CN106344022B - A kind of respiratory rate extracting method and device - Google Patents
A kind of respiratory rate extracting method and device Download PDFInfo
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- CN106344022B CN106344022B CN201610828637.9A CN201610828637A CN106344022B CN 106344022 B CN106344022 B CN 106344022B CN 201610828637 A CN201610828637 A CN 201610828637A CN 106344022 B CN106344022 B CN 106344022B
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- 230000036387 respiratory rate Effects 0.000 title claims abstract description 163
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Abstract
The invention discloses a kind of respiratory rate extracting methods, comprising: obtains original electro-cardiologic signals, and to obtaining electrocardiosignal to be processed after the original electro-cardiologic signals notch filter；Kalman filtering is carried out to the electrocardiosignal to be processed, extraction obtains the first breath signal, and first respiratory rate at current time is calculated according to first breath signal；Wavelet transformation is carried out to the electrocardiosignal to be processed and obtains the second breath signal, and second respiratory rate at current time is calculated according to second breath signal；Based on the Kalman's residual error generated in Kalman filtering process, the first weight factor corresponding with first breath signal is calculated；Based on signal quality index, second breath signal is analyzed, obtains corresponding second weight factor；The respiratory rate at current time is calculated.The invention also discloses a kind of respiratory rate extraction element, can convenient effective extraction breath signal, the respiratory rate of accurate stable is calculated.
Description
Technical field
The present invention relates to breathing detection field more particularly to a kind of respiratory rate extracting method and devices.
Background technique
Breathing is the important physiology course of human body, and the monitoring detection to human body respiration is also the one of modern medicine monitoring technology
A important component.Patient whether the lesion of respiratory system itself or the pathological development of other important organs to certain journey
Degree can all influence respiratory center.The failure of respiratory function is often involved in the failure of multi viscera system function, and the failure of respiratory function is again
Lead to the failure of other organs function, reciprocal causation.
The prior art mainly detects respiratory movement using following method: impedance volumetric method: measuring chest with high-frequency constant current source
Respiration information is extracted in the variation of portion's impedance；Sensor method: temperature, pressure, humidity and gas flow transducer is used to pass as nostril
Sensor；Capacitance method: capacitance is caused to generate corresponding variation when breathing；Breath sound method: by picking up breath sound identification of breathing；
Ultrasonic method: Doppler phenomenon is generated using ultrasonic wave, detects respiratory rate.It not only needs to increase signal using these methods and adopt
Collect component, and the shadow noon by movement and environment, is not suitable for daily monitoring.
A large amount of clinical datas show that respiratory movement can cause the variation of electrocardiogram.By electrocardiogram, we can observe that
By the change of the caused ecg wave form peak-to-peak value of chest exercise and cardiac position variation within the respiratory cycle.This is because breathing
In period, the heart electric axis rotation in description heart electric wave main propagation direction causes QRS complex form to be changed.QRS wave
Refer to the maximum wave group of amplitude in normal ECG, reflects the overall process of sequences of ventricular depolarization.Normal ventricle depolarization starts from interventricular septum
Portion, direction depolarization from left to right, therefore one small downward q wave is first presented in QRS complex.Normal chest leads QRS complex form is more permanent
It is fixed.It is a kind of breath signal detection skill that breath signal (ECG-DerivedRespiration, EDR) is extracted from electrocardiosignal
Art, this technology do not need sensor special and hardware module detection breath signal, it is only necessary to obtain electrocardio with ECG monitor
Signal avoids constraint of the above two detection method to human body, makes it possible dynamic breathing detection.
But the existing technology that breath signal is extracted from electrocardiosignal, Waveform Method is mainly used when calculating, and this method is logical
The average value (i.e. baseline value) of interior waveform after a period of time rises or falls trend to determine that current respiratory wave is in, uses extreme value
Method acquire the wave crest of waveform, trough.Effective wave crest or trough are determined according to certain threshold condition, further according to effective
The period of wave crest or trough calculates wave period, to obtain respiratory rate.Although this algorithm has, relatively more intuitive, operand is small
The advantages of, but the respiratory waveform obtained in the actual process more or less will receive the influence of electrocardio-activity, when base occurs in waveform
Line drift about when, the baseline value of calculating can not update quickly, and will lead to waveform missing inspection causes respiratory rate value relatively low, result have compared with
Large deviation.
