CN115486833B - Respiratory state detection method, respiratory state detection device, computer equipment and storage medium - Google Patents
Respiratory state detection method, respiratory state detection device, computer equipment and storage medium Download PDFInfo
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02405—Determining heart rate variability
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
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- A61B5/0816—Measuring devices for examining respiratory frequency
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1102—Ballistocardiography
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Abstract
The application relates to a respiration state detection method, a respiration state detection device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring a sign signal of a user under a preset first time scale; extracting a ballistocardiogram signal and a respiration signal from the physical sign signal; determining a first characteristic sequence corresponding to the ballistocardiogram signal and the respiratory signal; determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second characteristic sequence comprises a reference characteristic sequence, a fluctuation characteristic sequence, a most value characteristic sequence and a mode characteristic sequence; and inputting the second characteristic sequences under a plurality of different second time scales into a trained respiratory state detection model to obtain a respiratory state detection result, thereby reducing the cost and improving the accuracy of respiratory state detection.
Description
Technical Field
The present application relates to the field of breath detection, and in particular, to a breath state detection apparatus, computer device, and storage medium.
Background
Chronic obstructive pulmonary disease (Chronic Obstructive Pulmonary Disease, abbreviated as COPD) is a respiratory disease that can be prevented and treated, and is characterized by sustained airflow limitation, clinically manifested as symptoms of dyspnea, cough or expectoration of different degrees, and the like, which can accelerate disease progression without timely intervention, and seriously affects the quality of life of patients.
At present, early screening of COPD may be aided by the detection of the respiratory status of a patient. The existing respiration state detection method is short in monitoring time and is limited to daytime monitoring, so that the respiration state detection accuracy is low and the price is high.
Disclosure of Invention
Based on this, it is an object of the present application to provide a respiratory state detection, apparatus, computer device and storage medium, which can reduce costs, improve accuracy of respiratory state detection.
According to a first aspect of embodiments of the present application, there is provided a respiration state detection method, including the steps of:
acquiring a sign signal of a user under a preset first time scale;
extracting a ballistocardiogram signal and a respiration signal from the physical sign signal;
determining a first characteristic sequence corresponding to the ballistocardiogram signal and the respiratory signal; the first characteristic sequence comprises a heart rate characteristic sequence, a heart rate variability characteristic sequence, a respiratory rate characteristic sequence and a sleep characteristic sequence;
Determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second characteristic sequence comprises a reference characteristic sequence, a fluctuation characteristic sequence, a most value characteristic sequence and a mode characteristic sequence;
and inputting the second characteristic sequences under a plurality of different second time scales into a trained respiratory state detection model to obtain a respiratory state detection result.
According to a second aspect of embodiments of the present application, there is provided a respiratory state detection apparatus, including:
the sign signal acquisition module is used for acquiring sign signals of a user under a preset plurality of different time scales;
the signal extraction module is used for extracting a plurality of ballistocardiogram signals and respiratory signals under different time scales from the physical sign signals;
the first characteristic sequence determining module is used for determining first characteristic sequences corresponding to the ballistocardiogram signals and the respiratory signals under a plurality of different time scales; the first characteristic sequence comprises a heart rate characteristic sequence, a heart rate variability characteristic sequence, a respiratory rate characteristic sequence and a sleep characteristic sequence;
A second feature determining module, configured to determine second features of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence that correspond at a number of different time scales; the second features include a reference feature, a ripple feature, a most valued feature, and a mode feature;
the detection result obtaining module is used for inputting the second characteristics under a plurality of different time scales into the trained respiratory state detection model to obtain respiratory state detection results under a plurality of different time scales.
According to a third aspect of embodiments of the present application, there is provided a computer device comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform a method of detecting a respiratory state as described in any of the preceding claims.
According to a fourth aspect of embodiments of the present application, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of detecting a respiratory state as described in any of the above.
According to the embodiment of the application, the sign signals of the user under the preset first time scale are obtained; extracting a ballistocardiogram signal and a respiration signal from the physical sign signal; determining a first characteristic sequence corresponding to the ballistocardiogram signal and the respiratory signal; the first characteristic sequence comprises a heart rate characteristic sequence, a heart rate variability characteristic sequence, a respiratory rate characteristic sequence and a sleep characteristic sequence; determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second characteristic sequence comprises a reference characteristic sequence, a fluctuation characteristic sequence, a most value characteristic sequence and a mode characteristic sequence; and inputting the second characteristic sequences under a plurality of different second time scales into a trained respiratory state detection model to obtain a respiratory state detection result, thereby reducing the cost and improving the accuracy of respiratory state detection.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
For a better understanding and implementation, the present invention is described in detail below with reference to the drawings.
