Patient status monitoring system for wards

Abstract

The present invention provides a patient status monitoring system suitable for a ward, comprising an acquisition module, a processing module and a display module, wherein the acquisition module comprises a first millimeter-wave radar device and a second millimeter-wave radar device, wherein the first millimeter-wave radar device is used to acquire a first echo signal of a bed area in the ward, and the second millimeter-wave radar device is used to acquire a second echo signal of a bathroom area in the ward; the processing module is used to determine the patient's sleep information, breathing information and heart rate information according to the first echo signal, and determine the patient's behavior information according to the second echo signal, and then perform corresponding operations; the display module is used to display real-time data and post-processed data, and receive and display alarm information.

Description

Technical Field

[0001] The present invention relates to the field of medical equipment, and in particular to a patient status monitoring system suitable for a ward. Background Art

[0002] In the clinical care process, especially at night, the number of medical staff is relatively small, so it is difficult to detect potential changes in patients' physical functions in time. For patients in the critical recovery period after surgery, subtle fluctuations in their vital signs may reveal the effectiveness of the surgery and potential complications.

[0003] However, the traditional three-level nursing model of the hospital has obvious shortcomings: whether it is the primary care patients with milder conditions, the secondary care patients with moderate conditions, or the tertiary care patients with more serious conditions, they all face nursing challenges to varying degrees. The manual inspection method is limited by the number of personnel and work arrangements, and it is difficult to achieve real-time vital sign monitoring throughout the entire process. Frequent manual inspections not only disturb patients' rest and affect the recovery process, but also lead to high manpower consumption of nursing resources, increasing the hospital's operating costs. Summary of the invention

[0004] In view of this, the present application provides a patient status monitoring system suitable for a ward, comprising a collection module, a processing module and a display module, wherein:

[0005] The acquisition module includes a first millimeter wave radar device and a second millimeter wave radar device, wherein the first millimeter wave radar device is used to acquire a first echo signal of a bed area in the ward, and the second millimeter wave radar device is used to acquire a second echo signal of a bathroom area in the ward;

[0006] The processing module is used to determine the patient's sleep information, breathing information, and heart rate information according to the first echo signal, and determine the patient's behavior information according to the second echo signal, and then perform operations including the following:

[0007] determining work and rest regularity information according to the sleep information, and judging whether the patient has left the bed area for a long time according to the sleep information, and outputting alarm information if the patient has left the bed area for a long time;

[0008] By comparing the breathing information with the patient's breathing baseline information to determine whether the patient's breathing state is normal, outputting an alarm message when the breathing state is abnormal;

[0009] determining whether the patient presents a tidal breathing pattern according to the breathing information, and outputting an alarm message when the patient presents a tidal breathing pattern;

[0010] Determine whether the heart rate information is within a preset range, and output an alarm message when the heart rate information is outside the preset range;

[0011] Determine whether the patient stays for a long time or falls according to the behavior information, and output alarm information when the patient stays for a long time or falls;

[0012] The display module is used to display real-time data and post-processing data, and to receive and display the alarm information, wherein the real-time data includes the sleep information, the work and rest regularity information, the breathing information, the heart rate information, and the behavior information, and the post-processing data is statistical data generated based on the real-time data over a period of time.

[0013] Optionally, the acquisition module also includes a temperature sensor for monitoring the patient's body temperature and / or a blood pressure meter for measuring the patient's blood pressure; the processing module is also used to generate the patient's vital signs data based on the patient's body temperature and / or the patient's blood pressure, the respiratory information and the heart rate information; the display module is used to display the patient's vital signs data and its statistical data.

[0014] Optionally, the acquisition module also includes a sensor for collecting the temperature and / or humidity of the bathroom area in the ward; the processing module is also used to determine whether the temperature and / or humidity exceeds a preset range, and output an alarm message when the temperature and / or humidity exceeds the preset range.

[0015] Optionally, the sleep information includes bedtime, sleep falling time, morning awakening time, getting up time, number of times and time of leaving bed at night.

[0016] Optionally, the respiratory information includes a respiratory cycle and a respiratory movement amplitude; the processing module determines whether the patient presents a tidal breathing pattern based on the respiratory cycle and the respiratory movement amplitude, and intercepts the respiratory information corresponding to the tidal breathing pattern; the display module is used to display the respiratory information corresponding to the tidal breathing pattern.

[0017] Optionally, the respiratory information includes a respiratory rate BPM, and the processing module is used to determine probability density information P of the respiratory rate BPM. i The respiratory baseline information is the probability density information P of the same patient in multiple monitoring periods. i The generated probability density baseline information B.

[0018] Optionally, the processing module is used to convert the probability density information P of the patient in the target monitoring period into j The deviation feature b is obtained by comparing it with the probability density baseline information B, and then determining whether the patient's breathing state during the target monitoring period is normal based on the deviation feature b.

[0019] Optionally, the processing module is used to determine the respiratory rate B corresponding to the maximum probability value in the probability density baseline information B. max , and the probability density information P during the target monitoring period j Determine the respiratory rate P corresponding to the maximum probability max , using the respiratory rate B max and respiratory rate P max Obtaining a deviation characteristic value b1; and / or

[0020] The probability density baseline information B and the probability density information P of the target monitoring period are respectively j Extract the probability value B corresponding to the same respiratory rate value x x and P x , and calculate the difference between each pair of extracted probability values;

[0021] The differences between each pair of probability values ​​are accumulated to obtain the deviation characteristic value b2.