Summary of the invention
In view of the above-mentioned problems, can not moved the purpose of the present invention is to provide a kind of breathing extracting method and device
In the case where influence with environment, the detection of respiratory rate accurate stable is realized.
The present invention provides a kind of respiratory rate extracting methods, which comprises
Original electro-cardiologic signals are obtained, and obtain electrocardio letter to be processed after carrying out notch filter to the original electro-cardiologic signals
Number；
Kalman filtering carried out to the electrocardiosignal to be processed, extraction obtains the first breath signal, and according to described the
First respiratory rate at current time is calculated in one breath signal；
Wavelet transformation is carried out to the electrocardiosignal to be processed and obtains the second breath signal, and is believed according to second breathing
Second respiratory rate at current time number is calculated；
Based on the Kalman's residual error generated in Kalman filtering process, it is calculated corresponding with first breath signal
The first weight factor；
Based on signal quality index, second breath signal is analyzed, is obtained and second breath signal pair
The second weight factor answered；
According to first respiratory rate, the first weight factor, the second respiratory rate and the second weight factor, it is calculated current
The respiratory rate at moment.
Preferably, before carrying out wavelet transformation to the electrocardiosignal to be processed and obtaining the second breath signal, further includes:
The electrocardiosignal to be processed is carried out down-sampled.
Preferably, described to carry out Kalman filtering to the electrocardiosignal to be processed, extraction obtains the first breath signal, and
First respiratory rate at current time is calculated according to first breath signal specifically:
Kalman filtering is carried out to the electrocardiogram (ECG) data to be processed, extraction obtains the first breath signal；
From the time interval extracted in first breath signal between two wave crests being newly generated；
Sampling rate conversion, which is carried out, according to the time interval obtains first respiratory rate at current time.
Preferably, first weight factor by pairAcquisition is normalized, whereinFor Kalman's residual error, KkFor the gain coefficient of Kalman's residual error.
Preferably, described that second breath signal is obtained to the electrocardiosignal progress wavelet transformation to be processed, and according to institute
State the second respiratory rate that current time is calculated in the second breath signal specifically:
Frequency range layering is carried out according to Shannon-nyquist sampling principle and the sample frequency of the electrocardiosignal to be processed,
Every layer of frequency range is calculated；
Wavelet decomposition and reconstruct institute are determined according to every layer in frequency range layering of frequency range and preset band connection frequency
The number of plies needed；
Signal decomposition is carried out according to the number of plies needed for the wavelet decomposition and pre-selected morther wavelet, is obtained by frequency range
The multi-layer corrugated of division；
The multi-layer corrugated obtained according to coefficient corresponding with the number of plies needed for the wavelet reconstruction and decomposition carries out letter
Number reconstruct, obtain the second breath signal；
From the time interval extracted in second breath signal between two wave crests being newly generated；
Sampling rate conversion, which is carried out, according to the time interval obtains second respiratory rate at current time.
Preferably, the upper cut-off frequency of the band connection frequency is 0.8Hz, and the lower-cut-off frequency of the band connection frequency is
0.1Hz。
It is preferably, described according to first respiratory rate, the first weight factor, the second respiratory rate and the second weight factor,
The respiratory rate at current time is calculated specifically:
When judging that first weight factor is greater than preset a reference value and second weight factor and is less than the benchmark
When value, it sets first respiratory rate to the respiratory rate at current time；
When judging that first weight factor is less than preset a reference value and second weight factor and is greater than the benchmark
When value, it sets second respiratory rate to the respiratory rate at current time；
When judging that first weight factor and second weight factor are all larger than preset a reference value, according to described
First weight factor and second weight factor are weighted summation to first respiratory rate and the second respiratory rate, calculate
To the respiratory rate at current time.
The present invention provides a kind of respiratory rate extraction elements, comprising:
Notch filter unit, for obtaining original electro-cardiologic signals, and after carrying out notch filter to the original electro-cardiologic signals
Obtain electrocardiosignal to be processed；
Filter unit carries out Kalman filtering to the electrocardiosignal to be processed and obtains the first breath signal, and according to institute
State the first respiratory rate that current time is calculated in the first breath signal；
Wavelet transform unit carries out wavelet transformation to the electrocardiosignal to be processed and obtains the second breath signal, and according to
Second respiratory rate at current time is calculated in second breath signal；
First weight factor computing unit, for being obtained based on the Kalman's residual computations generated in Kalman filtering process
To the first weight factor corresponding with first breath signal；
Second weight factor computing unit, for being analyzed based on signal quality index second breath signal,
The second weight factor corresponding with second breath signal is calculated；
Respiratory rate computing unit, for according to first respiratory rate, the first weight factor, the second respiratory rate and the second power
The respiratory rate at current time is calculated in repeated factor.
