WO2024066502A1

WO2024066502A1 - Respiration monitoring method and device

Info

Publication number: WO2024066502A1
Application number: PCT/CN2023/101243
Authority: WO
Inventors: 林秋培
Original assignee: 上海矽睿科技股份有限公司
Priority date: 2022-09-26
Filing date: 2023-06-20
Publication date: 2024-04-04
Also published as: CN115778366A

Abstract

Disclosed herein is a respiration monitoring method. After an acceleration sensor obtains acceleration signals, the acceleration signals are preprocessed to give one-dimensional signals. A plurality of one-dimensional signals are then compared and recorded, and a target signal is determined in the plurality of one-dimensional signals. After two occurrences of the target signal, the time interval between the two adjacent target signals is calculated, and a first peak value and a first peak-to-peak value in the one-dimensional signals within the time interval are obtained. Finally, the time interval, the first peak value, and the first peak-to-peak value are compared, and one respiration can be recorded if the requirement is met. The respiration monitoring method only requires placing the acceleration sensor on the chest or abdomen of a monitoring subject, obtaining acceleration signals caused by respiration by means of the acceleration sensor, and then processing the acceleration signals. Therefore, a device corresponding to the respiration monitoring method is simple in structure, easy and convenient to operate, and suitable for daily health monitoring. The present application further relates to a respiration monitoring device.

Description

Respiration monitoring method and device

Technical Field

The present application relates to the field of health monitoring technology, and in particular to a respiratory monitoring method and device.

Background technique

In recent years, with the improvement of living standards, more and more people have begun to pay attention to their physical health. As breathing is an important physiological process, people have an increasing demand for monitoring breathing. Respiration monitoring has many applications, such as providing care for critically ill patients. However, the current bedside respiratory monitoring equipment for critically ill patients is large in size and not suitable for daily monitoring.

Summary of the invention

The purpose of this application is to provide a respiratory monitoring method and device suitable for daily monitoring.

A respiratory monitoring method, comprising the steps of:

S110, sampling and acquiring acceleration signals of multiple different spatial axes caused by breathing through a three-axis acceleration sensor and preprocessing the acceleration signals to obtain a one-dimensional signal;

S120, comparing and recording the plurality of one-dimensional signals, and executing step S130 when a target signal appears;

S130, repeating steps S110 to S120, then calculating the time interval between two adjacent target signals, and obtaining the first peak value and the first peak-to-peak value of the one-dimensional signal within the time interval;

S140, comparing the time interval, the first peak value, and the first peak-to-peak value, and repeating steps S110 to S140;

If the time interval, the first peak value and the first peak-to-peak value meet the requirements, a breath is recorded.

By adopting the above-mentioned breathing monitoring method, after the acceleration sensor obtains the acceleration signal, the acceleration signal is preprocessed to obtain a one-dimensional signal, and then the multiple one-dimensional signals are compared and Record, determine the target signal among multiple one-dimensional signals, calculate the time interval between two adjacent target signals after two target signals appear, and obtain the first peak value and the first peak-to-peak value in the one-dimensional signal within the time interval, and finally compare the time interval, the first peak value and the first peak-to-peak value. If the requirements are met, a breath can be recorded.

Since the respiratory monitoring method only needs to place the acceleration sensor on the chest or abdomen of the monitored object, obtain the acceleration signal caused by breathing through the acceleration sensor, and then process the acceleration signal. Therefore, the device corresponding to the respiratory monitoring method has a simple structure and is easy to operate. The respiratory monitoring method is simple to calculate and convenient for daily operation. It is suitable for daily health monitoring in scenes or places such as families, infant care, and nursing homes.

At the same time, after calculating and obtaining the time interval, the first peak value and the first peak-to-peak value, the respiratory monitoring method will determine whether the three meet the requirements. As long as one of them does not meet the requirements, the number of breaths will not be recorded. Only after all three meet the requirements will a breath be recorded, avoiding misjudgment of breathing and ensuring monitoring accuracy.

In one embodiment, step S110 includes:

S111, sampling and acquiring the acceleration signals of the three axes caused by breathing through a three-axis acceleration sensor;

S112, removing the acceleration signal of an axis with the largest value;

S114, performing modulus calculation on the acceleration signals of the remaining two axes to obtain the one-dimensional signal.

In one embodiment, the steps between step S112 and step S114 further include:

S113, filtering the acceleration signals of the remaining two axes.

In one embodiment, the one-dimensional signal in step S114

Among them, a and b are the acceleration signals of the remaining two axes respectively.

In one embodiment, step S113 includes:

The acceleration signals of the remaining two axes are filtered by constructing a 0.2 Hz to 0.5 Hz low-pass filter.