Drawings
FIG. 1 is a flow chart of a method for detecting respiratory status according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of step S20 according to an embodiment of the present application;
fig. 3 is a schematic flow chart of step S30 according to an embodiment of the present application;
FIG. 4 is a flowchart of step S40 according to one embodiment of the present application;
FIG. 5 is a block diagram of a respiratory state detection apparatus according to one embodiment of the present application;
fig. 6 is a schematic block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following detailed description of the embodiments of the present application will be given with reference to the accompanying drawings.
It should be understood that the described embodiments are merely some, but not all, of the embodiments of the present application. All other embodiments, based on the embodiments herein, which would be apparent to one of ordinary skill in the art without making any inventive effort, are intended to be within the scope of the present application.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the application. As used in this application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims. In the description of this application, it should be understood that the terms "first," "second," "third," and the like are used merely to distinguish between similar objects and are not necessarily used to describe a particular order or sequence, nor should they be construed to indicate or imply relative importance. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
Example 1
Fig. 1 is a flowchart of a respiration state detection method according to an embodiment of the present application. The respiratory state detection method provided by the embodiment of the application comprises the following steps:
s10: and acquiring a sign signal of the user under a preset first time scale.
In this embodiment of the present application, the main execution body of the respiration state detection method is a respiration state detection device (hereinafter referred to as detection device), and the detection device may be a computer device, a server, or a server cluster formed by combining multiple computer devices.
The detection device can acquire the sign signals of the user at a plurality of different preset time scales by inquiring in a preset database. The detection device can also adopt an undisturbed sensor to collect vital sign signals of a user at a preset plurality of different time scales in real time. The undisturbed sensor comprises a signal acquisition module and a data storage module.
Specifically, the signal acquisition module is placed under a user pillow, the user sleeps on the pillow, the user generates micro vibration due to heart activity, respiratory activity and the like, the gravity center is shifted, and a force signal is generated. The signal acquisition module can convert the force signal into an analog electric signal, and then the analog electric signal is filtered, amplified and A/D converted into a digital signal with the sampling rate of 1000Hz through the built-in filter circuit, the amplification circuit and the A/D conversion circuit, namely the sign signal.
The preset first time scale is an average duration of using the detection device by the user, for example, the preset first time scale is 100 days.
S20: and extracting a ballistocardiogram signal and a respiration signal from the physical sign signal.
In the embodiment of the present application, the sign signals are aliasing signals of the ballistocardiogram signal and the respiratory signal, and the independent ballistocardiogram signal and the independent respiratory signal are extracted from the sign signals respectively.
S30: determining a first characteristic sequence corresponding to the ballistocardiogram signal and the respiratory signal; the first feature sequence includes a heart rate feature sequence, a heart rate variability feature sequence, a respiratory rate feature sequence, and a sleep feature sequence.
Heart Rate (HR) refers to the number of beats per minute in a normal person's resting state. Heart rate variability (Heart Rate Variability, HRV for short) refers to the variation of the beat-to-beat cycle differences. Respiratory Rate (RR) is the number of breaths per minute.
In the embodiment of the application, a plurality of cardiac intervals can be obtained according to the ballistocardiogram signal, and a plurality of respiratory intervals can be obtained according to the respiratory signal. The heart rate characteristic sequence and the heart rate variability characteristic sequence can be determined according to a plurality of cardiac intervals, the respiratory rate characteristic sequence can be determined according to a plurality of respiratory intervals, and the sleep characteristic sequence can be determined through a plurality of cardiac intervals and a plurality of respiratory intervals.
S40: determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second feature sequence includes a reference feature sequence, a ripple feature sequence, a maximum feature sequence, and a mode feature sequence.
In the embodiment of the present application, the second time scale is a continuous time scale, and the plurality of different second time scales may be 3 days continuous, 5 days continuous, 7 days continuous, 10 days continuous, 14 days continuous, and 30 days continuous. After obtaining the heart rate characteristic sequence, the heart rate variability characteristic sequence, the respiratory rate characteristic sequence and the sleep characteristic sequence under the preset first time scale, the reference characteristic sequence, the fluctuation characteristic sequence, the most value characteristic sequence and the mode characteristic sequence under a plurality of different second time scales can be further obtained.
S50: and inputting the second characteristic sequences under a plurality of different second time scales into a trained respiratory state detection model to obtain a respiratory state detection result.
In the embodiment of the present application, the breathing state of the user includes both normal and abnormal states, which can be represented by the labels 0 and 1. And inputting the second characteristic sequences under a plurality of different second time scales into a trained respiratory state detection model, wherein the trained respiratory state detection model can output one of normal or abnormal states.
