CN101991418A - Method for improving respiratory rate detection accuracy - Google Patents
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- CN101991418A CN101991418A CN2009101627974A CN200910162797A CN101991418A CN 101991418 A CN101991418 A CN 101991418A CN 2009101627974 A CN2009101627974 A CN 2009101627974A CN 200910162797 A CN200910162797 A CN 200910162797A CN 101991418 A CN101991418 A CN 101991418A
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Abstract
The invention discloses a method for improving respiratory rate detection accuracy, comprising the following steps of: preprocessing respiratory data by utilizing high pass and low pass filtering; then carrying out empirical mode decomposition on the preprocessed respiratory data; subsequently, selecting a natural mode function obtained by the decomposition according to the physiological feature of a respiratory signal; then carrying out choking judgment on the obtained natural mode function by utilizing a method of getting the absolute value and solving the average; and if the choking judgment condition is not satisfied, figuring out the respiratory rate at the time by utilizing a wave form method or a frequency spectrum method. By adopting the technical scheme, the respiratory signal can be effectively extracted from respiratory waves of dysphoria interference and heartbeat interference. The method of the empirical mode decomposition effectively overcomes the defect that the respiratory signal containing the interference is directly processed by the wave form method and the frequency spectrum method, and finally, the weighted average of the historical respiratory rate is introduced, and thereby, the interference resistance of respiratory detection can be improved to make the respiratory rate calculation stable and reliable.
Description
Technical field
The present invention relates to a kind of method that improves the breathing rate detection accuracy, relating in particular to a kind of empirical mode decomposition method that utilizes decomposes time domain waveform, find the intrinsic mode function corresponding then, disturb, improve the accuracy of breathing rate detection and the method for stability thereby remove with respiratory wave.
Background technology
Method based on impedance measurement often is used to breathing equipment acquisition respiratory wave signal.During human body respiration, the impedance meeting in thoracic cavity by detecting the variation of impedance, just can obtain respiratory wave along with corresponding the variation taken place in the fluctuating of thorax.Actual respiration measurement process generally is by the electrode that is attached to the body surface ad-hoc location, impedance variation by cardiac diagnosis lead bundle of lines thoracic cavity utilizes high-frequency carrier signal to modulate, then through a series of circuit to this carrier signal amplify, processes such as detection, filtering, just can obtain mimic breath signal, be converted to the breath signal of numeral again through A/D, utilization is at last breathed algorithm computation and is gone out breathing rate.
During eupnea, neonatal breathing rate is 30～70BPM (Beats Per Minute), and that the adult is 12～20BPM, if but consider abnormal conditions, generally requiring the respiration detection scope is 8～120BPM, individually can be up to 150BPM.So can obtain the pairing frequency of breathing rate detection range is 0.125～2.5Hz.
The method of calculating breathing rate in the market mainly is a Waveform Method, by the adjacent effective crest of searching respiratory waveform, calculating wave period, thereby obtains breathing rate.Though the computational process of Waveform Method has advantage more intuitively, find that in the actual clinical process when patient is restless when causing waveform disorderly, often effectively wave period is looked for inaccurately, causes mistake in computation; When serious interference aroused in interest caused the eupnea waveform to superpose waveform aroused in interest, Waveform Method can not be divided interference waveform aroused in interest and respiratory wave by right area, thereby has the risk of breathing rate mistake in computation; When the limb motion interference occurring and causing baseline drift, because Waveform Method need be judged crest, trough by baseline, so the leakage identification of respiratory wave may appear in Waveform Method simultaneously.In a word, if these interference are not eliminated, utilize Waveform Method to calculate breathing rate and finally can cause the inaccurate and unstable of respiration measurement.
Summary of the invention
The objective of the invention is correctly to distinguish the deficiency of interference waveform and respiratory wave, cause the risk of breathing rate mistake in computation easily, proposed the breathing rate detection method of a kind of accuracy height, good stability in order to overcome Waveform Method.
