阅读说明：本技术 一种呼吸正异常识别方法及装置 (Method and device for identifying breathing positive abnormality ) 是由 孙琪 余星光 张婷 于 2021-09-30 设计创作，主要内容包括：本发明公开了一种呼吸正异常识别方法及装置,属于睡眠监控技术领域,目的在于克服现有睡眠异常检测较为不便的缺陷。压力传感器获取原始振动数据,并传递给处理器；原始振动数据加入定长信号队列,以得到在离床结果；若判断在床状态,对信号队列中连续若干个信号值进行判断,以得到体动状态结果；若判断为平静状态,则对原始振动数据进行分析处理以获得睡眠状态参数,将睡眠状态参数与标准呼吸参数进行比对,以得到呼吸正异常判断结果。通过床垫上的压力传感器获取呼吸信息,并通过呼吸包络信息判断呼吸是否异常,准确性较高,抗干扰性强,且无需随身佩戴检测设备,不会对被监测对象行动造成限制,具有较好的被监测对象体验。(The invention discloses a method and a device for identifying abnormal breathing, belongs to the technical field of sleep monitoring, and aims to overcome the defect that the conventional sleep abnormity detection is inconvenient. The pressure sensor acquires original vibration data and transmits the original vibration data to the processor; adding original vibration data into a fixed-length signal queue to obtain an in-bed result; if the bed state is judged, judging a plurality of continuous signal values in the signal queue to obtain a body motion state result; if the judgment result is a calm state, analyzing and processing the original vibration data to obtain a sleep state parameter, and comparing the sleep state parameter with a standard breathing parameter to obtain a breathing positive and abnormal judgment result. The breathing information is acquired through the pressure sensor on the mattress, whether breathing is abnormal or not is judged through breathing envelope information, the accuracy is high, the anti-interference performance is high, the detection equipment does not need to be worn with a user, the action of the monitored object is not limited, and the monitored object has good experience.)

1. A method for recognizing breathing abnormality is based on a mattress, a pressure sensor and a processor, wherein the pressure sensor is arranged at a position 40-60cm away from a bed head and corresponds to the position of a lung, and the method for recognizing the breathing abnormality comprises the following steps:

the pressure sensor acquires original vibration data and transmits the original vibration data to the processor;

adding original vibration data into a fixed-length signal queue, and performing sectional calculation on the signal queue through a sliding data window to obtain an in-bed result;

if the bed state is judged, judging a plurality of continuous signal values in the signal queue to obtain a body motion state result;

if the judgment result is a calm state, analyzing and processing the original vibration data to obtain a sleep state parameter, and comparing the sleep state parameter with a standard breathing parameter to obtain a breathing positive and abnormal judgment result.

2. The method for identifying respiratory positive anomalies according to claim 1, wherein the method is characterized in that when the difference between the maximum value and the minimum value of the window signal queue obtained by calculating the signal queue in sections through the sliding data window and the signal base value is less than or equal to 0.05, the patient is judged to be in the bed leaving state, otherwise, the patient is judged to be in the bed leaving state.

3. The method as claimed in claim 1, wherein the breathing positive abnormality is determined as a body movement state when a plurality of consecutive signal values in the signal queue are equal and greater than 3 or less than 0.5, otherwise, the breathing positive abnormality is determined as a calm state.

4. The method for identifying positive respiratory anomaly according to claim 1, wherein a pressure sensor is used to obtain a respiratory pressure analog signal, and an AD converter is used to convert the respiratory pressure analog signal into raw vibration data S1 (h);

the analysis processing of the raw vibration data S1(h) includes the steps of:

performing band-pass filtering on the original vibration data S1(h) to obtain a digital signal set S2 (h); a plurality of maximum values and minimum values in the digital signal set S2(h) are obtained through a successive approximation method, two adjacent minimum values or maximum values are marked as a breathing envelope containing M data, and N breathing envelopes are marked.