By applying the embodiment of the application, the sign signals of the user under the preset first time scale are obtained; extracting a ballistocardiogram signal and a respiration signal from the physical sign signal; determining a first characteristic sequence corresponding to the ballistocardiogram signal and the respiratory signal; the first characteristic sequence comprises a heart rate characteristic sequence, a heart rate variability characteristic sequence, a respiratory rate characteristic sequence and a sleep characteristic sequence; determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second characteristic sequence comprises a reference characteristic sequence, a fluctuation characteristic sequence, a most value characteristic sequence and a mode characteristic sequence; and inputting the second characteristic sequences under a plurality of different second time scales into a trained respiratory state detection model to obtain a respiratory state detection result. According to the method and the device, the physical sign signals of the user are acquired through the undisturbed sensor acquisition equipment, and the monitoring can be carried out for a long time, so that the cost is reduced, and the accuracy of detecting the breathing state is improved.
In an alternative embodiment, referring to fig. 2, the step S20 includes steps S21 to S25, specifically as follows:
s21: and filtering the sign signal by adopting a filter to obtain a filtered sign signal.
In the embodiment of the application, a second-order Butterworth band-pass filter with a cut-off frequency of 2-20 Hz is adopted to remove noise in a plurality of sign signals with different time scales. Further, the power frequency interference and the Gaussian additive noise are removed through a fourth-order Butterworth band-pass filter with the cut-off frequency of 20 Hz. Further, taking 15s as a fixed time window, the sliding scale is 1s, detecting the peak-to-average ratio of the signals of the time window to detect the body movement signal, when the peak-to-average ratio of the signals is larger than a threshold value, considering that the body movement signal exists in the time window, and emptying and re-incorporating the signals in the time window into a new queue signal.
S22: performing corrosion-before-expansion open operation on the filtered sign signals to obtain first sign signals;
s23: performing a closing operation of expanding and then corroding the filtered sign signals to obtain second sign signals;
s24: averaging the first and second sign signals, and taking the average result as a respiration signal;
S25: and subtracting the filtered sign signals from the respiratory signals to obtain ballistocardiogram signals.
In the embodiment of the application, on the basis of eliminating the body movement signal, due to the fact that the difference exists between the main frequency bands of the respiratory signal and the heart attack signal, the contribution of the respiratory signal to the heart attack signal is filtered through morphological filtering, and then the respiratory signal is extracted. Because the respiration signal and the ballistocardiogram signal are in a superposition relationship, the ballistocardiogram signal can be obtained by subtracting the respiration signal from the filtered physical sign signal.
In an alternative embodiment, referring to fig. 3, the step S30 includes steps S31 to S36, specifically as follows:
s31: and carrying out time sequence bidirectional detection on the ballistocardiogram signal according to a preset ballistocardiogram signal template, determining the peak point coordinate of the ballistocardiogram signal, and obtaining a plurality of cardiac intervals according to the peak point coordinate.
In the embodiment of the application, a preset ballistocardiogram signal template is established according to morphological characteristics of ballistocardiogram signals, and then, sequential bidirectional detection is carried out on the ballistocardiogram signals according to the preset ballistocardiogram signal template, so that the 'J' -peak position is positioned. And (3) further refining and calibrating the positioned 'J' peak position through setting of the refractory period and interval stability to obtain the final 'J' peak position. And carrying out differential calculation on a plurality of front and rear J peaks to obtain corresponding values of the J intervals, namely obtaining a plurality of cardiac intervals.
S32: obtaining a plurality of heart rates according to the plurality of cardiac intervals; acquiring a plurality of heart rate data parameters corresponding to heart rate, and taking the heart rate data parameters as heart rate characteristic sequences; wherein the heart rate data parameters include a mode of the heart rate, a mean of the heart rate, a maximum of the heart rate, and a minimum of the heart rate.
In the present embodiment, several heart rates may be calculated from several cardiac intervals. Wherein, the calculation formula of the heart rate per minute is as follows:
where HR denotes heart rate and t_bcg denotes cardiac interval.
After obtaining several heart rates, the mode, mean, maximum and minimum values of several heart rates may be calculated, resulting in a heart rate signature sequence. Specifically, taking an example of a sleep time period of 5 hours for one night, 5 minutes is one unit time period, and 5 hours are divided into 60 unit time periods. An average cardiac interval can be obtained every 5 minutes, so that 60 average cardiac intervals can be obtained every night, and the corresponding heart rate is calculated for the 60 average cardiac intervals, so that the mode, average, maximum and minimum of the heart rate are obtained.
S33: calculating standard deviations of the plurality of cardiac intervals, and obtaining a plurality of power spectrums according to the standard deviations and a preset autoregressive spectrum estimation method; comparing a plurality of power spectrums with a preset frequency spectrum range, and determining the frequency spectrum range of each power spectrum; obtaining heart rate deceleration force according to the plurality of cardiac intervals; obtaining a heart rate variability characteristic sequence according to the average value of all power spectrums in the same frequency spectrum range and the heart rate deceleration force;
In the embodiment of the present application, according to a plurality of cardiac intervals, a corresponding standard deviation may be calculated, and the calculation formula is as follows:
wherein N represents the total number of cardiac intervals,
representing N cardiac interval averages, T_BCG i Representing the ith cardiac interval. Specifically, an average value is calculated for 60 average cardiac intervals, and a standard deviation is calculated.