In one embodiment, step S120 includes:

S121, calculating the difference between two adjacent one-dimensional signals;

S122, comparing the absolute value of the difference with the breathing threshold;

If the absolute value of the difference is greater than or equal to the breathing threshold, the one-dimensional signal obtained this time is retained and step S123 is executed; if the absolute value of the difference is less than the breathing threshold, the one-dimensional signal obtained this time is discarded, and a new one-dimensional signal is obtained and step S121 is repeated;

S123, record the retained one-dimensional signal;

S125, comparing two adjacent retained one-dimensional signals with a target threshold;

If the previous one-dimensional signal is greater than the target threshold and the current one-dimensional signal is less than or equal to the target threshold, the current one-dimensional signal is determined to be the target signal; otherwise, the current one-dimensional signal is determined not to be the target signal, a new one-dimensional signal is obtained and step S121 is repeated.

In one embodiment, the respiratory monitoring method further comprises the steps of:

S210, recording the number of samplings of the acceleration sensor by a sampling counter, and resetting it after the number of samplings reaches a sampling threshold, so as to obtain sampling data of a stage;

S220, obtaining the second peak-to-peak value in the sampling data of the previous stage, and calculating the respiratory threshold by formula (1);
Respiratory threshold = second peak value/m (1);

Here, m is an integer, and 4≤m≤32.

In one embodiment, the second peak-to-peak value=the second peak value-the second valley value;

The second peak value is the maximum value in the sampling data of the previous stage, and the second valley value is the minimum value in the sampling data of the previous stage.

In one embodiment, the steps between step S123 and step S125 further include:

S124, calculating the average value of the retained one-dimensional signal to obtain the target threshold.

In one embodiment, step S140 includes:

S141, comparing the time interval with a time lower limit threshold, if the time interval is greater than the time lower limit threshold, executing step S142, otherwise it is determined that the time interval does not meet the requirement;

S142, comparing the time interval with the time upper limit threshold, if the time interval is less than or equal to the time upper limit threshold, then executing step S143; otherwise, it is determined that the time interval does not meet the requirement;

S143, comparing the first peak value with the sum value and the first peak-to-peak value with the peak-to-peak value threshold; if the first peak value is greater than the sum value and the first peak-to-peak value is greater than the peak-to-peak value threshold, executing step S144; if the first peak value is less than or equal to the sum value and/or the first peak-to-peak value is less than or equal to the peak-to-peak value threshold, determining that the first peak value and/or the first peak-to-peak value do not meet the requirements;

S144, recording a breath by a breath counter;

The sum value is the sum of the target threshold and the peak threshold.

In one embodiment, step S142 further includes:

If the time interval is greater than the upper time limit threshold, a breathing interruption alarm is issued.

S310, recording the number of times the acceleration signal is obtained through a time counter;

S320, when it is determined that the target signal appears, the time counter is reset;

S330, repeat steps S310 to S320 to calculate the time interval between two adjacent target signals.

A respiratory monitoring device includes a processor and an acceleration sensor, a time counter and a respiratory counter electrically connected to the processor, wherein the acceleration sensor is used to obtain an acceleration signal, the processor is used to preprocess the acceleration signal to obtain multiple one-dimensional signals, and compare and record the multiple one-dimensional signals, the time counter is used to calculate the time interval, and the respiratory counter is used to record the number of breaths.

The respiratory monitoring device is used to execute the above-mentioned respiratory monitoring method, and therefore has all the beneficial effects of the above-mentioned respiratory monitoring method.

In one embodiment, the respiratory monitoring device further includes a low-pass filter electrically connected to the processor, and the low-pass filter is used to filter the acceleration signal.

In one embodiment, the respiratory monitoring device further includes a sampling counter. The counter is used to record the number of times the acceleration sensor samples.

In one of the embodiments, the respiratory monitoring device further comprises an alarm electrically connected to the processor, wherein the alarm is configured to issue a respiratory interruption alarm when the time interval is greater than an upper time limit threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a respiratory monitoring method provided by an embodiment of the present application;

FIG2 is a flow chart of step S110 in the respiratory monitoring method shown in FIG1 ;

FIG3 is a flow chart of step S120 in the respiratory monitoring method shown in FIG1 ;

FIG4 is a flow chart of calculating the breathing threshold in step S122 shown in FIG3 ;

FIG5 is a flow chart of step S140 in the respiratory monitoring method shown in FIG1 ;

FIG6 is a flow chart of calculating the time interval in the respiratory monitoring method shown in FIG1 ;

FIG. 7 is a flow chart of a respiratory monitoring method provided in a specific embodiment of the present application.

Detailed ways

In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

As shown in FIG1 , an embodiment of the present application provides a respiratory monitoring method, comprising the following steps:

S110, acquiring the acceleration of multiple different spatial axes caused by breathing through the acceleration sensor sampling Signal and preprocess the acceleration signal to obtain a one-dimensional signal.

S120, compare and record multiple one-dimensional signals, and execute step S130 when a target signal appears.

S130, repeating steps S110 to S120, then calculating the time interval between two adjacent target signals, and obtaining the first peak value and the first peak-to-peak value in the one-dimensional signal within the time interval.