In order from disturbed breath data, to extract respiratory wave exactly, design of the present invention is: Waveform Method is when calculating breathing rate usually, all be at first to eliminate out-of-band interference by the method for filtering, then filtered respiratory wave is discerned from waveform, by different Rule of judgment is set, carry out interferential rejecting, because Waveform Method is the calculating of carrying out breathing rate in shape from respiratory wave, and interference restless and aroused in interest has made respiratory wave mixed and disorderly and unusual, therefore by being set, some Rule of judgment still are difficult to realize accurate calculating to breathing rate, disturb and breath signal if can from primary breath data, distinguish, and then utilizing Waveform Method that breath signal is calculated, the calculating of breathing rate is very accurate with making.
In order to realize above-mentioned purpose, the present invention by the following technical solutions:
By breathing the breath signal that hardware circuit obtains numeral, utilize high pass and low-pass filtering technique that it is carried out pretreatment then;
Pretreated breath data is carried out empirical modal decomposes;
Physiological feature according to breath signal carries out choosing of intrinsic mode function (breath signal of corresponding this moment);
The intrinsic mode function that obtains is breathed the judgement that suffocates;
Utilize Waveform Method or Spectrum Method that respiratory wave is analyzed, calculate the breathing rate of this moment;
Breathing rate and current breathing rate according to history are weighted on average, calculate the current breathing rate.
The time series of supposing breath data is x (t), and above-mentioned empirical modal decomposition may further comprise the steps:
Find out breath data x (t) all maximum point and minimum point; Utilize cubic spline function to fit to the upper and lower envelope of original breath data sequence respectively; Ask its meansigma methods to obtain average packet winding thread M to upper and lower envelope
1(t); Former breath data sequence is deducted M
1(t), obtain 1 new data sequence D that removes low-frequency component
1(t), that is:
D
1(t)＝x(t)-M
1(t)
D
1(t) steady data sequence not necessarily needs to repeat said process and handles.If D
1(t) average envelope is M
11(t), then remove data sequence D behind the low-frequency component of envelope representative
11(t) be:
D
11(t)＝D
1(t)-M
11(t)
Repeat said process, resulting average envelope is gone to zero, obtain first natural mode of vibration component (Intrinsic Mode Function, IMF) F like this
1(t).The composition of high frequency in its expression breath data sequence;
Deduct F with x (t)
1(t), obtain 1 new data sequence R that removes radio-frequency component
1(t), to R
1(t) handle according to above-mentioned steps again, obtain second IMF component F
2(t); So repeat, to the last a data sequence R
n(t) can not decompose again till.
Choosing of above-mentioned intrinsic mode function (breath signal of corresponding this moment) may further comprise the steps:
Decompose by top empirical modal, can resolve into n IMF component F to breath signal x (t)
1(t) and a residual components R
n(t), the n that wherein a decomposites component F
i(t) comprised the different frequency segment components of breath signal from the high frequency to the low frequency respectively, and residual components R
n(t) be the central tendency value of primary signal.According to the physiological feature of breath signal, select r IMF component F
r(t) as the breath signal that from primary signal x (t), extracts.
The judgement that above-mentioned breathing suffocates may further comprise the steps:
Mode function (breath signal of corresponding this moment) is chosen the breath signal of acquisition by taking absolute value, ask average then, compare with the given threshold condition that suffocates again, when this meansigma methods less than the threshold value certain hour that suffocates, then be judged to and suffocate, otherwise carry out the calculating of breathing rate.
Above-mentioned Waveform Method or the Spectrum Method utilized analyzed respiratory wave, calculates the breathing rate of this moment, may further comprise the steps:
If do not satisfy the Rule of judgment that suffocates, can in time domain, utilize Waveform Method to carry out the calculating of breathing rate to the breath signal that obtains; Perhaps in frequency domain, utilize Fourier transform or chirp z transform to carry out spectrum analysis, the pairing frequency of maximum spectrum peak value is the frequency of breathing, owing to utilize empirical modal to decompose the interference of having removed high frequency, so the pairing spectrum peak of the breath signal that extracts is the highest, and the spectrum peak outside the maximum spectrum peak value will be very low.