The autoregressive spectrum estimation method is an important spectrum estimation method, has the advantage of high frequency resolution, and improves spectral line splitting and frequency offset phenomena. And carrying out frequency domain analysis on each standard deviation by a preset autoregressive spectrum estimation method to obtain a plurality of power spectrums. According to a preset Frequency spectrum range, dividing a plurality of power spectrums into four Frequency bands, wherein the four Frequency bands comprise High Frequency (HF): 0.15 Hz-0.4 Hz, low Frequency (LF): 0.04 Hz-0.15 Hz, very low frequency band (Very Low Frequency, VLF): 0.003 Hz-0.04 Hz, ultra-low frequency band (Ultra Low Frequency, ULF): <0.003Hz. The four frequency band powers are used as HRV frequency domain characteristics.
Heart rate deceleration force (Deceleration Capacity of Rate, DC for short) is obtained from several cardiac intervals. Specifically, the heart rate deceleration force calculation formula is as follows:
DC＝[X(0)+X(1)-X(-1)-X(-2)]*1/4
Wherein X (0) is the mean value of the cardiac intervals of all deceleration center points; x (1) is the mean value of the first cardiac intervals immediately to the right of the deceleration center point; x (-1) is the mean value of the first cardiac interval adjacent to the left side of the deceleration center point; x (-2) is the mean of all cardiac intervals of the second adjacent left side of the deceleration center point.
The ratio of the band power LF, HF, ULF, VLF, LF to HF and DC were used as the heart rate variability signature sequence.
S34: and determining a plurality of breathing intervals of the breathing signal according to a preset zero crossing point detection method.
In the embodiment of the present application, a zero crossing detection method is adopted, and by detecting the zero crossing of a respiratory signal in two adjacent rising edges as a respiratory interval, that is, the ending point of one respiratory signal is regarded as the starting point of the next respiratory signal.
S35: obtaining a plurality of respiratory frequencies according to the plurality of respiratory intervals; acquiring a plurality of breathing data parameters corresponding to breathing frequency, and taking the breathing data parameters as a breathing frequency characteristic sequence; the respiratory data parameters comprise a mode of respiratory frequency, a mean value of respiratory frequency, a maximum value of respiratory frequency, a minimum value of respiratory frequency, a mean value of respiratory frequency which is larger than or equal to a first preset threshold value and a mean value of respiratory frequency which is smaller than a second preset threshold value.
In the embodiment of the present application, the calculation formula of the respiratory rate per minute is as follows:
where RR denotes the respiratory rate and t_rr denotes the respiratory interval.
After obtaining several respiratory frequencies, the respiratory frequency mode RR1, average RR2, maximum RR3, minimum RR4 can be calculated. And comparing the plurality of respiratory frequencies with a first preset threshold value and a second preset threshold value, so as to determine all respiratory frequencies which are larger than or equal to the first preset threshold value and all respiratory frequencies which are smaller than the second preset threshold value. All respiratory frequencies greater than or equal to the first preset threshold are averaged, obtaining an average result RR5. Wherein the first preset threshold is 21 times/min, and RR5 is used for indicating that the breathing frequency is too fast. All respiratory frequencies smaller than the second preset threshold are averaged to obtain an average result RR6. Wherein the first preset threshold is 10 times/min and RR6 is used for indicating that the breathing rate is too slow. RR1, RR2, RR3, RR4, RR5, and RR6 were used as the respiratory rate characteristic sequences.
S36: and obtaining a sleep characteristic sequence according to the cardiac intervals, the respiratory intervals and a preset cardiopulmonary coupling method.
In the embodiment of the application, the sleep characteristic sequence includes a sleep time, a wake time, a total sleep duration, and an effective sleep duration (i.e., the total sleep duration minus the sleep wake duration). And calculating sleep stages of the user under different time scales by means of a heart-lung coupling method by means of the heart-lung interval and the breathing interval, so that a sleep characteristic sequence under the corresponding time scales is obtained.
In an alternative embodiment, referring to fig. 4, the step S40 includes steps S41 to S42, specifically as follows:
s41: traversing each second time scale, and calculating mathematical expectations, normalized variances, maximum values and modes of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence and the sleep feature sequence under the current second time scale to be respectively used as a reference feature sequence, a fluctuation feature sequence, a maximum value feature sequence and a mode feature sequence corresponding to the current second time scale;
s42: and repeatedly traversing each second time scale until the heart rate characteristic sequence, the heart rate variability characteristic sequence, the respiratory frequency characteristic sequence and the reference characteristic sequence, the fluctuation characteristic sequence, the maximum characteristic sequence and the mode characteristic sequence corresponding to the sleep characteristic sequence are determined under all second time scales.