5. The method for identifying respiratory positive abnormality according to claim 4, wherein the band-pass filtering processing is performed on the raw vibration data S1(h) by the following formula to obtain a digital signal set S2 (h):

S2(h)＝[0.54-0.46cos(2πh/H)]*S1(h)，0≤h≤H。

6. the method for recognizing the positive respiratory abnormality according to claim 4, wherein the pressure sensor is used for detecting whether the monitored object is calmly on the mattress for a preset time, and when the monitored object is on the mattress for the preset time, the data analysis module enters a calibration mode to start to acquire standard respiratory parameters; when the monitored object is on the mattress for more than the preset time, the data analysis module enters a detection mode and starts to acquire the sleep state parameters.

7. The method for identifying respiratory positive anomalies according to claim 6, wherein the respiratory envelopes obtained in the calibration mode are standard respiratory envelopes, each standard respiratory envelope is denoted as Signal (N), 0 ≦ N ≦ N, the standard respiratory envelope with the least amount of data among N standard respiratory envelopes is obtained, the least amount of data of one standard respiratory envelope is obtained as M, M ≦ M, the amount of data of each standard respiratory envelope is normalized as M, and the N respiratory envelopes are averaged to obtain Signal _ mean (i) (. Sigma [ N ] [ i ])/N, 0 ≦ i ≦ M;

calculating the mean square error of each standard respiratory envelope and Signal _ mean (i) to obtain a standard respiratory parameter STD:

STD＝(Σ(Signal[n][i]-Signal_Mean(i))²)/m，0≤i≤m。

8. the method of claim 7, wherein the breathing envelopes obtained in the detection mode are detected breathing envelopes, each detected breathing envelope is represented as test _ Signal (N), N is greater than or equal to 0 and less than or equal to N, the detected breathing envelope with the least amount of data among the N detected breathing envelopes is obtained, the least amount of data of one detected breathing envelope is M and M is less than or equal to M, the amount of data of each detected breathing envelope is normalized to M, and the N breathing envelopes are averaged to obtain test _ Signal _ mean (i) (Σ Signal [ N ] [ i ])/N, i is greater than or equal to 0 and less than or equal to M;

calculating the mean square error of each detected respiratory envelope and test _ Signal _ mean (i) to obtain a sleep state parameter test _ STD:

test_STD＝(Σ(Signal[n][i]-test_Signal_Mean(i))²)/m，0≤i≤m；

and when the test _ STD/STD is less than or equal to K and the K is less than 1, judging the respiratory abnormality.

9. The method of claim 8, wherein the breathing envelopes obtained in the detection mode are detected breathing envelopes, each detected breathing envelope is decomposed into an inhalation envelope V1(n) and an exhalation envelope V2(n), and when ^ V1(n) > V2(n) and test _ STD/STD ≤ K, K < 1, the breathing abnormality is determined.

10. A device for identifying positive respiratory abnormalities, comprising:

the data acquisition module is used for acquiring original vibration data;

the on-bed judgment module is used for judging whether the monitored object is in the on-bed state according to the original vibration data;

the body motion judging module is used for judging whether the body motion exists in the monitored object according to the original vibration data;

the data analysis module is used for acquiring sleep state parameters of the monitored object according to the original vibration data;

and the breathing positive abnormity identification module is used for judging whether the breathing of the monitored object is normal.

Technical Field

The invention belongs to the technical field of sleep monitoring, and relates to a method for identifying abnormal breathing.

Background

The sleeping time occupies one third of the life of human beings, and the influence of the sleeping quality on the health of the human bodies is huge. In the sleeping state of a human body, changes of central nerves, various organs of the body, a cardiovascular system, respiratory rhythm and the like directly reflect the health degree of the human body. The effective and accurate monitoring and recording of the sleep state has practical significance on the health state of the human body. The abnormal breathing state in the sleep state is monitored, the health problems of the human body can be found in time, and the intervention can be performed quickly, so that the sleep quality and the health level are improved.

The common sleep abnormal breathing monitoring methods at present include sound detection, airflow detection, blood oxygen detection and the like.

The sound detection is not high in detection accuracy due to various noise conditions of the use environment, and the radio equipment needs to be externally arranged at a position close to the monitored object, so that the directivity and the anti-interference performance are poor.