Above-mentioned breathing rate and current breathing rate according to history is weighted average computation, is breathing rate and the historical breathing rate that obtains is weighted on average, obtains the value of current breathing rate.
The Time-Frequency Analysis Method of decomposing based on empirical modal both had been suitable for analysis non-linear, non-stationary signal, also was suitable for the analysis of linearity, stationary signal, and empirical modal each mode function physical significance of reflected signal preferably of decomposing gained.The intrinsic advantage that the present invention has utilized empirical modal to decompose at first resolves into interfering signal and breath signal to the breath signal initial data, utilizes Waveform Method or Spectrum Method that breath signal is analyzed then, thereby calculates breathing rate.This method can be eliminated interference restless, aroused in interest and that limb motion causes preferably, from the source signal is distinguished, and is very different on processing mode with present breathing rate computational methods.So at first by extracting breath signal, the method with present calculating breathing rate combines again, can improve the capacity of resisting disturbance of breathing rate calculating preferably and improve accuracy and the stability that breathing rate calculates.Adopt above-mentioned technical scheme, can from restless interference, interferential respiratory wave aroused in interest, extract breath signal effectively.The method that empirical modal decomposes can be extracted the different characteristic of signal preferably, then interested feature is carried out selective analysis, Waveform Method and Spectrum Method have effectively been overcome directly to containing the shortcoming that noisy breath signal is handled, introduce the weighted average of historical breathing rate again, can improve the anti-interference of respiration detection, thereby make breathing rate calculation stability and reliable.
Description of drawings
Fig. 1 is a flow chart of the present invention;
Fig. 2 is the breath data of eupnea;
The figure as a result of Fig. 3 respiratory wave that to be the present invention go out from the eupnea extracting data;
Fig. 4 utilizes chirp z transform that the respiratory wave that normal breath data extracts is calculated, the figure as a result of the breathing rate of acquisition;
Fig. 5 contains bigger aroused in interest interferential breath data in breathing;
Fig. 6 is the figure as a result of the respiratory wave that extracts from contain bigger aroused in interest interferential breath data of the present invention;
Fig. 7 utilizes chirp z transform that the respiratory wave that contains interferential breath data aroused in interest and extract is calculated the figure as a result of the breathing rate of acquisition;
Fig. 8 is the breath data that patient obtains when restless;
Fig. 9 is the figure as a result of the respiratory wave that extracts in the breath data of the present invention when patient is restless;
Figure 10 utilizes chirp z transform to calculate the figure as a result of the breathing rate of acquisition to containing restless interferential breath data.
The specific embodiment
The present invention is described in further detail below in conjunction with the drawings and specific embodiments.
The custodial care facility that is used for the breath signal measurement mainly comprises host computer and slave computer, and slave computer mainly is to obtain breath data by hardware circuit, utilizes the breathing algorithm computation to go out breathing rate then; Host computer mainly is the data of accepting from slave computer, shows respiratory waveform, breathing rate then and the warning that suffocates.The calculation process of method of the present invention as shown in Figure 1, its key step comprises:
At first utilize high pass and low-pass filtering that breath data is carried out pretreatment.Then pretreated breath data being carried out empirical modal decomposes; Secondly according to the physiological feature of breath signal the intrinsic mode function that decomposition obtains is chosen; Again the intrinsic mode function utilization that obtains is taken absolute value, the judgement of asking average method to suffocate, if do not satisfy the condition of suffocating and judging, then utilize Waveform Method or Spectrum Method to calculate the breathing rate of this moment, owing to breathe disturbed easily, in order further to improve the accuracy that breathing rate calculates, the breathing rate of historical breathing rate and previous step calculating is weighted on average, thereby obtains the current breathing rate.