S140, comparing the time interval, the first peak value, and the first peak-to-peak value.

If the time interval, the first peak value and the first peak-to-peak value meet the requirements, a breath is recorded; if at least one of the time interval, the first peak value and the first peak-to-peak value does not meet the requirements, the number of breaths will not be recorded.

By adopting the above-mentioned breathing monitoring method, after the acceleration sensor obtains the acceleration signal, the acceleration signal is first preprocessed to obtain a one-dimensional signal, and then multiple one-dimensional signals are compared and recorded, and the target signal is determined in the multiple one-dimensional signals. After two target signals appear, the time interval between two adjacent target signals is calculated, and the first peak value and the first peak-to-peak value in the one-dimensional signal within the time interval are obtained. Finally, the time interval, the first peak value and the first peak-to-peak value are compared respectively. If they meet the requirements, one breath can be recorded.

It is understandable that, in step S110, a plurality of single-axis acceleration sensors may be used for sampling, or a single-axis acceleration sensor and a dual-axis acceleration sensor may be used in combination for sampling. Alternatively, a three-axis acceleration sensor may be used for sampling. When a three-axis acceleration sensor is used for sampling, correspondingly, the acceleration signals of multiple spatial axes may be acceleration signals of mutually perpendicular X-axis, Y-axis, and Z-axis.

In addition, the multiple one-dimensional signals in step S120 are obtained by sampling the acceleration sensor multiple times in step S110 to obtain the acceleration signal and preprocessing the acceleration signal.

At the same time, it should be explained that after recording the one-dimensional signal that meets the requirements obtained by continuous sampling, a continuous and fluctuating curve will be obtained, which has alternating and continuous rising edges and falling edges. Through a preset comparison relationship, one of the one-dimensional signals is determined as the target signal, and there are peak values (maximum values) and valley values (minimum values) on the continuous rising edges and falling edges between two adjacent target signals, and the peak-to-peak value = peak value-valley value. In this way, in steps S130 and S140, the time interval between two adjacent target signals is calculated, and the first peak value and the first peak-to-peak value in the one-dimensional signal within the time interval are obtained. If the time interval, the first peak value and the first peak-to-peak value all meet the requirements, then a breath is recorded.

After completing a breathing record, steps S110 to S140 are repeated to continue monitoring and recording breathing. In step S140, if at least one of the time interval, the first peak value, and the first peak-to-peak value does not meet the requirements, steps S110 to S140 are also repeated.

In addition, when the respiratory monitoring method is applied, the monitored object preferably remains static, so as to avoid the acceleration sensor being disturbed by the acceleration during movement.

Please refer to FIG. 2 , in some embodiments, step S110 includes:

S111, acquiring acceleration signals of three axes caused by breathing through a three-axis acceleration sensor.

It is understandable that the three-axis acceleration sensor can be placed on the abdomen or chest of the monitored subject, and along with the breathing, abdomen or chest movements, acceleration signals of the three axes caused by breathing can be obtained.

S112, removing the acceleration signal of the axis with the largest value.

It needs to be explained that the acceleration signal caused by breathing is smaller than the acceleration signal caused by gravity, and among the three acceleration signals obtained by the three-axis acceleration sensor, there is one acceleration signal that is most affected by gravity. Therefore, the largest acceleration signal is removed to reduce the influence of gravity, improve the accuracy of the acceleration signal, and further improve the monitoring accuracy.

In addition, in order to avoid the overlap of the spatial axis with the greatest influence of gravity and the spatial axis with the greatest influence of respiration, the monitored subject should avoid lying flat when undergoing respiratory monitoring.

S114, performing modulus calculation on the acceleration signals of the remaining two axes to obtain a one-dimensional signal.

Specifically, one-dimensional signal = a and b are the acceleration signals of the remaining two axes respectively.

In some embodiments, the steps between step S112 and step S114 further include:

S113, filtering the acceleration signals of the remaining two axes to further remove interfering noise, such as noise generated by heartbeat, limb movement, etc., so as to further improve the monitoring accuracy.

Specifically, in this step, the acceleration signals of the remaining two axes may be filtered by constructing a 0.2 Hz to 0.5 Hz low-pass filter.

Please refer to FIG. 3 , in some embodiments, step S120 includes:

S121, calculating the difference between two adjacent one-dimensional signals.

S122, comparing the absolute value of the difference with the breathing threshold.

If the absolute value of the difference is greater than or equal to the breathing threshold, the one-dimensional signal obtained this time is retained and step S123 is executed; if the absolute value of the difference is less than the breathing threshold, the one-dimensional signal obtained this time is discarded, a new one-dimensional signal is obtained and step S121 is repeated.

S123, recording the retained one-dimensional signal to obtain rising edges and falling edges that are alternately and continuously distributed.

S125, comparing two adjacent retained one-dimensional signals with the target threshold.