In the embodiment of the present application, for the case where the second time scale is 3 consecutive days, 5 consecutive days, 7 consecutive days, 10 consecutive days, 14 consecutive days, and 30 consecutive days, respectively, the corresponding reference feature sequence, fluctuation feature sequence, the most significant feature sequence, and the mode feature sequence are calculated.
Taking the second time scale as 3 consecutive days, the reference feature sequence corresponding to HR1 in the heart rate feature sequence is described as an example. At a preset first time scale, HR1 per day is known, taking the mathematical expectation of HR1 for 3 consecutive days as the value per day in the baseline signature sequence. For the values on day 1 in the baseline signature, only HR1 on day 1 is known, and HR1 on days 2 and 3 are absent, so the value on day 1 in the baseline signature is 0. For the values on day 2 in the baseline signature, only HR1 on days 1 and 2 are known, and there is no HR1 on day 3, so the value on day 2 in the baseline signature is 0. For the values on day 3 in the baseline signature, since HR1 on day 1, day 2, and day 3 are known, mathematical expectations for HR1 on day 1, day 2, and day 3 are taken as the value X1 on day 3 in the baseline signature, mathematical expectations for HR1 on day 2, day 3, and day 4 are taken as the value X2 on day 4 in the baseline signature, and mathematical expectations for HR1 on day 98, day 99, and day 100 are taken as the value X98 on day 100 in the baseline signature, thereby obtaining the baseline signature of HR1 for heart rate signatures on consecutive 3 days.
The solving process of the heart rate characteristic sequence, the heart rate variability characteristic sequence, the respiratory rate characteristic sequence, and the reference characteristic sequence, the fluctuation characteristic sequence, the maximum value characteristic sequence, and the mode characteristic sequence corresponding to the sleep characteristic sequence in the second different time scale is consistent with the solving process of the above examples, and is not repeated here.
In an alternative embodiment, before the step S50, step S501 is included, which specifically includes the following steps:
s501: and taking the second characteristic sample data under a plurality of different second time scales as input, taking the breath state sample data under a plurality of different second time scales as output, and inputting the breath state sample data into a breath state detection model to be trained to obtain a trained breath state detection model.
In the embodiment of the present application, the respiratory state detection model to be trained may be a deep learning network model or a machine learning model. Specifically, the machine learning model may be one of Decision Tree model (DT), logistic regression model (Logistic Regression, LR), support vector machine model (Support Vector Machines, SVM), random Forest model (RF), and AdaBoost model. Obtaining a plurality of second characteristic sample data under different second time scales and corresponding breath state sample data, taking the second characteristic sample data under the different second time scales as input, taking the breath state sample data under the different second time scales as output, inputting the breath state sample data into a breath state detection model to be trained, updating network weight parameters of the breath state detection model to be trained, and obtaining the trained breath state detection model.
Example 2
The following are examples of apparatus that may be used to perform the method of example 1 of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method in embodiment 1 of the present application.
Fig. 5 is a schematic structural diagram of a respiratory state detection device according to an embodiment of the present application. The respiratory state detection apparatus 6 provided in the embodiment of the present application includes:
the sign signal obtaining module 61 is configured to obtain a sign signal of a user at a preset first time scale;
a signal extraction module 62 for extracting a ballistocardiogram signal and a respiration signal from the sign signal;
a first feature sequence determining module 63, configured to determine a first feature sequence corresponding to the ballistocardiogram signal and the respiratory signal; the first characteristic sequence comprises a heart rate characteristic sequence, a heart rate variability characteristic sequence, a respiratory rate characteristic sequence and a sleep characteristic sequence;
a second feature sequence determination module 64 for determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second characteristic sequence comprises a reference characteristic sequence, a fluctuation characteristic sequence, a most value characteristic sequence and a mode characteristic sequence;
The detection result obtaining module 65 is configured to input the second feature sequences under a plurality of different second time scales to a trained respiratory state detection model, so as to obtain a respiratory state detection result.
Optionally, the signal extraction module includes:
the signal filtering unit is used for filtering the sign signal by adopting a filter to obtain a filtered sign signal;
the first sign signal unit is used for performing corrosion-before-expansion open operation on the filtered sign signal to obtain a first sign signal;
the second sign signal unit is used for performing expansion-before-corrosion closing operation on the filtered sign signal to obtain a second sign signal;
a respiratory signal obtaining unit for averaging the first and second sign signals and taking the average result as a respiratory signal;
and the ballistocardiogram signal obtaining unit is used for subtracting the filtered physical sign signal from the respiratory signal to obtain a ballistocardiogram signal.