The method that the respiratory wave extracting method use experience mode that relates in the example of the present invention is decomposed.Its catabolic process is as follows:
The first step: all Local Extremum of determining time series x (t), then all maximum points and all minimum points are used Cubic Spline Functions Fitting respectively, obtaining two different curves couples together all maximum points and minimum point respectively, obtain the upper and lower envelope of x (t), so just make all data points of signal be between the upper and lower envelope.If the meansigma methods of upper and lower envelope is m
1(t).
Second step: deduct m with signal x (t)
1(t), their difference is designated as d
1(t), ideally, d
1(t) should be a basic modal components.Yet for non-linear, non-stationary data, the envelope average may be different from real local mean value, because most of waveforms are not symmetric.Therefore obtain an empirical modal component by a screening process, the main effect of this process mainly contains two: one is to remove the stack ripple, the 2nd, make waveform symmetry more.In order to reach this purpose, make d
1(t), repeat the process of the first step, up to obtaining a basic modal components f as pending data
1(t).
The 3rd step: from original data sequence x (t), decomposite first basic modal components f
1(t) afterwards, deduct f with x (t)
1(t), obtain residue sequence r
1(t)=x (t)-f
1(t).
The 4th step: r
1(t) as a new sequence, repeat above-mentioned steps, extract the second, the three respectively, until n basic modal components.At this moment, r
n(t) become a monotonic sequence, wherein no longer comprise the information of any mode, so can not carry out top catabolic process again, it is exactly the remainder of primary signal, r
n(t) average of representative data sequence x (t).
Said process can be expressed as:
r
1(t)＝x(t)-f
1(t)
r
2(t)＝r
1(t)-f
2(t)
…………………
r
n(t)＝r
n-1(t)-f
n(t)
So can get:
Wherein each IMF component has all been represented an internal characteristics of signal.IMF must satisfy two conditions; For a column data, extreme point and zero crossing number equate or differ a point at the most; In the arbitrfary point, the meansigma methods of the envelope that envelope that is made of the local maximum point and local minizing point constitute is zero.
Theoretical basis based on the empirical modal decomposition, for the characteristics of the breath signal of actual measurement itself, breath signal is very faint, disturbed easily, can carry out empirical modal to breath signal and decompose, obtain the natural mode of vibration composition of different frequency composition, then from these natural mode of vibration components, select and the corresponding mode of respiratory wave, this can select according to actual measurement, and is more stable by the mode of pretreated respiratory wave.
After obtaining the natural mode of vibration component of respiratory wave, just can be to the breath signal judgement that suffocates, in order to make the accuracy of judgement that suffocates reliable, can take absolute value to breath signal, average then, the last threshold value of suffocating with setting compares, thereby realizes the judgement to suffocating.
If breath signal does not satisfy the condition suffocate and to judge, then can be by the calculating of following method realization to breathing rate:
Waveform Method is to be widely used in the breathing rate Calculation Method at present, and this method is simple, directly perceived.If the respiratory waveform comparison rule, so the breathing rate that calculates of this method accurately, reliable, if restless but respiratory wave is subjected to, the influence of aroused in interest and baseline drift etc., then the reliability of result of calculation will reduce.Utilize the method for empirical modal decomposition to remove the interference in the breathing, at this moment the respiratory waveform comparison rule of acquisition is utilized Waveform Method to calculate breathing rate again and will be obtained stable and reliable result.Spectrum Method is the another kind of method of calculating breathing rate, and the signal of single-frequency is carried out spectrum analysis, the peak-peak of a frequency spectrum can occur at this frequency place; The formed composite signal in a plurality of frequency stack backs is carried out spectrum analysis, a local maximum all can appear in the spectrum energy at each frequency place, in all local maximums, the signal of maximum pairing this frequency of frequency representation of local maximum is the strongest when superposeing in time domain.
Theoretical basis based on Spectrum Method, can in frequency domain, utilize Fourier transform or chirp z transform to carry out spectrum analysis to the breath signal that obtains, the pairing frequency of maximum spectrum peak value is the frequency of breathing, owing to utilize empirical modal to decompose the interference of having removed high frequency, so the pairing spectrum peak of the breath signal that extracts is the highest, this calculating to breathing rate is very convenient, and the present invention only uses the method for chirp z transform to calculate breathing rate.