It should be noted that if the absolute value of the difference is greater than or equal to the breathing threshold, it can be roughly determined that the one-dimensional signal obtained this time is a breathing signal and the record is retained, and then the next step is performed to continue to determine whether it is a target signal; if the absolute value of the difference is less than the breathing threshold, it can be determined that the one-dimensional signal obtained this time is not a breathing signal, the one-dimensional signal obtained this time is discarded, and a new acceleration signal is obtained, and then the new one-dimensional signal is pre-processed to recalculate the difference with the one-dimensional signal retained last time and compare them.

It can be seen that the two adjacent one-dimensional signals in step S121 do not include the eliminated one-dimensional signal, but refer to the previous one-dimensional signal that meets the breathing threshold requirement and the one-dimensional signal obtained this time.

At the same time, if the one-dimensional signal obtained last time is greater than the target threshold, and the one-dimensional signal this time is less than or equal to the target threshold, then the one-dimensional signal this time is judged to be the target signal; if one of the comparison results of the one-dimensional signal obtained last time and the one-dimensional signal this time and the target threshold does not meet the requirements, then the one-dimensional signal this time is judged not to be the target signal, and then a new acceleration signal is obtained, and after preprocessing the new one-dimensional signal, it is preliminarily judged whether it is a breathing signal, and after preliminarily judging it as a breathing signal, it is further judged whether it is a target signal.

In addition, after the new one-dimensional signal is acquired, the new one-dimensional signal becomes the one-dimensional signal obtained this time.

It is understandable that in step S123, only the retained one-dimensional signal may be recorded, and whether the retained one-dimensional signal is further processed to form a curve with continuous rising and falling edges is optional. Forming a curve can intuitively understand the respiratory monitoring situation, but it is not necessary for the internal calculation and judgment of the processor.

Assuming that the one-dimensional signal obtained for the eighth time is compared with the one-dimensional signal obtained for the ninth time, the one-dimensional signal obtained for the eighth time is the one-dimensional signal obtained for the previous time, and the one-dimensional signal obtained for the ninth time is the one-dimensional signal obtained for this time.

If the absolute value of the difference between the one-dimensional signal obtained for the eighth time and the one-dimensional signal obtained for the ninth time does not meet the requirement of the respiratory threshold, a new one-dimensional signal needs to be obtained, and the new one-dimensional signal is the one-dimensional signal obtained for the tenth time. At this time, the one-dimensional signal obtained last time is still the one-dimensional signal obtained for the eighth time, and the one-dimensional signal obtained for the ninth time is eliminated, and the one-dimensional signal obtained this time becomes the one-dimensional signal obtained for the tenth time. That is, because the one-dimensional signal obtained for the ninth time is eliminated, the two adjacent one-dimensional signals are the one-dimensional signal obtained for the eighth time and the one-dimensional signal obtained for the tenth time.

When the absolute value of the difference between the eighth and ninth one-dimensional signals meets the requirement of the respiratory threshold, and the ninth one-dimensional signal is not the target signal, a new one-dimensional signal needs to be obtained. However, the ninth one-dimensional signal is retained, so the corresponding new one-dimensional signal is the one obtained tenth time. The one-dimensional signal, that is, the one-dimensional signal obtained this time becomes the one-dimensional signal obtained the tenth time, and the one-dimensional signal obtained the previous time becomes the one-dimensional signal obtained the ninth time.

It can be understood that, for the one-dimensional signal obtained for the first time, the one-dimensional signal obtained the previous time is the one-dimensional signal obtained for the zeroth time, and the one-dimensional signal obtained for the zeroth time can be preset to zero.

In addition, the retained one-dimensional signal is recorded. Specifically, each time the acceleration signal is obtained by the three-axis acceleration sensor is sampled once, so a coordinate system is established with the sampling time as the horizontal coordinate and the one-dimensional signal as the vertical coordinate. The retained one-dimensional signal and the corresponding sampling time are recorded in the coordinate system, and adjacent one-dimensional signals are connected to obtain alternating and continuously distributed rising and falling edges.

It should be noted that the waveform corresponding to the respiratory signal is a continuously distributed rising edge and falling edge, so there is a difference between two adjacent one-dimensional signals, that is, the absolute value of the difference between two adjacent one-dimensional signals is greater than or equal to the respiratory threshold.

In addition, in some embodiments, the breathing threshold is a preset fixed value, and in other embodiments, the breathing threshold is a variable dynamic value to accommodate individual differences.

Please refer to FIG. 4 , in some embodiments, the respiratory monitoring method further includes the steps of:

S210, recording the number of samplings of the acceleration sensor through a sampling counter, and resetting it after the sampling number reaches a sampling threshold, so as to obtain sampling data of a stage.

It can be understood that each time the acceleration signal is acquired is a sampling, the sampling counter counts up by one, and after the count reaches the sampling threshold, the sampling counter is reset, at which time one stage of data sampling is completed, that is, the sampling data of one stage is obtained, and the data sampling of the next stage can be carried out, and the sampling counter starts counting again.