Preferably, the filter unit specifically includes:
Submodule is filtered, for being filtered using Kalman filter to the electrocardiosignal to be processed, extraction is obtained
First breath signal；
First time interval extraction module, for extracted from first breath signal be newly generated two wave crests it
Between time interval；
First respiratory rate computing module obtains the of current time for carrying out sampling rate conversion according to the time interval
One respiratory rate.
Preferably, the wavelet transform unit specifically includes:
Frequency range is divided into module, for the sampling according to Shannon-nyquist sampling principle and the electrocardiosignal to be processed
Frequency carries out frequency range layering, and every layer of frequency range is calculated；
Number of plies determining module, for being layered every layer of frequency range according to the frequency range and band connection frequency determines wavelet decomposition
With the number of plies needed for reconstruct；
Signal decomposition module, for carrying out letter according to the number of plies needed for the wavelet decomposition and pre-selected morther wavelet
Number decompose, obtain by frequency range divide multi-layer corrugated；
Signal reconstruction module, the institute for being obtained according to coefficient corresponding with the number of plies needed for the wavelet reconstruction and decomposition
It states multi-layer corrugated and carries out signal reconstruction, obtain the second breath signal；
Second time interval extraction module, for extracted from second breath signal be newly generated two wave crests it
Between time interval；
Second respiratory rate computing module obtains the of current time for carrying out sampling rate conversion according to the time interval
Two respiratory rates.
Preferably, the respiratory rate computing unit specifically includes:
First judgment module judges that first weight factor is greater than preset a reference value and second weight for working as
When the factor is less than a reference value, it sets first respiratory rate to the respiratory rate at current time；
Second judgment module judges that first weight factor is less than preset a reference value and second weight for working as
When the factor is greater than a reference value, it sets second respiratory rate to the respiratory rate at current time；
Third judgment module, for preset when judging that first weight factor and second weight factor are all larger than
When a reference value, according to first weight factor and second weight factor to first respiratory rate and the second respiratory rate into
The respiratory rate at current time is calculated in row weighted sum.
Respiratory rate extracting method provided by the invention and device, by utilizing Kalman Filter Technology and wavelet transformation technique
The mode processing cardioelectric signals combined obtain the first respiratory rate and the second respiratory rate, and according to corresponding with first respiratory rate
The first weight factor and the second weight factor corresponding with second respiratory rate obtain the respiratory rate at current time, compared to
Existing to obtain the scheme of breath signal from electrocardiosignal by monotechnics, calculated result is more acurrate reliable, and can reduce due to outer
Measurement fluctuation or error caused by the interference of boundary or environment, so as to obtain more accurate stable measurement result.
Detailed description of the invention
Fig. 1 is the flow chart of respiratory rate extracting method according to an embodiment of the invention.
Fig. 2 is the waveform diagram of original electro-cardiologic signals according to an embodiment of the invention.
Fig. 3 is electrocardiosignal figure after notch filter according to an embodiment of the invention.
Fig. 4 is the waveform diagram of the first breath signal extracted after Kalman filtering according to an embodiment of the invention.
Fig. 5 is the waveform diagram for the second breath signal that wavelet transformation according to an embodiment of the invention extracts.
Fig. 6 is the structural schematic diagram of respiratory rate extraction element according to an embodiment of the invention.
Fig. 7 is the structural schematic diagram of filter unit according to an embodiment of the invention.
Fig. 8 is the structural schematic diagram of wavelet transform unit according to an embodiment of the invention.
Fig. 9 is the structural schematic diagram of respiratory rate computing unit according to an embodiment of the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The present invention provides a kind of respiratory rate extracting methods, for extracting respiration information from electrocardiosignal, due to exhaling
Baseline drift in electrocardiogram caused by suction acts on, regards respiration information as the low-frequency component of electrocardiosignal, is exhaled by removal
The signal other than frequency is inhaled, thus the respiration information extracted needed for obtaining.