The accuracy of air current detection and blood oxygen detection is higher, but because need the monitored object to wear corresponding equipment next to the shin, cause inconvenience to the monitored object action, influence monitored object and experience.

Disclosure of Invention

The invention provides a method for identifying abnormal breathing aiming at the problems in the prior art and aims to overcome the defect that the conventional sleep abnormity detection is inconvenient.

The invention is realized by the following steps:

a method for recognizing breathing abnormality is based on a mattress, a pressure sensor and a processor, wherein the pressure sensor is arranged at a position 40-60cm away from a bed head and corresponds to the position of a lung, and the method for recognizing the breathing abnormality comprises the following steps:

the pressure sensor acquires original vibration data and transmits the original vibration data to the processor;

adding original vibration data into a fixed-length signal queue, and performing sectional calculation on the signal queue through a sliding data window to obtain an in-bed result;

if the bed state is judged, judging a plurality of continuous signal values in the signal queue to obtain a body motion state result;

if the judgment result is a calm state, analyzing and processing the original vibration data to obtain a sleep state parameter, and comparing the sleep state parameter with a standard breathing parameter to obtain a breathing positive and abnormal judgment result.

And when the sliding data window carries out sectional calculation on the signal queue to obtain the difference value between the maximum value and the minimum value of the window signal queue and the signal base value, the signal queue is judged to be in the out-of-bed state, and otherwise, the signal queue is judged to be in the in-bed state.

And when a plurality of continuous signal values in the signal queue are equal and are more than 3 or less than 0.5, judging the signal queue to be in a body motion state, otherwise, judging the signal queue to be in a calm state.

Acquiring a respiratory pressure analog signal through a pressure sensor, and converting the respiratory pressure analog signal into original vibration data S1(h) through an AD converter;

the analysis processing of the raw vibration data S1(h) includes the steps of:

performing band-pass filtering on the original vibration data S1(h) to obtain a digital signal set S2 (h); a plurality of maximum values and minimum values in the digital signal set S2(h) are obtained through a successive approximation method, two adjacent minimum values or maximum values are marked as a breathing envelope containing M data, and N breathing envelopes are marked.

The original vibration data S1(h) is subjected to band-pass filtering processing by the following formula to obtain a digital signal set S2 (h):

S2(h)＝[0.54-0.46cos(2πh/H)]*S1(h)，0≤h≤H。

detecting whether the monitored object is calmly on the mattress for a preset time through a pressure sensor, and when the monitored object is on the mattress for the preset time, entering a calibration mode by a data analysis module to start to acquire standard breathing parameters; when the monitored object is on the mattress for more than the preset time, the data analysis module enters a detection mode and starts to acquire the sleep state parameters.

The preset time is 15-90 s.

The method comprises the steps that a respiratory envelope acquired in a calibration mode is a standard respiratory envelope, each standard respiratory envelope is marked as Signal (N), N is more than or equal to 0 and less than or equal to N, the standard respiratory envelope with the minimum data volume in N standard respiratory envelopes is acquired, the minimum data volume of one standard respiratory envelope is M, M is less than or equal to M, the data volume of each standard respiratory envelope is normalized to M, N respiratory envelopes are averaged to obtain Signal _ mean (i) (sigma Signal [ N ] [ i ])/N, and i is more than or equal to 0 and less than or equal to M;

calculating the mean square error of each standard respiratory envelope and Signal _ mean (i) to obtain a standard respiratory parameter STD:

STD＝(Σ(Signal[n][i]-Signal_Mean(i))²)/m，0≤i≤m。

the method comprises the steps that a breathing envelope acquired in a detection mode is a detected breathing envelope, each detected breathing envelope is recorded as test _ Signal (N), N is more than or equal to 0 and less than or equal to N, the detected breathing envelope with the minimum data volume in N detected breathing envelopes is acquired, the minimum data volume of one detected breathing envelope is M, M is less than or equal to M, the data volume of each detected breathing envelope is normalized to M, the N breathing envelopes are averaged to obtain test _ Signal _ mean (i) ((Sigma Signal [ N ] [ i ])/N), and i is more than or equal to 0 and less than or equal to M;

calculating the mean square error of each detected respiratory envelope and test _ Signal _ mean (i) to obtain a sleep state parameter test _ STD:

test_STD＝(Σ(Signal[n][i]-test_Signal_Mean(i))²)/m，0≤i≤m；

and when the test _ STD/STD is less than or equal to K and the K is less than 1, judging the respiratory abnormality.