S220, obtaining the second peak-to-peak value in the sampling data of the previous stage, and calculating the respiratory threshold value by formula (1).
Respiratory threshold = second peak-to-peak value/m (1).

Here, m is an integer, and 4≤m≤32.

It is understandable that after the sampling counter is reset, the sampling data of the previous stage changes, and the second peak value (the maximum value in the sampling data of the previous stage) and the second valley value (the maximum value in the sampling data of the previous stage) need to be updated accordingly. the minimum value in the sampling data of the first segment), the second peak-to-peak value, and the respiratory threshold.

In addition, the breathing threshold used in this stage is the breathing threshold calculated based on the sampling data of the previous stage. In this way, the breathing threshold can be dynamically updated to adapt to individual differences.

It can be understood that the larger the value of m is, the smaller the respiratory threshold is, and the absolute value of the difference between two adjacent one-dimensional signals is more likely to meet the requirements of the respiratory threshold, while the smaller the value of m is, the larger the respiratory threshold is, and the absolute value of the difference between two adjacent one-dimensional signals is less likely to meet the requirements of the respiratory threshold. It can be seen that the smaller the value of m is, the more one-dimensional signals that do not meet the requirements are eliminated by the respiratory threshold, which makes the accuracy of the respiratory monitoring method higher.

It should be explained that step S210 and step S220 are performed synchronously with the above steps. Moreover, the second peak-to-peak value in step S220 refers to the peak-to-peak value in the previous stage, that is, it is calculated by obtaining the second peak value and the second valley value in the previous stage, while the first peak value, the first valley value and the first peak-to-peak value in the above embodiments refer to the data in this stage, that is, the data in the ongoing stage.

In addition, during the first stage of monitoring, the respiratory threshold can be set to 0.

Specifically, taking 40 samplings in one stage as an example, in the first stage, the breathing threshold is 0. After the sampling times reach 40 times, 40 one-dimensional signals are obtained. The breathing threshold can be calculated by these 40 one-dimensional signals, that is, the maximum value minus the minimum value in the 40 one-dimensional signals is used to obtain the second peak-to-peak value, and then the breathing threshold is calculated by formula (1).

When sampling is performed 40 times, the sampling counter is reset, and the next sampling is the sampling of the current stage (ie, the second stage), while the 40 samplings before the sampling counter is reset are the sampling of the previous stage.

It should be noted that in the first stage, the breathing threshold is 0, which is not conducive to preliminarily judging whether the one-dimensional signal is a breathing signal based on the breathing threshold. Therefore, in actual operation, the breathing threshold can be calculated using only the sampling data of the first stage, so that the breathing threshold can be used in the second stage, and normal breathing monitoring can be performed at the beginning of the second stage.

In addition, when performing sampling in the later stage, the difference between two consecutive one-dimensional signals may not meet the requirements. In this case, one of the one-dimensional signals will be discarded. The number of samplings in this stage will not change, but the number of one-dimensional signals retained in the end will increase due to the one-dimensional signal being discarded. Reduce by removing.

It can be understood that each breath corresponds to a waveform including a rising edge and a falling edge, and the one-dimensional signal corresponding to any point in the waveform can be defined as a target signal, so that the curves between two adjacent target signals can be integrated into one waveform. That is, the time interval between two adjacent target signals corresponds to one breathing time.

In some embodiments, the one-dimensional signal corresponding to a point approximately in the middle of the falling edge is defined as a target signal, which can improve monitoring accuracy.

Please refer to FIG. 3 , in some embodiments, the steps between step S123 and step S125 further include:

S124, calculating the average value of the retained one-dimensional signal to obtain a dynamic average value, and the dynamic average value is used as the target threshold.

It should be noted that the calculation of the dynamic average is to find the average value of the one-dimensional signal that has been retained, that is, each time a new one-dimensional signal is retained, the average value of the one-dimensional signal is updated. In order to ensure the accuracy of the dynamic average, the first several one-dimensional signals that are severely interfered can be removed, and then the dynamic average can be calculated.

In this way, by setting the dynamic average value as the target threshold in step S124, when the comparison results of two adjacent one-dimensional signals with the target threshold meet the requirements in step S125, the one-dimensional signal is located approximately in the middle of the falling edge of the corresponding waveform and can be used as the target signal.

The traditional comparison method is to directly compare the sizes of two adjacent one-dimensional signals, and then determine whether the current edge is a falling edge or a rising edge based on the size. For example, if the one-dimensional signal obtained last time is larger than the one-dimensional signal obtained this time, it can be determined that the one-dimensional signal obtained this time is on the falling edge.