Referring to Fig. 1, the embodiment of the invention provides a kind of respiratory rate extracting methods, include the following steps:
S1 obtains original electro-cardiologic signals, and obtains electrocardio to be processed after carrying out notch filter to the original electro-cardiologic signals
Signal.
Referring to fig. 2, for according to the waveform diagram of the original electro-cardiologic signals of one embodiment of the invention.The original electro-cardiologic signals packet
It containing a large amount of Hz noise, needs to carry out 50Hz notch filter, is real according to the present invention one referring to Fig. 3 to filter out Hz noise
Apply electrocardiosignal figure after the notch filter of example.
It should be noted that in embodiments of the present invention, wavelet transformation is carried out due to needing to treat processing cardioelectric signals, if
The sample frequency of the original electro-cardiologic signals is larger (usually 500Hz), then the number of plies that will lead to wavelet decomposition is too high, calculation amount
Increase, is unfavorable for subsequent wavelet transformation.Therefore, down-sampled processing need to be carried out to electrocardiosignal to be processed after notch filter.
It in embodiments of the present invention, can be down-sampled to 100Hz by the electrocardiosignal to be processed.It certainly, can also be according to reality
The needs on border are down-sampled to other frequencies, such as 50Hz, 200Hz etc., and the present invention is not specifically limited.
S2 carries out Kalman filtering to the electrocardiosignal to be processed, and extraction obtains the first breath signal, and according to described
First respiratory rate at current time is calculated in first breath signal.
In embodiments of the present invention, it can be realized by the way that the electrocardiosignal to be processed to be input in Kalman filter
Kalman filtering is carried out to the electrocardiosignal to be processed, the course of work of the Kalman filter is substantially are as follows:
Process is estimated, the prior estimate to current state is established using time update equation, calculates current shape forward in time
The value of state variable and error covariance estimation constructs priori estimates for next time state.
Correction course, using state renewal equation on the basis of the priori estimates for process of estimating and current measurand
Set up the improved Posterior estimator to current state.
Wherein, the time update equation of the Kalman filter are as follows:
Pk=APk-1AT+Q(2)
Wherein, the kalman filter state renewal equation are as follows:
Kk=Pk-HT(HPk-HT+R)-2 (3)
Pk=(1-KkH)Pk- (5)
Wherein,For kth walks in situation known to the state before kth step prior state estimated value (- represents elder generation
It tests, ^ represents estimation)；
A is to act on Xk-1On n × n-state transformation matrix；
B is to act on dominant vector Uk-1On the input control matrix of n × 1；
H is m × n observation model matrix, it is time of day space reflection at observation space；
PkIt is n × n prior estimate error covariance matrix；
PkFor n × n Posterior estimator error co-variance matrix；
R is n × n process noise covariance matrix；
I is n × n rank unit matrix；
Refer to Kalman's residual error；
KkIt is the gain coefficient of Kalman's residual error for n × m rank matrix, referred to as kalman gain or mixing factor, effect
It is to keep Posterior estimator error covariance minimum.
In embodiments of the present invention, after obtaining first breath signal, referring to fig. 4, to be implemented according to the present invention one
The waveform diagram for the first breath signal that the filter of example extracts.First breath signal is handled and can be calculated
To the first respiratory rate R1, specifically:
By asking extremum method to find wave crest (or the wave of the first breath signal in the waveform diagram of first breath signal
Paddy), referring to fig. 4 in point label.
By extracting the time interval between two wave crests being newly generated, to obtain the cycle T 1 at current time.
Carrying out sampling rate conversion to the period can be obtained the first respiratory rate R1 at current time.
For example, R1=60/T1.
S3 carries out wavelet transformation to the electrocardiosignal to be processed and obtains the second breath signal, and exhales according to described second
Inhale the second respiratory rate that current time is calculated in signal.
In embodiments of the present invention, step S3 is specifically included:
S31 carries out frequency range point according to Shannon-nyquist sampling principle and the sample frequency of the electrocardiosignal to be processed
Layer, is calculated every layer of frequency range.
According to Shannon-nyquist sampling principle, if the sample frequency of electrocardiosignal to be processed is fs, target frequency bands are
F1-f2 (Hz) is N with the number of plies that wavelet transformation decomposes, by Nyquist law it is found that then having:
F1=(fs/2)/2N1 (6)
F2=(fs/2)/2N2(7)
N>N1(N1>N2) (8)
The number of plies for the small echo for needing to reconstruct is N2~N1 layers.