[0022] Optionally, the processing module is used to determine whether the deviation characteristic value b1 and / or the deviation characteristic value b2 exceeds a set threshold, and when the deviation characteristic value b1 and / or the deviation characteristic value b2 exceeds the set threshold, it is determined that the patient's breathing state during the target monitoring period is abnormal.

[0023] Optionally, the processing module is further used to identify apnea events and hypopnea events based on the respiratory information, and calculate the apnea index based on the number of apnea events and the number of hypopnea events in a unit time; the display module is used to display the apnea index and its statistical data.

[0024] Optionally, the behavior information is used to indicate whether the patient is in a bathroom area, and whether normal movements and falling movements occur.

[0025] Optionally, the normal actions include washing and going to the toilet.

[0026] Optionally, the display module includes a display device arranged in the ward, a display device of the ward management center and a mobile display device, wherein the display device in the ward is used to display the real-time data and post-processed data of the ward, and the display device of the ward management center and the mobile display device are used to display the real-time data and post-processed data of all wards.

[0027] The patient status monitoring system provided in this application monitors the patient's vital signs such as sleep information, breathing information and heart rate information through a millimeter wave radar device arranged in the ward, monitors the patient's behavior through another millimeter wave radar device arranged in the bathroom, and issues an alarm when abnormal vital signs or abnormal behavior of the patient are detected. The real-time data monitored and the post-processed statistical data can be displayed through the display module. This system realizes comprehensive and safe monitoring of patients under the premise of protecting the privacy of patients, reduces the monitoring burden of medical staff, improves the efficiency of ward management, improves the quality of nursing, and can improve the response speed to patient abnormalities, reduce the occurrence of clinical risk events at night, and raise the level of nursing services to a new height, optimize the utilization of medical resources, and help hospitals achieve efficient and intelligent operations. At the same time, the millimeter wave radar device collects signals based on non-contact sensing technology, and there is no need to attach any sensors to the patient's body, which will not interfere with the patient's rest at night, allowing patients to obtain a safer, more comfortable and high-quality medical experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 is a schematic diagram of a patient status monitoring system in an embodiment of the present invention;

[0030] Figure 2 is a schematic diagram of a collection module in an embodiment of the present invention;

[0031] Figure 3 is a waveform diagram of a breathing signal in an embodiment of the present invention;

[0032] Figure 4 It is a schematic diagram of performing sliding window on the respiratory signal and obtaining the respiratory rate in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] In addition, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0035] The embodiment of the present invention provides a patient status monitoring system suitable for a ward, such as Figure 1 The system shown includes a collection module 1, a processing module 2 and a display module 3.

[0036] The acquisition module 1 includes a first millimeter wave radar device 11 and a second millimeter wave radar device 12, wherein the first millimeter wave radar device 11 is used to acquire a first echo signal from a bed area in a ward, and the second millimeter wave radar device 12 is used to acquire a second echo signal from a bathroom area in the ward. The first millimeter wave radar device 11 can be installed above the head of a bed in a ward, transmitting a millimeter wave radar signal to the bed and receiving an echo signal; the second millimeter wave radar device 12 can be installed on the ceiling of the bathroom in the ward, transmitting a millimeter wave radar signal to the bathroom space below and receiving an echo signal.

[0037] The processing module 2 is used to determine the patient's sleep information, breathing information and heart rate information according to the first echo signal, and to determine the patient's behavior information according to the second echo signal. The processing module 2 can be a computer, server or other electronic device, which extracts the patient's vital signs from the echo signal collected by the first millimeter wave radar device 11, and extracts the patient's behavior in the bathroom from the echo signal collected in real time by the second millimeter wave radar device 12.

[0038] Specifically, the processing module 2 performs the following operations:

[0039] A. Determine the work and rest regularity information based on the sleep information, and judge whether the patient has been away from the bed area for a long time based on the sleep information. If the patient has been away from the bed area for a long time, output alarm information. Processing module 2 can determine whether the patient is in bed and identify the patient's sleep and wake-up state through the first echo signal. The obtained sleep information includes, for example, bed time, sleep time, morning awakening time, get-up time, number of times leaving the bed at night, time of leaving the bed at night, sleep efficiency, body movement information and other contents related to sleep quality. Combined with the clock information, an alarm for leaving the bed too long at night is issued when the patient is not in bed for a long time at night.

[0040] B. By comparing the breathing information with the patient's breathing baseline information, it is determined whether the patient's breathing state is normal, and an alarm message is output when the breathing state is abnormal. The breathing baseline information is used to characterize the patient's comprehensive breathing state over a long period of time. For example, if the monitoring period is during sleep every day, it can be continuously monitored for one month, and the breathing baseline information is generated based on the breathing information for one month.

[0041] C. Determine whether the patient presents a Cheyne-Stokes breathing pattern based on the breathing information, and output an alarm message when the patient presents a Cheyne-Stokes breathing pattern. Cheyne-Stokes breathing, also known as Cheyne-Stokes breathing, is characterized by alternating between gradual weakening and cessation of breathing and gradual strengthening of breathing. Processing module 2 extracts characteristic information related to the patient's chest and abdominal movements from the first echo signal, thereby obtaining a breathing waveform. Based on the breathing waveform within a certain period of time, it can be determined whether the patient presents a Cheyne-Stokes breathing pattern.