Optionally, the first feature sequence determining module includes:
the cardiac interval obtaining unit is used for carrying out time sequence bidirectional detection on the ballistocardiogram signals according to a preset ballistocardiogram signal template, determining peak point coordinates of the ballistocardiogram signals, and obtaining a plurality of cardiac intervals according to the peak point coordinates;
The heart rate characteristic sequence obtaining unit is used for obtaining a plurality of heart rates according to the plurality of cardiac intervals; acquiring a plurality of heart rate data parameters corresponding to heart rate, and taking the heart rate data parameters as heart rate characteristic sequences; wherein the heart rate data parameters include a mode of the heart rate, a mean of the heart rate, a maximum of the heart rate, and a minimum of the heart rate;
the heart rate variability characteristic sequence obtaining unit is used for calculating standard deviations of the plurality of cardiac intervals and obtaining a plurality of power spectrums according to the standard deviations and a preset autoregressive spectrum estimation method; comparing a plurality of power spectrums with a preset frequency spectrum range, and determining the frequency spectrum range of each power spectrum; obtaining heart rate deceleration force according to the plurality of cardiac intervals; obtaining a heart rate variability characteristic sequence according to the average value of all power spectrums in the same frequency spectrum range and the heart rate deceleration force;
a breath interval determining unit, configured to determine a plurality of breath intervals of the breath signal according to a preset zero crossing point detection method;
the respiratory frequency characteristic sequence obtaining unit is used for obtaining a plurality of respiratory frequencies according to the respiratory intervals; acquiring a plurality of breathing data parameters corresponding to breathing frequency, and taking the breathing data parameters as a breathing frequency characteristic sequence; the respiratory data parameters comprise a mode of respiratory frequency, a mean value of respiratory frequency, a maximum value of respiratory frequency, a minimum value of respiratory frequency, a mean value of respiratory frequency which is larger than or equal to a first preset threshold value and a mean value of respiratory frequency which is smaller than a second preset threshold value;
The sleep characteristic sequence obtaining unit is used for obtaining a sleep characteristic sequence according to a plurality of cardiac intervals, a plurality of respiratory intervals and a preset cardiopulmonary coupling method.
Optionally, the second feature sequence determining module includes:
the feature sequence calculation unit is used for traversing each second time scale, and calculating mathematical expectations, normalized variances, maximum values and modes of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence and the sleep feature sequence under the current second time scale to be respectively used as a reference feature sequence, a fluctuation feature sequence, a maximum value feature sequence and a mode feature sequence corresponding to the current second time scale.
And the characteristic sequence determining unit is used for repeatedly traversing each second time scale until the heart rate characteristic sequence, the heart rate variability characteristic sequence, the respiratory rate characteristic sequence and the reference characteristic sequence, the fluctuation characteristic sequence, the maximum characteristic sequence and the mode characteristic sequence corresponding to the sleep characteristic sequence are determined under all the second time scales.
By applying the embodiment of the application, the sign signals of the user under the preset first time scale are obtained; extracting a ballistocardiogram signal and a respiration signal from the physical sign signal; determining a first characteristic sequence corresponding to the ballistocardiogram signal and the respiratory signal; the first characteristic sequence comprises a heart rate characteristic sequence, a heart rate variability characteristic sequence, a respiratory rate characteristic sequence and a sleep characteristic sequence; determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second characteristic sequence comprises a reference characteristic sequence, a fluctuation characteristic sequence, a most value characteristic sequence and a mode characteristic sequence; and inputting the second characteristic sequences under a plurality of different second time scales into a trained respiratory state detection model to obtain a respiratory state detection result. According to the method and the device, the physical sign signals of the user are acquired through the undisturbed sensor acquisition equipment, and the monitoring can be carried out for a long time, so that the cost is reduced, and the accuracy of detecting the breathing state is improved.
Example 3
The following are device embodiments of the present application that may be used to perform the method of embodiment 1 of the present application. For details not disclosed in the apparatus embodiments of the present application, please refer to the method in embodiment 1 of the present application.
Referring to fig. 6, the present application further provides an electronic device 300, which may be specifically a computer, a mobile phone, a tablet computer, an interactive tablet, and the like, in an exemplary embodiment of the present application, the electronic device 300 is an interactive tablet, and the interactive tablet may include: at least one processor 301, at least one memory 302, at least one display, at least one network interface 303, a user interface 304, and at least one communication bus 305.
The user interface 304 is mainly used for providing an input interface for a user, and acquiring data input by the user. Optionally, the user interface may also include a standard wired interface, a wireless interface.
The network interface 303 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein a communication bus 305 is used to enable connected communications between these components.
Wherein the processor 301 may include one or more processing cores. The processor uses various interfaces and lines to connect various portions of the overall electronic device, perform various functions of the electronic device, and process data by executing or executing instructions, programs, code sets, or instruction sets stored in memory, and invoking data stored in memory. Alternatively, the processor may be implemented in hardware in at least one of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display layer; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor and may be implemented by a single chip.