It is understandable that the traditional comparison method can only determine whether it is a falling edge or a rising edge, but it does not determine which one-dimensional signal of the falling edge or the rising edge is the target signal. That is to say, after determining the falling edge or the rising edge, further calculation is required to obtain the target signal. In addition, in the traditional comparison method, the obtained one-dimensional signal generally needs to be compared twice, that is, the one-dimensional signal obtained last time is compared with the one-dimensional signal obtained this time, and the one-dimensional signal obtained this time is compared with the one-dimensional signal obtained next time. The comparison process is cumbersome. Moreover, it is necessary to compare the one-dimensional signal obtained in the previous comparison with the one-dimensional signal obtained in the next comparison. After obtaining all the one-dimensional signals of the entire falling edge or rising edge, it is clear that one of the one-dimensional signals is the target signal. In this embodiment, by comparing the one-dimensional signal with the target threshold, the comparison process is simple, and it can be known in real time whether the one-dimensional signal obtained this time is on the falling edge and whether it is the target signal.

Please refer to FIG. 5 , in some embodiments, step S140 includes:

S141, compare the time interval with the time lower limit threshold. If the time interval is greater than the time lower limit threshold, execute step S142. Otherwise, it is determined that the time interval does not meet the requirements.

S142, compare the time interval with the time upper limit threshold, if the time interval is less than or equal to the time upper limit threshold, execute step S143, otherwise it is determined that the time interval does not meet the requirement.

S143, compare the first peak value with the sum value and the first peak-to-peak value with the peak-to-peak value threshold. If the first peak value is greater than the sum value and the first peak-to-peak value is greater than the peak-to-peak value threshold, execute step S144. If the first peak value is less than or equal to the sum value and/or the first peak-to-peak value is less than or equal to the peak-to-peak value threshold, determine that the first peak value and/or the first peak-to-peak value do not meet the requirements.

The sum is the sum of the dynamic average and the peak threshold.

S144, recording a breath by a breath counter.

It should be noted that the peak value threshold and the peak-to-peak value threshold are preset values.

Please refer to FIG. 6 , in some embodiments, the respiratory monitoring method further includes the steps of:

S310, recording the number of times the acceleration signal is obtained through a time counter.

S330, repeating steps S310 to S320 to calculate the time interval between two adjacent target signals.

It should be explained that the time counter records the number of times, but the duration of each recording can be determined. In this way, the time interval can be calculated based on the number of times the time counter records between two adjacent target signals.

In addition, the removed one-dimensional signal is considered non-existent, that is, the one-dimensional signal will not be used in the calculation of the dynamic average value to ensure the calculation accuracy of the dynamic average value, but the sampling time corresponding to the one-dimensional signal will be recorded to facilitate the calculation of the time interval between adjacent target signals, and the sampling time corresponding to the one-dimensional signal will be recorded. The sampling times of the signal will be recorded.

It should be noted that when it is determined that the target signal appears, the time counter is reset, and when at least one of the time interval, the first peak value and the first peak-to-peak value does not meet the requirements, the time counter for calculating the time interval needs to be reset, and then steps S110 to S140 can be repeated for the next respiratory monitoring.

When it is determined that the target signal appears and the time interval, the first peak value and the first peak-to-peak value all meet the requirements, the time counter is reset and the breath counter records a breath. Then steps S110 to S140 can be repeated to perform the next breath monitoring.

In some embodiments, step S142 further includes: if the time interval is greater than an upper time threshold, issuing a breathing interruption alarm.

It can be understood that the respiratory monitoring method determines whether the target signal appears through steps S121 to S125, so that it can be determined that when the target signal appears, the state of the monitored object tends to be stable, that is, the acceleration signal obtained next can be regarded as a respiratory signal, and the time interval between two adjacent target signals can be regarded as a breath. If the time interval between two adjacent target signals is greater than the upper time threshold, it means that the monitored object is in a certain breath for a long time, and accordingly it can be judged that the monitored object is at risk of respiratory interruption.

In order to facilitate understanding of the technical solution of the present application, the workflow of the respiratory monitoring method in the above embodiment is described here with reference to FIG. 7:

The three-axis acceleration sensor is used for continuous sampling to continuously obtain acceleration signals, and each sampling obtains acceleration signals of three axes. While sampling, the time counter and the sampling counter record the number of samplings.

For the acceleration signals of the three axes obtained by each sampling, the acceleration signal of the axis with the largest value is first removed, and then the acceleration signals of the remaining two axes are filtered. After filtering, the acceleration signals of the remaining two axes are modulo-calculated to obtain a one-dimensional signal.

A one-dimensional signal can be calculated for each sampling, and multiple samplings can obtain multiple one-dimensional signals (to ensure data accuracy, the one-dimensional signals calculated and measured in the first several times can be eliminated).

Next, the difference between two adjacent one-dimensional signals is calculated, and the absolute value of the difference is compared with the respiratory threshold. If the absolute value of the difference meets the requirements, the one-dimensional signal is preliminarily judged to be a respiratory signal, and the one-dimensional signal that meets the requirements is retained and recorded.