S32 determines wavelet decomposition and again according to every layer in frequency range layering of frequency range and preset band connection frequency
The number of plies needed for structure；
Assuming that the electrocardiosignal to be processed is down-sampled to 100Hz, then fs is 100Hz, and signal highest frequency is 50Hz,
According to formula (6), (7), (8) it is found that the corresponding frequency range of each layer is as follows:
| Frequency range | Frequency range/Hz | Frequency range | Frequency range/Hz |
| A1 | 0~25 | D1 | 25~50 |
| A2 | 1~12.5 | D2 | 12.5~25 |
| A3 | 0~6.25 | D3 | 6.25~12.5 |
| A4 | 0~3.125 | D4 | 3.125~6.25 |
| A5 | 0~1.625 | D5 | 1.625~3.125 |
| A6 | 0~0.8125 | D6 | 0.8125~1.625 |
| A7 | 0~0.40625 | D7 | 0.40625~0.8125 |
| A8 | 0~0.203125 | D8 | 0.203125~0.40625 |
| A9 | 0~0.10156 | D9 | 0.10156~0.203125 |
Since the band limits of breath signal is usually 0.1~0.4Hz, it is contemplated that the case where being short of breath expands frequency range
Exhibition is 0.1~0.8Hz, and cardiac electrical frequency range is 0.9~6Hz, therefore, can separate breath signal and electrocardio letter well
Number, so, select the 9th, 8,7 layer of approximation coefficient (D9/D8/D7) to carry out reconstruction signal.
S33 carries out signal decomposition according to the number of plies needed for the wavelet decomposition and pre-selected morther wavelet, obtain by
The multi-layer corrugated that frequency range divides.
In embodiments of the present invention, verified, the extraction effect of coifN small echo and dmey small echo is preferable, and preferably, with
When coif3 wavelet basis is as morther wavelet, there is optimal extraction effect.Thus the embodiment of the present invention is made using coif3 wavelet basis
Wavelet decomposition is carried out for morther wavelet.It is, of course, understood that in other embodiments of the invention, others can also be chosen
Morther wavelet, such as db small echo, the present invention is not specifically limited.
In embodiments of the present invention, when decomposing, N layers of waveform can be obtained, at this point, can extract N2~N1 layers of corresponding waveform
To be reconstructed.
S34, according to coefficient corresponding with the number of plies needed for the wavelet reconstruction and decompose the obtained multi-layer corrugated into
Row signal reconstruction obtains the second breath signal.
As step S33 it is found that the number of plies needed for wavelet reconstruction be the 9th, 8,7 layer, at this point, can according to the small echo weight
The multi-layer corrugated that the corresponding coefficient of the number of plies needed for structure and decomposition obtain carries out signal reconstruction, obtains the second breath signal.
Referring to Fig. 5, for the waveform diagram of the second breath signal extracted according to the wavelet transformation of one embodiment of the invention.
In embodiments of the present invention, after obtaining second breath signal, the second respiratory rate R2 can be calculated, specifically
Are as follows:
By asking extremum method to find wave crest (or the wave of the second breath signal in the waveform diagram of second breath signal
Paddy), it is marked referring to the point in Fig. 5.
By extracting the time interval between two wave crests being newly generated, to obtain cycle T 2.
Real-time second respiratory rate R2 can be obtained according to sampling rate conversion.
S4 is calculated and first breath signal based on the Kalman's residual error generated in Kalman filtering process
Corresponding first weight factor；
In embodiments of the present invention, step S4 specifically:
By rightIt is normalized and obtains first weight factor, whereinFor Kalman's residual error, KkFor the gain coefficient of Kalman's residual error.
S5 is based on signal quality index, analyzes second breath signal, obtain and second breath signal
Corresponding second weight factor；
In embodiments of the present invention, specifically, institute can be analyzed by carrying out power spectrumanalysis to second breath signal
The Spectral structure for stating the second breath signal obtains the second weight factor corresponding with second breath signal.
It certainly, can also be described to be calculated by analyzing the distribution of the peak value spectrum or other spectrums of second breath signal
Second weight factor, the present invention is not specifically limited.
S6 is calculated according to first respiratory rate, the first weight factor, the second respiratory rate and the second weight factor
The respiratory rate at current time.
In embodiments of the present invention, it can be calculated by being weighted and averaged to the first respiratory rate R1 and the second respiratory rate R2
Obtain the respiratory rate R at current time.