[0042] As a preferred embodiment, the respiratory information extracted by the processing module 2 includes the respiratory cycle and the respiratory movement amplitude. The processing module 2 determines whether the patient presents a tidal breathing pattern based on changes in the respiratory cycle and the respiratory movement amplitude, and intercepts the respiratory information corresponding to the tidal breathing pattern.

[0043] D. Identify apnea events and hypopnea events based on respiratory information, and calculate the apnea index based on the number of apnea events and hypopnea events per unit time. The respiratory cycle, frequency, and respiratory amplitude can be extracted from the respiratory waveform to determine whether apnea events and hypopnea events occur.

[0044] E. Determine whether the heart rate information is within the preset range, and output an alarm message when the heart rate information is outside the preset range. The first echo signal can reflect the tiny displacement of the human chest caused by heartbeat and breathing (these displacements will change the phase of the first echo signal). Through filtering, Fourier transform and other processing methods, the frequency component corresponding to the heartbeat can be extracted to calculate the heart rate.

[0045] F. Determine whether the patient stays for a long time or falls based on the behavior information, and output alarm information when the patient stays for a long time or falls. The distance between each reflection position and the transmission position can be obtained by using the second echo signal, so that the human body contour data can be obtained. Specifically, the three-dimensional point cloud data of the human body can be generated. According to the three-dimensional point cloud data, it can be determined whether there is a living body (patient) within the monitoring range, and the patient's limb movements can be identified. The three-dimensional point cloud data can be used to determine whether the patient has fallen, and combined with the clock information and the liveness detection results, it can be determined whether the patient has stayed for a long time.

[0046] In addition to detecting abnormal behaviors such as prolonged stay and falls, in some embodiments, the processing module 2 is also used to detect the patient's normal movements / daily movements, including but not limited to washing movements and toileting movements, which can reflect the patient's daily behavioral patterns.

[0047] The display module 3 is used to display real-time data and post-processing data, and to receive and display alarm information, wherein the real-time data includes sleep information, work and rest information, breathing information, heart rate information, and behavior information, and the post-processing data is statistical data generated based on real-time data over a period of time. In actual application scenarios, a multi-terminal display solution can be adopted. For example, the display module 3 may include a display device (bedside display 31) arranged in a ward, a display device (central control console 32) of a ward management center, and a mobile display device (mobile device 33), wherein the display device in the ward is used to display the real-time data and post-processing data of the ward, and the display device and mobile display device of the ward management center are used to display the real-time data and post-processing data of all wards.

[0048] As an example, real-time data includes, but is not limited to, detailed respiratory waveforms describing the patient's chest and abdominal movements, respiratory rate BPM, heart rate BPM, out-of-bed state, and sleep-wake state. Post-processing data includes but is not limited to statistical distribution histograms of second-level respiratory rate during sleep for the whole night; duration of the inspiratory phase, duration of the expiratory phase, peak inspiratory velocity, peak expiratory velocity, and ratio of duration of the inspiratory and expiratory phases in each stable breathing cycle; statistical distribution histogram of the ratio of peak inspiratory and expiratory velocity; detection of the characteristics of the Steffen-Chronotype breathing through the respiratory waveform and display of the respiratory waveform slice diagram in the corresponding time period; detection of the characteristics of apnea through the respiratory waveform and display of the respiratory waveform diagram in the corresponding time period; sleep staging information, displaying the distribution diagram of the time of going to bed, falling asleep, waking up in the morning, getting up, light sleep, and deep sleep on the time coordinate axis; through the sleep staging results, statistical distribution diagrams of various vital signs in different staging time periods; through the bedside radar sleep segment body movement detection, display the distribution diagram of body movement frequency in different time periods and different sleep stages during the complete sleep process; combined with the bedside radar and bathroom radar for detection of whether there is anyone, display the statistical distribution histogram of the number and duration of leaving the bed during the night sleep stage; statistical distribution diagram of the patient's bedtime throughout the day through the bedside radar.

[0049] In one embodiment, Figure 2 As shown, the acquisition module 1 also includes a temperature sensor 13 for monitoring the patient's body temperature and / or a blood pressure meter 14 for measuring the patient's blood pressure. The processing module 2 is also used to generate the patient's vital signs data according to the patient's body temperature and / or the patient's blood pressure, respiratory information and heart rate information, as a vital signs portrait of the patient, which more comprehensively reflects the patient's physiological state. The display module 3 is used to display the patient's vital signs data and its statistical data.

[0050] The acquisition module 1 in this embodiment also includes a temperature sensor 15 and / or a humidity sensor 16 for collecting the bathroom area in the ward. The processing module 2 is also used to determine whether the temperature and / or humidity exceeds a preset range, and output an alarm message when the temperature and / or humidity exceeds the preset range.

[0051] Regarding operation B performed by the processing module 2, in one embodiment, it specifically includes the following operations:

[0052] S1, extract the breathing signal s(t) from the echo signal of the first millimeter wave radar 11 within a single monitoring period. The breathing signal s(t) is as follows Figure 3 The waveform shown is continuous in time.

[0053] S2, extract multiple window breathing signals from the breathing signal s(t) by sliding window, and determine the breathing rate BPM of each window breathing signal respectively. Figure 4 As shown, the respiratory signal s(t) is continuous data at time t. In this step, a fixed window length is used to sample / divide the continuous signal. Adjacent windows can be partially overlapped or connected end to end without overlapping.