In addition, each time a new one-dimensional signal is retained, the dynamic average value is calculated and updated to serve as the target threshold. After the one-dimensional signal is preliminarily determined to be a respiratory signal, two adjacent one-dimensional signals are compared with the target threshold respectively.

After determining that the target signal appears (the determination method is as described in step S125), the time counter is reset until the target signal appears again, and the time counter is reset again. At this time, the time interval between two adjacent target signals can be calculated according to the number of the time counter. At the same time, each retained one-dimensional signal is recorded, so the first peak value and the first peak-to-peak value in the time interval can be obtained, that is, the first peak value and the first peak-to-peak value between two adjacent target signals can be obtained.

After obtaining the data of the time interval, the first peak value and the first peak-to-peak value, it is determined whether the three meet the requirements (the determination method is as described in step S140). If the requirements are met, the breath counter records one breath.

It is understandable that the sampling counter will be reset when it reaches the sampling threshold, and under normal circumstances, there will be at least several breaths within the sampling threshold range, that is, when the sampling counter reaches the sampling threshold, a stage of sampling is completed, and this stage includes several breaths.

Based on the above-mentioned respiratory monitoring method, the present application also provides a respiratory monitoring device, including a processor and an acceleration sensor, a time counter and a respiratory counter electrically connected to the processor.

The acceleration sensor is used to obtain acceleration signals of multiple different spatial axes caused by breathing, the processor is used to preprocess the acceleration signals to obtain multiple one-dimensional signals, and the processor is also used to compare and record the multiple one-dimensional signals, determine the target signal through comparison, and obtain alternating and continuous rising edges and falling edges through recording.

The time counter is used to calculate the time interval between the two adjacent target signals, and the breath counter is used to record the number of breaths.

By setting the above-mentioned breathing monitoring device, after the acceleration sensor obtains the acceleration signal, the acceleration The speed signal is transmitted to the processor, and the processor pre-processes the acceleration signal to obtain a one-dimensional signal. The processor can compare and record multiple one-dimensional signals. When two target signals appear, the time counter can calculate the time interval. The processor can also obtain the first peak value and the first peak-to-peak value in the one-dimensional signal within the time interval. Next, the processor compares the time interval, the first peak value, and the first peak-to-peak value to determine whether the three meet the requirements. After the three meet the requirements, the processor controls the respiration counter to record the number of respirations.

Multiple single-axis acceleration sensors may be used for sampling, or a single-axis acceleration sensor and a dual-axis acceleration sensor may be used in combination for sampling, or a triaxial acceleration sensor may be used for sampling. When a triaxial acceleration sensor is used for sampling, correspondingly, the acceleration signals of the multiple spatial axes may be acceleration signals of mutually perpendicular X-axis, Y-axis, and Z-axis.

Since the respiratory monitoring device does not require any other special equipment, it only needs to collect acceleration signals through the acceleration sensor, which is simple in structure and easy to operate, thus facilitating daily monitoring. In addition, the processor will compare the time interval, the first peak value, and the first peak-to-peak value, and only record the breathing after the three meet the requirements, ensuring the monitoring accuracy.

In some embodiments, the respiratory monitoring device further includes a low-pass filter electrically connected to the processor, and the low-pass filter is used to filter the acceleration signal.

In some embodiments, the respiratory monitoring device further includes a sampling counter, which is used to record the number of times the acceleration sensor samples, thereby dividing the sampled data into different stages.

Taking the sampling of n times in a stage in the above embodiment as an example, when the number of times recorded by the sampling counter reaches n times, the sampling counter is reset, and at this time, the n times of sampling data previously recorded by the processor are the sampling data of the previous stage.

In some embodiments, the respiratory monitoring device further comprises an alarm electrically connected to the processor, the alarm being configured to issue a respiratory interruption alarm when the time interval is greater than an upper time limit threshold.

The technical features of the above-mentioned embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. The combination of these technical features does not contain any contradiction and should be considered to be within the scope of this specification.

The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

A respiratory monitoring method, characterized in that it comprises the steps of:

S110, sampling and acquiring acceleration signals of multiple different spatial axes caused by breathing through an acceleration sensor and preprocessing the acceleration signals to obtain a one-dimensional signal;

S120, comparing and recording the plurality of one-dimensional signals, and executing step S130 when a target signal appears;

S130, repeating steps S110 to S120, then calculating the time interval between two adjacent target signals, and obtaining the first peak value and the first peak-to-peak value of the one-dimensional signal within the time interval;

S140, comparing the time interval, the first peak value, and the first peak-to-peak value;

If the time interval, the first peak value and the first peak-to-peak value meet the requirements, a breath is recorded.
The respiratory monitoring method according to claim 1, characterized in that step S110 comprises:

S111, sampling and acquiring the acceleration signals of the three axes caused by breathing through a three-axis acceleration sensor;

S112, removing the acceleration signal of an axis with the largest value;