The breathing envelope acquired in the detection mode is a detected breathing envelope, each detected breathing envelope is decomposed into an inspiration envelope V1(n) and an expiration envelope V2(n), and abnormal breathing is judged when ^ V1(n) > ^ V2(n) and test _ STD/STD is less than or equal to K and K is less than 1.

K is 0.4-0.8.

A device for identifying positive respiratory abnormalities, comprising:

the data acquisition module is used for acquiring original vibration data;

the on-bed judgment module is used for judging whether the monitored object is in the on-bed state according to the original vibration data;

the body motion judging module is used for judging whether the body motion exists in the monitored object according to the original vibration data;

the data analysis module is used for acquiring sleep state parameters of the monitored object according to the original vibration data;

and the breathing positive abnormity identification module is used for judging whether the breathing of the monitored object is normal.

According to the method for identifying the abnormal breathing, the original vibration data is obtained through the pressure sensor on the mattress, whether the breathing is abnormal or not is judged through the sleep state parameters, the accuracy is high, the anti-interference performance is high, the detection equipment does not need to be worn with a user, the action of the monitored object is not limited, and the monitored object experience is good.

Drawings

FIG. 1 is a schematic diagram of a waveform of an out-of-bed condition;

FIG. 2 is a schematic diagram of a waveform from an on-bed state to an off-bed state;

fig. 3 is a schematic diagram of waveforms when the body motion state of the object exists;

fig. 4 is a schematic diagram of a normal respiration waveform of a monitored subject;

FIG. 5 is a schematic diagram of the respiration waveform of a monitored subject during snoring;

FIG. 6 is a schematic view of the installation position of the pressure sensor;

FIG. 7 is a flow chart of an identification method;

FIG. 8 is a schematic diagram of a module relationship in an identification device.

Reference is made to the accompanying drawings in which: 100. a mattress; 200. a pressure sensor; 300. a processor.

Detailed Description

The following detailed description of the embodiments of the present invention is provided in connection with the accompanying drawings for the purpose of facilitating understanding and understanding of the technical solutions of the present invention. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The present embodiment provides a method and an apparatus for recognizing respiratory positive abnormality, the apparatus for recognizing respiratory positive abnormality includes a pressure sensor 200 and a processor 300, as shown in fig. 6, the pressure sensor 200 is disposed on a mattress 100 at a distance of 40-60cm from a bed head, and when a monitored subject normally lies on the mattress 100, the position of the pressure sensor 200 corresponds to a lung. The pressure sensor 200 is a piezoelectric film type band sensor, the pressure sensor 200 is connected with the processor 300, and the processor 300 is arranged at the side of the mattress 100 to avoid affecting the human body.

As shown in fig. 8, the processor 300 further includes:

the data acquisition module is used for acquiring original vibration data;

the on-bed judgment module is used for judging whether the monitored object is in the on-bed state according to the original vibration data;

the body motion judging module is used for judging whether the body motion exists in the monitored object according to the original vibration data;

the data analysis module is used for acquiring sleep state parameters of the monitored object according to the original vibration data;

and the breathing positive abnormity identification module is used for judging whether the breathing of the monitored object is normal.

As shown in fig. 7, the identification method mainly includes the following steps:

the method comprises the steps of firstly, acquiring original vibration data through a pressure sensor, and sending the original vibration data to a data acquisition module on a processor.

The respiratory pressure analog signal is acquired by the pressure sensor, and converted into raw vibration data S1(h) by the AD converter.

And secondly, adding the original vibration data into a fixed-length signal queue in a bed leaving judgment module, carrying out sectional calculation on the signal queue through a sliding data window to obtain a bed leaving result, and entering the third step when the monitored object is judged to be in a bed state.