[0054] Through the processing of this step, the respiration rate BPM corresponding to each window respiration signal can be obtained, which represents the number of respirations per unit time. For example, the respiration signal s(t) is divided into window respiration signal #1, window respiration signal #2, ... window respiration signal #n, and then the corresponding respiration rate BPM#1, respiration rate BPM#2, ... respiration rate BPM#n are obtained.

[0055] There are many ways to determine the respiratory rate from the respiratory signal collected by the first millimeter wave radar 11. Usually, the signal is processed based on Fourier transform to obtain spectrum data, and then frequency information is obtained based on the spectrum data, and finally the respiratory rate is obtained based on the frequency information.

[0056] S3, calculate the probability density information P of the respiratory rate BPM of multiple window respiratory signals i Probability density information is a kind of statistical information. The probability density function of breathing rate BPM#1, breathing rate BPM#2…breathing rate BPM#n is expressed by the kernel density estimation method, and a continuous probability density curve is constructed. When the curve is used to show the data, the value of breathing rate is usually used as the horizontal axis and the probability value is used as the vertical axis, that is, the probability density information P i The probability values ​​corresponding to different respiratory rate values ​​are included. In the multiple window respiratory signals, the more the same respiratory rate BPM is, the higher the probability value corresponding to the respiratory rate value is.

[0057] S4, using the probability density information P of the same patient in multiple monitoring periods i Generate probability density baseline information B. Figure 4 The same method as shown in the figure is used to calculate the probability density information P for N monitoring periods. i (i=1, 2...N), and then combine these probability density information P i, calculate the probability density baseline information B. The data type of the probability density baseline information B is the same as the probability density information P i The same is the probability distribution of the respiratory rate, but the probability value is a combination of the probability density information P of multiple monitoring periods. i The results obtained.

[0058] There are many ways to generate probability density baseline information B. As a simple example, for example, N probability density information P i Accumulate and normalize, or for N probability density information P i Find the average, etc.

[0059] The probability density baseline information B is used to characterize the patient's comprehensive respiratory state over a long period of time. For example, if the monitoring period is during daily sleep, and the multiple monitoring periods are one month, the probability density baseline information B obtained through the above steps S1 to S4 represents the patient's respiratory state during sleep during this one month.

[0060] S5, by taking the probability density information P of the patient in the target monitoring period j Compare with the probability density baseline information B to obtain the deviation feature b. The so-called target monitoring period in this step depends on the purpose of executing this method. The target monitoring period can be a historical period or a current period, and the length of the target monitoring period can be set arbitrarily.

[0061] The respiratory signal of the target monitoring period is processed in the same manner as steps S1 to S3 to obtain probability density information. i To distinguish the literal expression, the probability density information of the target monitoring period is recorded as P j .

[0062] There are many ways to determine the difference between two probability density distribution information. For example, the probability density information P j The indicators of probability density baseline information B are then used to obtain the deviation characteristic value b through the values ​​of one or more indicators. The indicators for measuring probability density distribution include but are not limited to expected value, variance and standard deviation of each point data and expected value, skewness and kurtosis, etc.

[0063] The magnitude of the deviation feature b represents the difference between the patient's breathing state during the so-called target monitoring period and the long-term breathing state.

[0064] S6, determining whether the patient's respiratory state in the target monitoring period is normal according to the deviation feature b. The deviation feature b can be one or more numerical values, or multi-dimensional data, depending on the calculation method used in step S5. For example, if the deviation feature b is a numerical value and is positively correlated with the difference in respiratory state, then the larger the value of the deviation feature b, the more different the patient's respiratory state in the (any) monitoring period is from the long-term respiratory state, that is, the probability density information P j It deviates from the probability density baseline information B, thereby determining that the respiratory state is abnormal; if the deviation feature b is not a numerical value, it can be determined based on the specific data type whether its characteristics are obvious or meet certain preset conditions, thereby determining whether the patient's respiratory state during the target monitoring period is normal.

[0065] The target monitoring period may be a period before, during or after the above-mentioned multiple monitoring periods. For example, in the above steps, the daily respiratory signals of a patient in a certain month of a year are used to generate the probability density baseline information B. Then, any period may be a monitoring period of a certain day in the month, used to determine whether the patient's respiratory state on that day is normal; any period may be a monitoring period of a certain day before the month, used to determine whether the patient's respiratory state is normal before generating the baseline; any period may be a monitoring period of a certain day after the month, used to determine whether the patient's respiratory state is normal after generating the baseline.

[0066] Taking the application scenario as an example, for example, when the present solution is applied to sleep breathing monitoring of a human body, the above steps S1 to S4 can be used to generate the above probability density baseline information B based on the breathing signal of a user during a long period of sleep (such as a week or a month, etc.), and then the above steps S5 to S6 can be used to generate the probability density information P based on the breathing signal of the user in each subsequent sleep period. j , so as to monitor whether the sleep state of the user is normal. And the probability density baseline information B can be updated regularly or irregularly over time, that is, the probability density baseline information B is recalculated using the breathing signal of a period of time close to the current time or a specified arbitrary period of time.