S114, performing modulus calculation on the acceleration signals of the remaining two axes to obtain the one-dimensional signal.
The respiratory monitoring method according to claim 2, characterized in that in step S114

Among them, a and b are the acceleration signals of the remaining two axes respectively.
The respiratory monitoring method according to claim 2, characterized in that the step between step S112 and step S114 further includes the following steps:

S113, filtering the acceleration signals of the remaining two axes.
The respiratory monitoring method according to claim 4, characterized in that step S113 comprises:

The acceleration signals of the remaining two axes are filtered by constructing a 0.2 Hz to 0.5 Hz low-pass filter.
The respiratory monitoring method according to claim 1, characterized in that step S120 comprises:

S121, calculating the difference between two adjacent one-dimensional signals;

S122, comparing the absolute value of the difference with the breathing threshold;

If the absolute value of the difference is greater than or equal to the breathing threshold, the one-dimensional signal obtained this time is retained and step S123 is executed; if the absolute value of the difference is less than the breathing threshold, the one-dimensional signal obtained this time is discarded, and a new one-dimensional signal is obtained and step S121 is repeated;

S123, record the retained one-dimensional signal;

S125, comparing two adjacent retained one-dimensional signals with a target threshold;

If the previous one-dimensional signal is greater than the target threshold and the current one-dimensional signal is less than or equal to the target threshold, the current one-dimensional signal is determined to be the target signal; otherwise, the current one-dimensional signal is determined not to be the target signal, a new one-dimensional signal is obtained and step S121 is repeated.
The respiratory monitoring method according to claim 6, characterized in that the respiratory monitoring method further comprises the steps of:

S210, recording the number of samplings of the acceleration sensor by a sampling counter, and resetting it after the number of samplings reaches a sampling threshold, so as to obtain sampling data of a stage;

S220, obtaining the second peak-to-peak value in the sampling data of the previous stage, and calculating the respiratory threshold by formula (1);
Respiratory threshold = second peak value/m (1);

Here, m is an integer, and 4≤m≤32.
The respiratory monitoring method according to claim 7, characterized in that the second peak-to-peak value = the second peak value - the second valley value;

The second peak value is the maximum value in the sampling data of the previous stage, and the second valley value is the minimum value in the sampling data of the previous stage.
The respiratory monitoring method according to claim 6, characterized in that the step between step S123 and step S125 further includes the following steps:

S124, calculating the average value of the retained one-dimensional signal to obtain the target threshold.
The respiratory monitoring method according to claim 1, characterized in that step S140 comprises:

S141, comparing the time interval with a time lower limit threshold, if the time interval is greater than the time lower limit threshold, executing step S142, otherwise it is determined that the time interval does not meet the requirement;

S142, comparing the time interval with the time upper limit threshold, if the time interval is less than or equal to the time upper limit threshold, executing step S143, otherwise it is determined that the time interval does not meet the requirement;

S143, comparing the first peak value with the sum value and the first peak-to-peak value with the peak-to-peak value threshold; if the first peak value is greater than the sum value and the first peak-to-peak value is greater than the peak-to-peak value threshold, executing step S144; if the first peak value is less than or equal to the sum value and/or the first peak-to-peak value is less than or equal to the peak-to-peak value threshold, determining that the first peak value and/or the first peak-to-peak value do not meet the requirements;

S144, recording a breath by a breath counter;

The sum value is the sum of the target threshold and the peak threshold.
The respiratory monitoring method according to claim 10, characterized in that step S142 further comprises:

If the time interval is greater than the upper time limit threshold, a breathing interruption alarm is issued.
The respiratory monitoring method according to claim 1, characterized in that the respiratory monitoring method further comprises the steps of:

S310, recording the number of times the acceleration signal is obtained through a time counter;

S320, when it is determined that the target signal appears, the time counter is reset;

S330, repeating steps S310 to S320 to calculate the distance between two adjacent target signals. The time interval.
A respiratory monitoring device, characterized in that it comprises a processor and an acceleration sensor, a time counter and a respiratory counter electrically connected to the processor;

The acceleration sensor is used to obtain an acceleration signal, the processor is used to preprocess the acceleration signal to obtain multiple one-dimensional signals, and compare and record the multiple one-dimensional signals, the time counter is used to calculate the time interval, and the respiration counter is used to record the number of respirations.
The respiratory monitoring device according to claim 13 is characterized in that the respiratory monitoring device also includes a low-pass filter electrically connected to the processor, and the low-pass filter is used to filter the acceleration signal.
The respiratory monitoring device according to claim 13 is characterized in that the respiratory monitoring device also includes a sampling counter, and the sampling counter is used to record the number of times the acceleration sensor samples.
The respiratory monitoring device according to claim 13 is characterized in that the respiratory monitoring device also includes an alarm electrically connected to the processor, and the alarm is used to issue a respiratory interruption alarm when the time interval is greater than an upper time limit threshold.