The Memory 302 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory may be used to store instructions, programs, code sets, or instruction sets. The memory may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory may optionally also be at least one storage device located remotely from the aforementioned processor. The memory as a computer storage medium may include an operating system, a network communication module, a user interface module, and an operating application program.
The processor may be configured to call an application program of the video resolution adjustment method stored in the memory, and specifically execute the method steps of the foregoing embodiment 1, and the specific execution process may refer to the specific description shown in embodiment 1, which is not repeated herein.
Example 4
The present application further provides a computer readable storage medium, on which a computer program is stored, where instructions are adapted to be loaded by a processor and execute the method steps of the above-described embodiment 1, and the specific execution process may refer to the specific description shown in the embodiment, which is not repeated herein. The storage medium can be an electronic device such as a personal computer, a notebook computer, a smart phone, a tablet computer and the like.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The above-described apparatus embodiments are merely illustrative, in which components illustrated as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present application. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (7)
1. A method of detecting respiratory status, the method comprising the steps of:
acquiring a sign signal of a user under a preset first time scale;
extracting a ballistocardiogram signal and a respiration signal from the physical sign signal;
Determining a first characteristic sequence corresponding to the ballistocardiogram signal and the respiratory signal; the first characteristic sequence comprises a heart rate characteristic sequence, a heart rate variability characteristic sequence, a respiratory rate characteristic sequence and a sleep characteristic sequence; performing time sequence bidirectional detection on the ballistocardiogram signal according to a preset ballistocardiogram signal template, determining peak point coordinates of the ballistocardiogram signal, and obtaining a plurality of cardiac intervals according to the peak point coordinates; obtaining a plurality of heart rates according to the plurality of cardiac intervals; acquiring a plurality of heart rate data parameters corresponding to heart rate, and taking the heart rate data parameters as heart rate characteristic sequences; wherein the heart rate data parameters include a mode of the heart rate, a mean of the heart rate, a maximum of the heart rate, and a minimum of the heart rate; calculating standard deviations of the plurality of cardiac intervals, and obtaining a plurality of power spectrums according to the standard deviations and a preset autoregressive spectrum estimation method; comparing a plurality of power spectrums with a preset frequency spectrum range, and determining the frequency spectrum range of each power spectrum; obtaining heart rate deceleration force according to the plurality of cardiac intervals; obtaining a heart rate variability characteristic sequence according to the average value of all power spectrums in the same frequency spectrum range and the heart rate deceleration force; determining a plurality of breathing intervals of the breathing signal according to a preset zero crossing point detection method; obtaining a plurality of respiratory frequencies according to the plurality of respiratory intervals; acquiring a plurality of breathing data parameters corresponding to breathing frequency, and taking the breathing data parameters as a breathing frequency characteristic sequence; the respiratory data parameters comprise a mode of respiratory frequency, a mean value of respiratory frequency, a maximum value of respiratory frequency, a minimum value of respiratory frequency, a mean value of respiratory frequency which is larger than or equal to a first preset threshold value and a mean value of respiratory frequency which is smaller than a second preset threshold value; obtaining a sleep characteristic sequence according to a plurality of cardiac intervals, a plurality of respiratory intervals and a preset cardiopulmonary coupling method;
Determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second characteristic sequence comprises a reference characteristic sequence, a fluctuation characteristic sequence, a most value characteristic sequence and a mode characteristic sequence; traversing each second time scale, and calculating mathematical expectations, normalized variances, maximum values and modes of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence and the sleep feature sequence under the current second time scale to be respectively used as a reference feature sequence, a fluctuation feature sequence, a maximum value feature sequence and a mode feature sequence corresponding to the current second time scale; repeatedly traversing each second time scale, and calculating a reference feature sequence, a fluctuation feature sequence, a maximum feature sequence and a mode feature sequence corresponding to each second time scale until the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence and the reference feature sequence, the fluctuation feature sequence, the maximum feature sequence and the mode feature sequence corresponding to the sleep feature sequence are determined under all second time scales;
And inputting the second characteristic sequences under a plurality of different second time scales into a trained respiratory state detection model to obtain a respiratory state detection result.
2. The method for detecting a respiratory state according to claim 1, wherein:
the step of extracting the ballistocardiogram signal and the respiratory signal from the physical sign signal comprises the following steps:
filtering the sign signal by adopting a filter to obtain a filtered sign signal;
performing corrosion-before-expansion open operation on the filtered sign signals to obtain first sign signals;
performing a closing operation of expanding and then corroding the filtered sign signals to obtain second sign signals;
averaging the first and second sign signals, and taking the average result as a respiration signal;
and subtracting the filtered sign signals from the respiratory signals to obtain ballistocardiogram signals.