[0067] Furthermore, the monitoring period further includes various sleep staging periods. Taking five stages as an example, the sleep period is divided into REM stage (rapid eye movement stage), N1 stage, N2 stage, N3 stage, and W stage (wakefulness stage). According to the above method, the respiratory signals in each sleep stage are processed respectively to generate probability density baseline information corresponding to each sleep stage, and then the probability density information of the patient in each sleep stage is compared with the corresponding probability density baseline information to determine whether the patient's breathing status in each sleep stage in a certain monitoring period is normal.

[0068] For another example, the present solution can be applied to the respiratory monitoring of patients in diseased states. First, the above steps S1 to S4 can be used to generate the above probability density baseline information B based on the respiratory signal of the patient in a healthy state. When the patient is in a diseased state, the above steps S5 to S6 can be used to generate the probability density information P based on the respiratory signal of the patient in the diseased state. j , and then monitor whether the patient's respiratory status is normal.

[0069] In one embodiment, determining the breathing rate BPM of each window breathing signal in the above step S2 further includes the following operations:

[0070] S21, perform short-time Fourier transform on each window respiration signal to obtain corresponding spectrum information, and use the peak point in the spectrum information and the data points near it to calculate the confidence C of the spectrum information, wherein the energy value of the data points near the peak point is negatively correlated with the confidence C. Specifically, the window respiration signal is a signal of a certain time length (such as several seconds or minutes), and the patient's behavior or noise in the environment may affect the extraction of the respiration signal during the specific implementation process. For example, the patient may have micro-body movements such as turning over, and other moving objects may appear in the environment, all of which may affect the echo signal of the first millimeter wave radar 11.

[0071] Spectrum information is the data of the corresponding relationship between frequency and energy. In an ideal state (without body movement and environmental influence), the spectrum information should have an obvious energy peak. When affected, the spectrum information will fluctuate to varying degrees. There may be multiple adjacent energy peaks in the spectrum information. For example, there are other slightly lower peaks near the highest energy peak point. The confidence C calculated in this step is used to reflect whether the data of each window is affected by the above influence.

[0072] S22, extracting the frequency of the peak point for the spectrum information corresponding to the respiratory signal in each window, that is, the frequency corresponding to the highest energy peak point in the spectrum.

[0073] S23, calculate the respiratory rate BPM corresponding to each window respiratory signal according to the frequency of the peak point. Specifically, first determine the sampling frequency Fs and the number of sampling points N. The sampling frequency refers to the number of samples per second, the unit is Hz, and the number of sampling points refers to the number of sample points used in an FFT calculation. Then calculate the frequency resolution. The frequency resolution Δf is the interval between each point on the frequency axis, Δf=Fs / N. For example, if the sampling frequency is 10Hz and the number of sampling points is 128 points, the frequency resolution is 0.078Hz. Then calculate BPM, determine the peak point index, assuming it is x, calculate the respiratory frequency f=Δf * x, assuming it is a Hz, BPM = a * 60.

[0074] S24, using the confidence C to screen the respiratory rate BPM of each window respiratory signal to obtain the respiratory rate BPM of multiple window respiratory signals that are credible. For example, an absolute threshold of the confidence can be set, and the respiratory rate BPM of the window respiratory signal with a confidence C higher than the absolute threshold is determined as credible data; or a relative threshold method can be used, such as determining that a certain proportion of the respiratory rate BPM of the window respiratory signal with a relatively higher confidence is credible data.

[0075] Based on the screening method of this embodiment, the disturbed window data can be discarded in the subsequent steps, and the respiratory rate BPM of multiple window respiratory signals with high credibility can be used to generate the probability density information P i , making the probability density information of a single monitoring period more accurate, and thus making the probability density baseline information B more accurate.

[0076] Further, step S21 may include:

[0077] S211, determine the maximum energy value and its corresponding frequency value in the spectrum information to obtain the highest peak point. Regardless of whether the window breathing signal is disturbed (whether there are multiple obvious peaks), the highest energy value can be found therein.

[0078] S212, extracting energy values ​​of data points within a first range centered on the highest peak point, and energy values ​​of data points within a second range centered on the highest peak point. For example, the first range is a frequency band close to the highest peak point, and the second range can be a relatively larger frequency band (the second range includes the first range), or a relatively larger frequency band minus the frequency band of the first range, thereby obtaining two segments of energy data.

[0079] S213, using the extracted energy values ​​to calculate confidence C. If the energy values ​​in the first range are significantly higher than the energy values ​​in the second range, it means that the highest peak point is the only obvious peak point, otherwise it means that there are other slightly lower peak points near the highest peak point.

[0080] As an example, the calculation can be performed as follows:

[0081] ;

[0082] Where C is the confidence level, P i is the energy value corresponding to the i-th frequency point in the spectrum, max is the frequency point corresponding to the energy peak point in the spectrum, L is the half-width of the main lobe interval (preset parameter, corresponding to the above-mentioned first range), A is the half-width of the side lobe interval (preset parameter, corresponding to the above-mentioned second range), and the larger the C value is, the more stable the waveform breathing characteristics in the window are, and the higher the confidence level is.

[0083] Further, step S22 may include:

[0084] S221, extracting frequency values ​​between a preset upper frequency limit and a preset lower frequency limit in the spectrum information. The maximum respiratory cycle and the minimum respiratory cycle that match the patient (such as the upper and lower limits of a human respiratory cycle) can be preset, and the upper and lower frequency limits are obtained according to the cycle.

[0085] S222, determine the frequency of the peak point with the maximum frequency value in the extracted frequency values. In the spectrum information obtained in step S21, only the energy peak point is extracted within this frequency range.