In order further to improve the capacity of resisting disturbance of respiratory wave, use historical breathing rate several times and current breathing rate to be weighted on average, choosing of weights can be provided with according to the significance level of historical data, thereby obtains the calculating of current breathing rate.
For the calculating effect of this method is described vividerly, below respectively with in the actual clinical to the eupnea data of different patient's accesses, contain interferential breath data aroused in interest, containing restless interferential breath data is example, and this method accuracy that breathing rate calculates when handling dissimilar breath data is described respectively.
For eupnea data shown in Figure 2, the respiratory waveform rule, disturb less, after the empirical modal decomposition, the natural mode of vibration component corresponding with respiratory wave of gained as shown in Figure 3, this component is the respiratory wave that extracts from original breath data, as can be seen from the figure, the curve of respiratory wave is very smooth, has eliminated synergetic less High-frequency Interference on the respiratory wave.To the respiratory wave that extracts, utilize chirp z transform to calculate breathing rate, as shown in Figure 4, this respiratory waveform has very concentrated Energy distribution in frequency domain, the spectrum energy at asterisk place is maximum in the local maximum of all spectrum energy envelopes among Fig. 4, its pairing frequency inverted is become the breathing rate of per minute, can obtain the breathing rate of respiratory wave shown in Figure 3.
For respiratory wave shown in Figure 5, wherein contain bigger interference aroused in interest, utilize empirical modal to decompose, can obtain the pairing natural mode of vibration component of respiratory wave, as shown in Figure 6, this component has removed interferential influence aroused in interest, and has reflected the Changing Pattern of respiratory wave exactly.To the respiratory wave that extracts, utilize chirp z transform to calculate breathing rate, as shown in Figure 7, the pairing frequency of maximum spectrum energy, be respiratory frequency, having marked with an asterisk among Fig. 7 is subjected to the breathing rate of interferential respiratory wave aroused in interest, and it understands that figuratively speaking empirical modal decomposes for the advantage that calculating had that contains interferential breathing rate aroused in interest in breathing.
For containing restless interferential situation in the respiratory wave shown in Figure 8, though respiratory waveform is relatively more chaotic, shape is also irregular, but utilize empirical modal to decompose, still can extract the change curve of respiratory wave, as shown in Figure 9, as can be seen from the figure, the natural mode of vibration corresponding with respiratory wave reflected the Changing Pattern of respiratory wave.Utilize chirp z transform to calculate breathing rate then, can realize the calculating of breathing rate from the frequency domain easily, the breathing rate among Figure 10 shown in the asterisk is the breathing rate that calculated by restless interferential breath data, and is very identical with practical situation.
Can illustrate by top example: when the human body eupnea, the respiratory waveform comparison rule, the calculating of breathing rate is relatively easy; But when respiratory wave is disturbed by electrocardio, perhaps breathe and be subjected to the restless interference of human body,, directly calculate breathing rate, can cause that breathing rate calculates the generation error with Waveform Method if do not eliminate these interference in the breathing.The method of utilizing empirical modal to decompose is at first extracted breath signal, and then utilizes Waveform Method or Spectrum Method to calculate breathing rate, will obtain result of calculation more accurately.The present invention has also added and is weighted average method with the data of historical breathing rate and current breathing rate and calculates current breathing rate, thereby makes the result of calculation of breathing rate more stable and reliable.
Claims (8)
1. method that improves the breathing rate detection accuracy may further comprise the steps:
A, the digital breath signal that will obtain carry out filter preprocessing;
B, pretreated breath data is carried out empirical modal decompose;
C, carry out choosing of intrinsic mode function according to the physiological feature of breath signal;
D, the intrinsic mode function that obtains is breathed the judgement that suffocates;
E, utilize Waveform Method or Spectrum Method that respiratory wave is analyzed, calculate the breathing rate of this moment;
F, be weighted average computation according to the breathing rate of history and current breathing rate and go out the current breathing rate.