3. The method for detecting a respiratory state according to claim 1, wherein:
the step of inputting the second feature sequences under a plurality of different second time scales to a trained respiratory state detection model to obtain a respiratory state detection result comprises the following steps:
And taking the second characteristic sample data under a plurality of different second time scales as input, taking the breath state sample data under a plurality of different second time scales as output, and inputting the breath state sample data into a breath state detection model to be trained to obtain a trained breath state detection model.
4. A respiratory state detection apparatus for implementing the respiratory state detection method according to any one of claims 1 to 3, comprising:
the sign signal acquisition module is used for acquiring sign signals of a user under a preset first time scale;
the signal extraction module is used for extracting a ballistocardiogram signal and a respiration signal from the physical sign signal;
the first characteristic sequence determining module is used for determining a first characteristic sequence corresponding to the ballistocardiogram signal and the respiration signal; the first characteristic sequence comprises a heart rate characteristic sequence, a heart rate variability characteristic sequence, a respiratory rate characteristic sequence and a sleep characteristic sequence; performing time sequence bidirectional detection on the ballistocardiogram signal according to a preset ballistocardiogram signal template, determining peak point coordinates of the ballistocardiogram signal, and obtaining a plurality of cardiac intervals according to the peak point coordinates; obtaining a plurality of heart rates according to the plurality of cardiac intervals; acquiring a plurality of heart rate data parameters corresponding to heart rate, and taking the heart rate data parameters as heart rate characteristic sequences; wherein the heart rate data parameters include a mode of the heart rate, a mean of the heart rate, a maximum of the heart rate, and a minimum of the heart rate; calculating standard deviations of the plurality of cardiac intervals, and obtaining a plurality of power spectrums according to the standard deviations and a preset autoregressive spectrum estimation method; comparing a plurality of power spectrums with a preset frequency spectrum range, and determining the frequency spectrum range of each power spectrum; obtaining heart rate deceleration force according to the plurality of cardiac intervals; obtaining a heart rate variability characteristic sequence according to the average value of all power spectrums in the same frequency spectrum range and the heart rate deceleration force; determining a plurality of breathing intervals of the breathing signal according to a preset zero crossing point detection method; obtaining a plurality of respiratory frequencies according to the plurality of respiratory intervals; acquiring a plurality of breathing data parameters corresponding to breathing frequency, and taking the breathing data parameters as a breathing frequency characteristic sequence; the respiratory data parameters comprise a mode of respiratory frequency, a mean value of respiratory frequency, a maximum value of respiratory frequency, a minimum value of respiratory frequency, a mean value of respiratory frequency which is larger than or equal to a first preset threshold value and a mean value of respiratory frequency which is smaller than a second preset threshold value; obtaining a sleep characteristic sequence according to a plurality of cardiac intervals, a plurality of respiratory intervals and a preset cardiopulmonary coupling method;
A second feature sequence determining module for determining a second feature sequence of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence, and the sleep feature sequence at a number of different second time scales; the second characteristic sequence comprises a reference characteristic sequence, a fluctuation characteristic sequence, a most value characteristic sequence and a mode characteristic sequence; traversing each second time scale, and calculating mathematical expectations, normalized variances, maximum values and modes of the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence and the sleep feature sequence under the current second time scale to be respectively used as a reference feature sequence, a fluctuation feature sequence, a maximum value feature sequence and a mode feature sequence corresponding to the current second time scale; repeatedly traversing each second time scale, and calculating a reference feature sequence, a fluctuation feature sequence, a maximum feature sequence and a mode feature sequence corresponding to each second time scale until the heart rate feature sequence, the heart rate variability feature sequence, the respiratory rate feature sequence and the reference feature sequence, the fluctuation feature sequence, the maximum feature sequence and the mode feature sequence corresponding to the sleep feature sequence are determined under all second time scales;
The detection result obtaining module is used for inputting the second characteristic sequences under a plurality of different second time scales into the trained respiratory state detection model to obtain respiratory state detection results.
5. The respiratory state detection apparatus of claim 4, wherein the signal extraction module comprises:
the signal filtering unit is used for filtering the sign signal by adopting a filter to obtain a filtered sign signal;
the first sign signal unit is used for performing corrosion-before-expansion open operation on the filtered sign signal to obtain a first sign signal;
the second sign signal unit is used for performing expansion-before-corrosion closing operation on the filtered sign signal to obtain a second sign signal;
a respiratory signal obtaining unit for averaging the first and second sign signals and taking the average result as a respiratory signal;
and the ballistocardiogram signal obtaining unit is used for subtracting the filtered physical sign signal from the respiratory signal to obtain a ballistocardiogram signal.
6. A computer device, comprising: a processor, a memory and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 3 when the computer program is executed.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 3.
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