[0086] This embodiment extracts a reasonable energy peak value from the spectrum information by setting the upper and lower frequency limits, thereby avoiding interference to the respiratory signal due to the patient's body movement or environmental factors, and improving the reliability of the respiratory rate of the respiratory signal in each window.

[0087] Based on the above embodiment of calculating the confidence level, in one embodiment, step S4 may include the following operations:

[0088] S41A, calculating the coefficient w according to the number of credible multiple window breathing signals and the number of all window breathing signals in a single monitoring period i , where the number of credible multiple window breathing signals and the coefficient w i According to the above embodiment, the total number of windows in each monitoring period is the same, but the number of credible multiple window respiratory signals may be different, then the corresponding coefficient w i There are many specific calculation methods, as long as they can reflect the above quantity and coefficient w i For example, the coefficient w i It can be the result of dividing the number of credible window breathing signals on a certain day by the number of all window breathing signals on that day (the proportion of credible windows).

[0089] S42A, using the probability density information P of each monitoring period i and the corresponding coefficient w i Get the probability density baseline information B. Coefficient w i Used to measure the probability density information P of each monitoring period i The weight in generating the probability density baseline information B, the higher the coefficient of a certain monitoring period, the greater the impact / contribution to generating the probability density baseline information B, thereby enabling the generated result to more accurately reflect the patient's long-term respiratory state and reduce the impact of unstable respiratory state on a certain day on the baseline.

[0090] As an example, the probability density baseline information B can be calculated as follows:

[0091]

[0092] Among them, B is the probability density baseline information, P i Table is the probability density information of the i-th monitoring period, w i Table is the coefficient of the ith monitoring period, and N represents the number of monitoring periods.

[0093] In one embodiment, in step S3, the probability density information P of the breathing rate BPM of the breathing signals in the multiple windows is calculated. i , further comprising the following operations:

[0094] S31, using the kernel function K to obtain the one-dimensional probability density function of the respiratory rate BPM of the respiratory signals in multiple windows. This embodiment adopts the kernel density estimation method, as an example, the one-dimensional probability density function can be

[0095] ;

[0096] Where N is the number of data points (the number of windows / the number of breathing rate BPM), h is the bandwidth (i.e. the preset width of the kernel function), and x is the i is the value of the data point (the respiratory rate of the respiratory signal in the i-th window), and x is the one-dimensional probability density function P (x) The independent variable, K(u) is the kernel function. There are many optional kernel functions, usually the Gaussian kernel function can be used:

[0097] ;

[0098] S32, normalize the one-dimensional probability density function to obtain probability density information P i , which includes the values ​​of each respiratory rate and its corresponding probability value. As mentioned above, the probability density information P i It can be understood as a curve data, in which the higher the probability value corresponding to a certain respiratory rate value, the more window data with this respiratory rate there are in all the window data in a monitoring period.

[0099] This embodiment uses the kernel density estimation method to calculate the probability density curve, which has the advantages of non-parametric, smoothness, visualization, and density estimation.

[0100] In one embodiment, the above step S4 further comprises:

[0101] S41B, respectively determine the probability density information P in multiple monitoring periods i The eigenvalues ​​of X i , and calculate the average eigenvalue X mean .

[0102] Eigenvalue X i It can be the probability density information P iA certain breathing rate in the probability density information P i There may be one or more obvious probability peaks in the eigenvalue X i It can be the breathing rate corresponding to the peak probability, where the peak probability can be the maximum probability value (unique) or multiple relatively large probability values ​​(multiple peaks), that is, the eigenvalue X i It can be one or more. Eigenvalue X i It can also be used to measure the probability density information P i One or more indicators of the data, such as skewness and kurtosis, etc.

[0103] Average eigenvalue X mean It can be the characteristic value X of multiple monitoring periods i The arithmetic mean, weighted mean, etc. of the average eigenvalue X mean It reflects the patient's long-term respiratory characteristics.

[0104] S42B, calculate the characteristic value X in each monitoring period respectively i With the average eigenvalue X mean The difference between the two is calculated, and the stability information bias of multiple monitoring periods is obtained based on all the differences. The characteristic value X in a certain monitoring period i With the average eigenvalue X mean The greater the difference, the more abnormal the patient's breathing state is during that period. For example, if the patient is monitored daily for one month, the daily characteristic value X i ; Daily characteristic value X i With the average eigenvalue X mean The difference of a single day is obtained by subtracting the difference, and the difference of a month is accumulated to obtain the stability information bias, which reflects the stability of the patient's long-term respiratory state.

[0105] The data type of the stability information bias depends on the eigenvalue X i , which can be one or more values, specifically the difference value of respiratory rate, the difference value of probability density curve index, etc.

[0106] It should be noted that the difference described in this embodiment may be a difference, and in addition to the difference, it may at least be similarity information, and the higher the similarity, the smaller the difference.

[0107] S43B, determine whether the stability information bias exceeds the full-time stability threshold. In this embodiment, the so-called "exceed" can be greater than or less than. If the stability information bias is based on the result obtained by difference, the larger the stability information bias is, the more unstable the breathing state is; if the stability information bias is based on the result obtained by similarity, the smaller the stability information bias is, the more unstable the breathing state is.

[0108] S44B, if the stability information bias does not exceed the full-time stability threshold, the probability density baseline information B is generated using the probability density information of the monitoring period whose difference does not exceed the single-time period stability threshold.