That is:
R=μ 1*R1+ μ 2*R2 (9)
Wherein, μ 1 is the first weight factor, and μ 2 is the second weight factor.
It should be noted that before being weighted and averaged need that first μ 1 and μ 2 is normalized, it is specifically, false
If μ 1+ μ 2=a, then need that μ 1 and μ 2 are normalized multiplied by normalization coefficient 1/a respectively, the μ 1+ μ 2 after guaranteeing normalization
=1.
Respiratory rate extracting method provided by the invention, by being combined using Kalman Filter Technology with wavelet transformation technique
Mode processing cardioelectric signals obtain the first respiratory rate and the second respiratory rate, and according to first respiratory rate corresponding first
Weight factor and the second weight factor corresponding with second respiratory rate obtain the respiratory rate at current time, compared to it is existing by
Monotechnics obtain the scheme of breath signal from electrocardiosignal, and calculated result is more acurrate reliable, and can reduce due to extraneous or ring
Measurement fluctuation or error caused by the interference in border, so as to obtain more accurate stable measurement result.
It should be noted that calculating knot caused by the first breath signal or the second breath signal are second-rate in order to prevent
Fruit inaccuracy, in a preferred embodiment of the invention, the step S6 may be used also are as follows:
S61, when judge first weight factor be greater than preset a reference value and second weight factor be less than it is described
When a reference value, it sets first respiratory rate to the respiratory rate at current time.
When second weight factor be less than a reference value when, it is believed that the signal quality of the second breath signal compared with
Difference, at this point, directly setting the first respiratory rate R1 to the respiratory rate R at current time.
S62, when judge first weight factor be less than preset a reference value and second weight factor be greater than it is described
When a reference value, it sets second respiratory rate to the respiratory rate at current time.
When first weight factor be less than a reference value when, it is believed that the signal quality of the first breath signal compared with
Difference, at this point, directly setting the first respiratory rate R1 to the respiratory rate R at current time.
S63, when judging that first weight factor and second weight factor are all larger than preset a reference value, according to
First weight factor and second weight factor are weighted summation to first respiratory rate and the second respiratory rate, meter
Calculation obtains the respiratory rate at current time.
That is: R=μ 1*R1+ μ 2*R2.
In this preferred embodiment, if weight factor is smaller, illustrates that corresponding breath signal is second-rate, then directly go
Fall respiratory rate corresponding with second-rate breath signal, guarantee the accurate of calculated result and stablizes.
Refering to Fig. 6, the embodiment of the present invention also provides a kind of respiratory rate extraction element 100, comprising:
Notch filter unit 10 carries out notch filter for obtaining original electro-cardiologic signals, and to the original electro-cardiologic signals
After obtain electrocardiosignal to be processed.
Filter unit 20 carries out Kalman filtering to the electrocardiosignal to be processed and obtains the first breath signal, and according to
First respiratory rate at current time is calculated in first breath signal.
Wavelet transform unit 30 carries out wavelet transformation to the electrocardiosignal to be processed and obtains the second breath signal, and root
Second respiratory rate at current time is calculated according to second breath signal.
First weight factor computing unit 40, for based on the Kalman's residual computations generated in Kalman filtering process
Obtain the first weight factor corresponding with first breath signal；
Second weight factor computing unit 50, for being divided based on signal quality index second breath signal
Analysis, is calculated the second weight factor corresponding with second breath signal；
Respiratory rate computing unit 60, for according to first respiratory rate, the first weight factor, the second respiratory rate and second
The respiratory rate at current time is calculated in weight factor.
Wherein it is preferred to the respiratory rate extraction element 100 further include:
Down-sampled unit, it is down-sampled for being carried out to the electrocardiosignal to be processed.
Wherein, as shown in Figure 7, it is preferable that the filter unit 20 specifically includes:
Submodule 21 is filtered, for carrying out Kalman filtering to the electrocardiosignal to be processed, extraction obtains the first breathing
Signal；
First time interval extraction module 22, for extracting be newly generated two wave crests from first breath signal
Between time interval；
First respiratory rate computing module 23 obtains current time for carrying out sampling rate conversion according to the time interval
First respiratory rate.