2. a kind of method that improves the breathing rate detection accuracy according to claim 1 is characterized in that described filter preprocessing comprises the pretreatment of high pass and low-pass filtering.
3. a kind of method that improves the breathing rate detection accuracy according to claim 1 is characterized in that, described empirical modal decomposes, and at first finds out breath data x (t) all maximum point and minimum point; Utilize cubic spline function to fit to the upper and lower envelope of original breath data sequence respectively; Ask its meansigma methods to obtain average packet winding thread M to upper and lower envelope
1(t); Former breath data sequence is deducted M
1(t), obtain a new data sequence D who removes low-frequency component
1(t), that is:
D
1(t)＝x(t)-M
1(t)
Work as D
1When (t) not being a steady data sequence, need repeat to handle; If D
1(t) average envelope is M
11(t), then remove data sequence D behind the low-frequency component of envelope representative
11(t), that is:
D
11(t)＝D
1(t)-M
11(t)
Repeat said process, resulting average envelope is gone to zero, obtain first natural mode of vibration component F
1(t), the composition of high frequency in its expression breath data sequence;
Deduct F with x (t)
1(t), obtain a new data sequence R who removes radio-frequency component
1(t), to R
1(t) repeat again to handle, obtain second IMF component F
2(t); So repeat, to the last a data sequence R
n(t) can not decompose again till.
4. a kind of method that improves the breathing rate detection accuracy according to claim 1 is characterized in that, the choosing of described intrinsic mode function, and its physiological feature according to breath signal is selected; Can resolve into n IMF component F to breath signal x (t) by decomposing
i(t) and 1 residual components R
n(t), the n that wherein a decomposites component F
i(t) comprised the different frequency segment components of breath signal from the high frequency to the low frequency respectively, and residual components R
n(t) be the central tendency value of primary signal.According to the physiological feature of breath signal, select r IMF component F
r(t) as the breath signal that from primary signal x (t), extracts.
5. a kind of method that improves the breathing rate detection accuracy according to claim 1, it is characterized in that, the intrinsic mode function that obtains is breathed the judgement that suffocates, take absolute value earlier, average then, last with suffocate threshold ratio, thereby realize judgement to suffocating, when this meansigma methods less than the threshold value certain hour that suffocates, then be judged to and suffocate, otherwise carry out the calculating of breathing rate; Can in time domain, utilize Waveform Method to carry out the calculating of breathing rate to the breath signal that obtains; Perhaps utilize Fourier transform or chirp z transform to carry out spectrum analysis in frequency domain, the pairing frequency of maximum spectrum peak value is the frequency of breathing.
6. a kind of method that improves the breathing rate detection accuracy according to claim 1 is characterized in that, utilizes Waveform Method or Spectrum Method that respiratory wave is analyzed, and when breathing rate is calculated, can use Waveform Method or Spectrum Method to calculate.
7. a kind of method that improves the breathing rate detection accuracy according to claim 6 is characterized in that the Spectrum Method that breathing rate is calculated comprises Fourier transform and chirp z transform.
8. a kind of method that improves the breathing rate detection accuracy according to claim 1, it is characterized in that, when being weighted the average computation breathing rate according to the breathing rate of history and current breathing rate, calculate the breathing rate that obtains to utilizing Waveform Method or Spectrum Method, be weighted on average with the breathing rate of history, obtain the current breathing rate.
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| CN107049310B (en) * | 2017-02-22 | 2020-01-17 | 广东工业大学 | EMG (electromyography) preprocessing method based on empirical mode decomposition |
| CN108186018A (en) * | 2017-11-23 | 2018-06-22 | 苏州朗开信通信息技术有限公司 | A kind of breath data processing method and processing device |
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| CN112932457A (en) * | 2021-01-26 | 2021-06-11 | 四川大学 | Respiratory system health monitoring method and device |
| CN113892913A (en) * | 2021-10-08 | 2022-01-07 | 珠海格力电器股份有限公司 | Prompt message generation method and device, electronic equipment and storage medium |
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