[0109] If the stability information bias exceeds the full-time stability threshold, it means that the patient's long-term breathing state is inherently unstable, and the data during these periods are not suitable for generating a baseline. You can use data from other monitoring times to re-judge, or analyze the reasons for the instability.

[0110] If the stability information bias does not exceed the full-time stability threshold, it means that the patient's long-term breathing state is relatively stable, but the data can be further filtered. As mentioned above, the characteristic value X can be calculated for each monitoring period. i With the average eigenvalue X mean In this embodiment, a single-day threshold (single-period stability threshold) is further set. If the difference on a certain day exceeds the single-day threshold, the probability density information of the day is discarded.

[0111] As an example, suppose there are 30 days (monitoring period) of respiratory signals, corresponding to 30 sets of eigenvalues ​​X 1~ X 30 Its average eigenvalue is X mean30 , further calculate the stability information bias; if the bias exceeds the full-time stability threshold Y 30 Continue, otherwise it means that the data of these 30 days or the patient is not suitable for respiratory monitoring using this scheme. Then judge based on X1~X 30 and X mean30 The stability information obtained is bias1~bias 30 Whether it exceeds the single period stability threshold Y i , take the data that does not exceed the probability density baseline information B, assuming that there are X3, X 19 , X 20 Corresponding bias3, bias 19 、bias 20 Exceeds Y i , then the data of these 3 days are enabled, and the probability density baseline information B is generated based on the probability density information of the remaining 27 days of respiratory signals 27 .

[0112] It should be noted that the operation of step S44B of this embodiment can be combined with the operations of steps S41A to S42A in the above embodiment, and the coefficient w is further calculated for the probability density information of the monitoring period whose difference does not exceed the single period stability threshold. i , and combined with the probability density information P iand the corresponding coefficient w i Generate probability density baseline information B.

[0113] In one embodiment, in step S5, the deviation feature b (deviation feature value b1) is obtained in the following manner:

[0114] Determine the respiratory rate B corresponding to the maximum probability in the probability density baseline information B max , and the probability density information P during the target monitoring period j Determine the respiratory rate P corresponding to the maximum probability max , using the respiratory rate B max and respiratory rate P max Get the deviation characteristic value b1, which can be b1=P max -B max . Respiration rate B max It can be understood as the patient's most common long-term respiratory rate, respiratory rate P max It can be understood as the respiratory rate that the patient most often presents on a single day, so b1 can be regarded as the deviation or fluctuation of the patient's respiratory rate on that day relative to his long-term respiratory rate. b1 reflects the amplitude of the respiratory rate change on that day, indicating the difference between the respiratory rate on that day and his baseline respiratory rate.

[0115] In one embodiment, in step S5, the deviation feature b (deviation feature value b2) is obtained in the following manner:

[0116] The probability density baseline information B and the probability density information P of the target monitoring period are respectively j Extract the probability value B corresponding to the same respiratory rate value x x and P x , and calculate the difference between each pair of probability values ​​extracted; the difference between each pair of probability values ​​is accumulated to obtain the deviation characteristic value b2. As a reference, the calculation formula is used:

[0117] ;

[0118] bpm min Represents probability density information P j and the minimum value of the respiratory rate in the probability density baseline information B, bpm max Represents probability density information P j And the maximum value of the respiratory rate in the probability density baseline information B.

[0119] b2 is a comprehensive characteristic value that describes the difference between the respiratory rate distribution during the target monitoring period and the baseline distribution, and is used to evaluate the stability and change trend of the respiratory rate.

[0120] b1 and b2 in the above two embodiments can be used either selectively or simultaneously.

[0121] Further, step S6 includes the following operations:

[0122] Determine whether the deviation characteristic value b1 and / or the deviation characteristic value b2 exceeds the set threshold; when the deviation characteristic value b1 and / or the deviation characteristic value b2 exceeds the set threshold, determine that the patient's respiratory state in the target monitoring period is abnormal.

[0123] If b1 is large, it means that the patient's respiratory rate during the monitoring period has increased significantly compared to the baseline respiratory rate, which may indicate a change in some physiological or health status.

[0124] If b2 is large, it means that the patient's respiratory rate distribution during the monitoring period is significantly different from the baseline, which may indicate that the patient's breathing pattern has changed, which may be due to factors such as health status, activity level, and environment.

[0125] According to the respiratory status monitoring scheme provided by the present application, first, the respiratory status monitoring period can be set, and a sliding window can be performed for the respiratory signal in each monitoring period to determine the respiratory rate of each window respectively, and the probability density information of the respiratory rate in a single monitoring period can be calculated, thereby obtaining the probability distribution of the respiratory rate of the monitored object in each monitoring period; then, for this monitored object, its personalized probability density baseline information is generated using its long-term breathing rate probability distribution; finally, for a certain period that needs to be determined, by comparing the probability density information of the respiratory rate in this period with its personalized probability density baseline information, it is determined whether the respiratory state of the monitored object deviates from its long-term respiratory state, thereby improving the accuracy of the respiratory state determination result; and this scheme does not need to rely on other vital sign signals, and performs non-contact, long-term respiratory status monitoring based on millimeter-wave radar equipment, which has high convenience and practicality.