Wherein, as shown in Figure 8, it is preferable that the wavelet transform unit 30 specifically includes:
Frequency range hierarchical block 31, for adopting according to Shannon-nyquist sampling principle and the electrocardiosignal to be processed
Sample frequency carries out frequency range layering, and every layer of frequency range is calculated；
Number of plies determining module 32 determines small wavelength-division for being layered every layer of frequency range and band connection frequency according to the frequency range
The number of plies needed for solution and reconstruct；
Signal decomposition module 33, for being carried out according to the number of plies needed for the wavelet decomposition and pre-selected morther wavelet
Signal decomposition obtains the multi-layer corrugated divided by frequency range；
Signal reconstruction module 34, for what is obtained according to coefficient corresponding with the number of plies needed for the wavelet reconstruction and decomposition
The multi-layer corrugated carries out signal reconstruction, obtains the second breath signal；
Second time interval extraction module 35, for extracting be newly generated two wave crests from second breath signal
Between time interval；
Second respiratory rate computing module 36 obtains current time for carrying out sampling rate conversion according to the time interval
Second respiratory rate.
Wherein, as shown in Figure 9, it is preferable that the respiratory rate computing unit 60 specifically includes:
First judgment module 61 judges that first weight factor is greater than preset a reference value and second power for working as
When repeated factor is less than a reference value, it sets first respiratory rate to the respiratory rate at current time；
Second judgment module 62 judges that first weight factor is less than preset a reference value and second power for working as
When repeated factor is greater than a reference value, it sets second respiratory rate to the respiratory rate at current time；
Third judgment module 63, for default when judging that first weight factor and second weight factor are all larger than
A reference value when, according to first weight factor and second weight factor to first respiratory rate and the second respiratory rate
It is weighted summation, the respiratory rate at current time is calculated.
Respiratory rate extraction element 100 provided by the invention, by utilizing Kalman Filter Technology and wavelet transformation technique phase
In conjunction with mode processing cardioelectric signals obtain the first respiratory rate and the second respiratory rate, and current time is obtained by weighted sum
Respiratory rate obtains the scheme of breath signal from electrocardiosignal by monotechnics compared to existing, and calculated result is more acurrate reliable, and
It can reduce measurement fluctuation or error due to caused by extraneous or environment interference, so as to obtain more accurate stable survey
Measure result.
Above disclosed is only a preferred embodiment of the present invention, cannot limit the power of the present invention with this certainly
Sharp range, those skilled in the art can understand all or part of the processes for realizing the above embodiment, and weighs according to the present invention
Benefit requires made equivalent variations, still belongs to the scope covered by the invention.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, the program can be stored in a computer-readable storage medium
In, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, the storage medium can be magnetic
Dish, CD, read-only memory (Read-Only Memory, ROM) or random access memory (Random Access
Memory, RAM) etc..
Claims (11)
1. a kind of respiratory rate extracting method characterized by comprising
Original electro-cardiologic signals are obtained, and obtain electrocardiosignal to be processed after carrying out notch filter to the original electro-cardiologic signals；
Kalman filtering is carried out to the electrocardiosignal to be processed, extraction obtains the first breath signal, and exhales according to described first
Inhale the first respiratory rate that current time is calculated in signal；
Wavelet transformation is carried out to the electrocardiosignal to be processed and obtains the second breath signal, and according to the second breath signal meter
Calculation obtains second respiratory rate at current time；
Based on the Kalman's residual error generated in Kalman filtering process, it is calculated corresponding with first breath signal
One weight factor；
Based on signal quality index, second breath signal is analyzed, is obtained corresponding with second breath signal
Second weight factor；
According to first respiratory rate, the first weight factor, the second respiratory rate and the second weight factor, current time is calculated
Respiratory rate.
2. respiratory rate extracting method according to claim 1, which is characterized in that carried out to the electrocardiosignal to be processed
Wavelet transformation obtains before the second breath signal, further includes:
The electrocardiosignal to be processed is carried out down-sampled.
3. respiratory rate extracting method according to claim 1, which is characterized in that it is described to the electrocardiosignal to be processed into
Row Kalman filtering, extraction obtain the first breath signal, and are calculated the of current time according to first breath signal
One respiratory rate specifically:
Kalman filtering is carried out to the electrocardiogram (ECG) data to be processed, extraction obtains the first breath signal；
From the time interval extracted in first breath signal between two wave crests being newly generated；
Sampling rate conversion, which is carried out, according to the time interval obtains first respiratory rate at current time.