As shown in fig. 1, when the sliding data window performs the segmented calculation on the signal queue to obtain that the difference between the maximum value and the minimum value of the window signal queue and the signal base value is less than or equal to 0.05, the out-of-bed state is determined. The pressure sensor may also detect slight vibrations when out of bed, for example, the pressure sensor detects vibrations of external sounds, which are usually slight.

As shown in fig. 2-5, when the sliding data window performs the segment calculation on the signal queue to obtain that the difference between the maximum value or the minimum value of the window signal queue and the signal base value is greater than 0.05, the bed-in state is determined. In fig. 2, the in-bed frame in the middle area indicates a sliding data window, and when the position in fig. 2 is calculated, the out-of-bed determination module determines that the object is in the in-bed state.

The respiration or the body movement of the human body can cause the great current or voltage change of the pressure sensor, so that whether the monitored object is in the bed can be accurately judged according to the signal value. The monitored object is further monitored when in bed, so that the power consumption of the processor can be effectively reduced, and the whole service life of the device can be prolonged.

Thirdly, as shown in fig. 3, a plurality of continuous signal values in the signal queue are judged to obtain a result of the body motion state.

And when a plurality of continuous signal values in the signal queue are equal and are more than 3 or less than 0.5, judging the signal queue to be in a body motion state, otherwise, judging the signal queue to be in a calm state. When the object moves, a continuously large pressure is applied to the pressure sensor, which is indicated by a large amplitude of the continuous signal value, and the continuous equal signal values are shown in fig. 3 because the signal values reach the continuous extremum.

And when a plurality of continuous signal values in the signal queue are different and are more than or equal to 0.5 and less than or equal to 3, judging that the signal queue is in a quiet state. The calm state may be a normal breathing state of the subject as shown in fig. 4, or may be a snoring state as shown in fig. 5.

When the monitored object is in a calm state, the original vibration data is further analyzed and processed, so that the influence of the body motion state data can be filtered, and the body motion state data is prevented from being processed into sleep state parameters or standard breathing parameters.

Fourthly, detecting whether the monitored object is calmly on the mattress 100 for a preset time through a pressure sensor, and when the monitored object is on the mattress 100 within the preset time, enabling a data analysis module to enter a calibration mode and start to acquire standard breathing parameters; when the monitored object is on the mattress for more than the preset time, the data analysis module enters a detection mode and starts to acquire the sleep state parameters. The preset time may be 15-90s, for example, 30s, and the pressure sensor detects that the monitored object is lying in the bed for 30 s.

In other optional embodiments, the method may also be customized for the monitored subject, the standard breathing parameter is prestored in the storage unit of the processor, and the processor does not need to enter the calibration mode, but directly enters the detection mode after detecting that the monitored subject is in the mattress for a preset time.

N breathing envelopes are marked in the standard mode. Specifically, the method comprises the steps of carrying out band-pass filtering on original vibration data S1(h) to obtain a digital signal set S2(h), obtaining a plurality of maximum values and minimum values in the digital signal set S2(h) through a successive approximation method, marking two adjacent minimum values or maximum values as a breathing envelope containing M data, marking N breathing envelopes, marking the breathing envelopes as standard breathing envelopes in a calibration mode, marking each standard breathing envelope as Signal (N), wherein N is more than or equal to 0 and less than or equal to N,

for example, if N is 15, there are 15 breathing envelopes, each of the 15 breathing envelopes contains M data, and the value of M may be different in different breathing envelopes, for example, M is 151 in the first breathing envelope Signal (1), M is 150 in the second breathing envelope Signal (2), M is 151 in the third breathing envelope Signal (3), M is 154 in the fourth breathing envelope Signal (4), and so on. The sum of the values of the 15 breathing envelopes M is the digital signal set S2 (h).

The original vibration data S1(h) is subjected to band-pass filtering processing by the following formula to obtain a digital signal set S2 (h):

S2(h)＝[0.54-0.46cos(2πh/H)]*S1(h)，0≤h≤H。

this allows taking out noise during signal transmission and conversion.