[0126] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A patient status monitoring system suitable for a ward, characterized in that: It includes acquisition module, processing module and display module, among which The acquisition module includes a first millimeter wave radar device and a second millimeter wave radar device, wherein the first millimeter wave radar device is used to acquire a first echo signal of a bed area in the ward, and the second millimeter wave radar device is used to acquire a second echo signal of a bathroom area in the ward; The processing module is used to determine the patient's sleep information, breathing information, and heart rate information according to the first echo signal, and determine the patient's behavior information according to the second echo signal, and then perform operations including the following: determining work and rest regularity information according to the sleep information, and judging whether the patient has left the bed area for a long time according to the sleep information, and outputting alarm information if the patient has left the bed area for a long time; The respiratory information is compared with the patient's respiratory baseline information to determine whether the patient's respiratory state is normal, and an alarm message is output when the respiratory state is abnormal, wherein the respiratory information includes the respiratory rate BPM, and the processing module is used to determine the probability density information P of the respiratory rate BPM. i The respiratory baseline information is obtained by using the probability density information P in multiple monitoring periods of the same patient. i The generated probability density baseline information B includes respectively determining the characteristic values ​​Xi of the probability density information Pi in a plurality of the monitoring time periods, and calculating the average characteristic value Xmean, respectively calculating the difference between the characteristic value Xi and the average characteristic value Xmean in each of the monitoring time periods, and obtaining the stability information bias of the monitoring time period from all the differences, and judging whether the stability information bias exceeds the stability threshold of the entire time period; if the stability information bias does not exceed the stability threshold of the entire time period, then using the probability density information of the monitoring time period whose difference does not exceed the stability threshold of a single time period to generate the probability density baseline information B; The processing module is used to convert the probability density information P of the patient in the target monitoring period j Compare with the probability density baseline information B to obtain the deviation feature b, and then determine whether the patient's respiratory state in the target monitoring period is normal according to the deviation feature b; determining whether the patient presents a tidal breathing pattern according to the breathing information, and outputting an alarm message when the patient presents a tidal breathing pattern; Determine whether the heart rate information is within a preset range, and output an alarm message when the heart rate information is outside the preset range; Determine whether the patient stays for a long time or falls according to the behavior information, and output alarm information when the patient stays for a long time or falls; The display module is used to display real-time data and post-processing data, and to receive and display the alarm information, wherein the real-time data includes the sleep information, the work and rest regularity information, the breathing information, the heart rate information, and the behavior information, and the post-processing data is statistical data generated based on the real-time data over a period of time.

2. The system according to claim 1, characterized in that The acquisition module also includes a temperature sensor for monitoring the patient's body temperature and / or a blood pressure meter for measuring the patient's blood pressure; the processing module is also used to generate the patient's vital signs data based on the patient's body temperature and / or the patient's blood pressure, the respiratory information and the heart rate information; the display module is used to display the patient's vital signs data and its statistical data.

3. The system according to claim 1, characterized in that The acquisition module also includes a sensor for collecting the temperature and / or humidity of the bathroom area in the ward; the processing module is also used to determine whether the temperature and / or humidity exceeds a preset range, and output an alarm message when the temperature and / or humidity exceeds the preset range.

4. The system according to claim 1, characterized in that The sleep information includes the time of going to bed, the time of falling asleep, the time of waking up in the morning, the time of getting up, and the number and time of leaving the bed at night.

5. The system according to claim 1, characterized in that The respiratory information includes a respiratory cycle and a respiratory action amplitude; the processing module determines whether the patient presents a tidal breathing pattern according to the respiratory cycle and the respiratory action amplitude, and intercepts the respiratory information corresponding to the tidal breathing pattern; The display module is used to display the breathing information corresponding to the Cheyne-Stokes breathing pattern.

6. The system according to claim 1, characterized in that The processing module is used to determine the respiratory rate B corresponding to the maximum probability value in the probability density baseline information B max , and the probability density information P during the target monitoring period j Determine the respiratory rate P corresponding to the maximum probability max , using the respiratory rate B max and respiratory rate P max Obtaining a deviation characteristic value b1; and / or The probability density baseline information B and the probability density information P of the target monitoring period are respectively j Extract the probability value B corresponding to the same respiratory rate value x x and P x , and calculate the difference between each pair of probability values ​​extracted; The differences between each pair of probability values ​​are accumulated to obtain the deviation characteristic value b2.

7. The system according to claim 6, characterized in that The processing module is used to determine whether the deviation characteristic value b1 and / or the deviation characteristic value b2 exceeds a set threshold value, and when the deviation characteristic value b1 and / or the deviation characteristic value b2 exceeds the set threshold value, it is determined that the patient's respiratory state during the target monitoring period is abnormal.

8. The system according to claim 1, characterized in that The processing module is also used to identify apnea events and hypopnea events according to the respiratory information, and calculate the apnea index according to the number of apnea events and the number of hypopnea events in a unit time; the display module is used to display the apnea index and its statistical data.

9. The system according to claim 1, characterized in that The behavior information is used to indicate whether the patient is in the bathroom area and whether normal movements and falling movements occur.

10. The system according to claim 9, characterized in that The normal actions include washing and going to the toilet.

11. The system according to claim 1, characterized in that The display module includes a display device arranged in the ward, a display device of the ward management center and a mobile display device, wherein the display device in the ward is used to display the real-time data and post-processed data of the ward, and the display device of the ward management center and the mobile display device are used to display the real-time data and post-processed data of all wards.

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