059
residual term determination before starting of the c-th decomposition
Figure 544265DEST_PATH_IMAGE060
All poles, all maximum points of
Figure 349410DEST_PATH_IMAGE061
Upper envelope curve of
Figure 137237DEST_PATH_IMAGE062
All minimum points forming the lower envelope
Figure 711438DEST_PATH_IMAGE063
The mean line of the signal is obtained according to the upper and lower envelope lines
Figure 926519DEST_PATH_IMAGE064
(ii) a Subtracting the envelope mean line from the residual termTo
Figure 953381DEST_PATH_IMAGE065
(ii) a If it is
Figure 279320DEST_PATH_IMAGE066
The zero point and the extreme point have equal number or differ by at most 1, and the average value of the envelope curve formed by the extreme points at any time is zero, then
Figure 708027DEST_PATH_IMAGE067
As an IMF value to
Figure 94009DEST_PATH_IMAGE068
And outputting and updating the number of iterations
Figure 873746DEST_PATH_IMAGE069
Up to
Figure 3376DEST_PATH_IMAGE070
Satisfying the criterion of iteration termination, outputting residual terms
Figure 286590DEST_PATH_IMAGE071
Completing EMD decomposition;
after EMD decomposition
Figure 619306DEST_PATH_IMAGE072
Wherein
Figure 620761DEST_PATH_IMAGE073
The intrinsic mode component IMF is represented,
Figure 288502DEST_PATH_IMAGE074
representing a residual term or a trend term;
using a multiple of the Donoho threshold as the threshold:
Figure 691802DEST_PATH_IMAGE075
where C is a constant and N is the data length; different IMFs have different energies, different scale factors C are selected for the different IMFs, and 0.6-1.2 are sequentially selected;
each intrinsic mode is subjected to soft thresholding, and the w mode after the soft thresholding
Figure 685166DEST_PATH_IMAGE076
Overlapping and reconstructing the IMF subjected to soft thresholding to obtain a denoised magnetocardiogram signal
Figure 111599DEST_PATH_IMAGE077
。
9. A magnetocardiogram-based sleep quality monitoring system, comprising:
a magnetocardiogram monitoring structure for acquiring magnetocardiogram signals;
a blood oxygen collecting component for collecting blood oxygen signals;
a video acquisition component for acquiring image signals;
a sound collection assembly for collecting sound signals;
the signal processing device is connected with the magnetocardiogram monitoring structure and the blood oxygen acquisition assembly; the video acquisition assembly and the sound acquisition assembly are both connected with the signal processing device;
the magnetocardiogram monitoring structure comprises: the magnetocardiogram acquisition waistcoat is internally provided with a magnetocardiogram sensor and is worn on the upper body of a target user; the signal processing device executes the magnetocardiogram-based sleep quality monitoring method as set forth in any one of claims 1 to 8.
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