Obtaining a standard respiration envelope with the minimum data volume in N standard respiration envelopes, wherein the minimum data volume of one standard respiration envelope is M which is less than or equal to M, normalizing the data volume of each standard respiration envelope into M, and averaging the N respiration envelopes to obtain Signal _ mean (i) ((Sigma Signal [ N ] [ i ])/N), wherein i is more than or equal to 0 and less than or equal to M;

calculating the mean square error of each standard respiratory envelope and Signal _ mean (i) to obtain a standard respiratory parameter STD:

STD＝(Σ(Signal[n][i]-Signal_Mean(i))²)/m，0≤i≤m。

n breathing envelopes are marked in the detection mode. Specifically, a respiratory pressure analog signal is obtained through a pressure sensor, the respiratory pressure analog signal is converted into original vibration data S1(h) through an AD converter, a digital signal set S2(h) is obtained through band-pass filtering processing of the original vibration data S1(h), a plurality of maximum values and minimum values in the digital signal set S2(h) are obtained through a successive approximation method, two adjacent minimum values or maximum values are marked as a respiratory envelope containing M data, N respiratory envelopes are marked, the respiratory envelope obtained in a detection mode is a detected respiratory envelope, each detected respiratory envelope is marked as test _ Signal (N), N is more than or equal to 0 and less than or equal to N,

the original vibration data S1(h) is subjected to band-pass filtering processing by the following formula to obtain a digital signal set S2 (h):

S2(h)＝[0.54-0.46cos(2πh/H)]*S1(h)，0≤h≤H。

obtaining a standard respiratory envelope with the minimum data volume in N detected respiratory envelopes, obtaining the minimum data volume of one detected respiratory envelope as M, wherein M is less than or equal to M, normalizing the data volume of each detected respiratory envelope as M, and averaging the N respiratory envelopes to obtain test _ Signal _ mean (i) (∑ Signal [ N ] [ i ])/N, wherein i is less than or equal to 0 and less than or equal to M;

calculating the mean square error of each detected respiratory envelope and test _ Signal _ mean (i) to obtain a sleep state parameter test _ STD:

test_STD＝(Σ(Signal[n][i]-test_Signal_Mean(i))²)/m，0≤i≤m。

therefore, the sleep state parameters of the monitored object in the sleep state are acquired through the pressure sensor.

And fifthly, comparing the sleep state parameter with the standard breathing parameter by the breathing positive and abnormal identification module, and judging breathing abnormality when test _ STD/STD is less than or equal to K and K is less than 1. Namely, when the preset range of the comparison result of the sleep state parameter and the standard respiratory parameter is test _ STD/STD & gt K, the respiration is normal within the preset range, K is 0.4-0.8, and K is preferably 0.6.

The human body has different degrees of breathing disorders during sleep, most of which are obstructive breathing disorders such as snoring. As shown in fig. 1-2, the ordinate of the graph is amplitude, the abscissa of the graph is time, and the breathing abnormality of the present embodiment is exemplified by snoring. Snoring is a partial or complete obstruction of the airway that causes the person to experience difficulty breathing and an obstruction to inspiration. The standard respiratory parameter and the sleep state parameter in the embodiment both include comprehensive information of amplitude, frequency and waveform, the amplitude corresponds to the exertion degree during breathing, the breathing exertion during snoring can be obviously increased, and the amplitude can be obviously increased relative to the amplitude under the standard condition.

In addition, as shown in fig. 6, the breathing envelope acquired in the detection mode is a detected breathing envelope, each detected breathing envelope is decomposed into an inhalation envelope V1(n) and an exhalation envelope V2(n), and when ^ V1(n) > [ ^ V2(n) and test _ STD/STD ═ K, K < 1, breathing abnormality is determined. Under normal breathing conditions, the integral of the inhalation envelope is usually smaller than the integral of the exhalation envelope, the ascending waveform in fig. 6 is inhalation, the descending waveform is exhalation, and when the integral of the inhalation envelope is larger than the integral of the exhalation envelope, the correctness of the respiratory abnormality judgment can be further checked, and the possibility of misjudgment is reduced.