4. respiratory rate extracting method according to claim 1, which is characterized in that first weight factor by pairAcquisition is normalized, whereinFor Kalman's residual error, KkIt is residual for the Kalman
The gain coefficient of difference.
5. respiratory rate extracting method according to claim 1, which is characterized in that it is described to the electrocardiosignal to be processed into
Row wavelet transformation obtains the second breath signal, and second respiratory rate at current time is calculated according to second breath signal
Specifically:
Frequency range layering is carried out according to Shannon-nyquist sampling principle and the sample frequency of the electrocardiosignal to be processed, is calculated
Obtain every layer of frequency range；
It is determined needed for wavelet decomposition and reconstruct according to every layer in frequency range layering of frequency range and preset band connection frequency
The number of plies；
Signal decomposition is carried out according to the number of plies needed for the wavelet decomposition and pre-selected morther wavelet, obtains dividing by frequency range
Multi-layer corrugated；
According to coefficient corresponding with the number of plies needed for the wavelet reconstruction and decompose obtained multi-layer corrugated progress signal weight
Structure obtains the second breath signal；
From the time interval extracted in second breath signal between two wave crests being newly generated；
Sampling rate conversion, which is carried out, according to the time interval obtains second respiratory rate at current time.
6. respiratory rate extracting method according to claim 5, which is characterized in that the upper cut-off frequency of the band connection frequency
For 0.8Hz, the lower-cut-off frequency of the band connection frequency is 0.1Hz.
7. respiratory rate extracting method according to claim 1, which is characterized in that described according to first respiratory rate,
One weight factor, the second respiratory rate and the second weight factor, are calculated the respiratory rate at current time specifically:
When judging that first weight factor is greater than preset a reference value and second weight factor is less than a reference value,
Set first respiratory rate to the respiratory rate at current time；
When judging that first weight factor is less than preset a reference value and second weight factor is greater than a reference value,
Set second respiratory rate to the respiratory rate at current time；
When judging that first weight factor and second weight factor are all larger than preset a reference value, according to described first
Weight factor and second weight factor are weighted summation to first respiratory rate and the second respiratory rate, are calculated and work as
The respiratory rate at preceding moment.
8. a kind of respiratory rate extraction element characterized by comprising
Notch filter unit is obtained for obtaining original electro-cardiologic signals, and after carrying out notch filter to the original electro-cardiologic signals
Electrocardiosignal to be processed；
Filter unit carries out Kalman filtering to the electrocardiosignal to be processed and obtains the first breath signal, and according to described the
First respiratory rate at current time is calculated in one breath signal；
Wavelet transform unit carries out wavelet transformation to the electrocardiosignal to be processed and obtains the second breath signal, and according to described
Second respiratory rate at current time is calculated in second breath signal；
First weight factor computing unit, for obtained based on the Kalman's residual computations generated in Kalman filtering process with
Corresponding first weight factor of first breath signal；
Second weight factor computing unit is calculated for being analyzed based on signal quality index second breath signal
Obtain the second weight factor corresponding with second breath signal；
Respiratory rate computing unit, for according to first respiratory rate, the first weight factor, the second respiratory rate and the second weight because
The respiratory rate at current time is calculated in son.
9. respiratory rate extraction element according to claim 8, which is characterized in that the filter unit specifically includes:
Submodule is filtered, for being filtered using Kalman filter to the electrocardiosignal to be processed, extraction obtains first
Breath signal；
First time interval extraction module, for from being extracted in first breath signal between be newly generated two wave crests
Time interval；
First respiratory rate computing module is exhaled for obtaining the first of current time according to time interval progress sampling rate conversion
Suction rate.
10. respiratory rate extraction element according to claim 8, which is characterized in that the wavelet transform unit specifically includes:
Frequency range is divided into module, for the sample frequency according to Shannon-nyquist sampling principle and the electrocardiosignal to be processed
Frequency range layering is carried out, every layer of frequency range is calculated；
Number of plies determining module, for being layered every layer of frequency range according to the frequency range and band connection frequency determines wavelet decomposition and again
The number of plies needed for structure；
Signal decomposition module, for carrying out signal point according to the number of plies needed for the wavelet decomposition and pre-selected morther wavelet
Solution obtains the multi-layer corrugated divided by frequency range；
Signal reconstruction module is described more for being obtained according to coefficient corresponding with the number of plies needed for the wavelet reconstruction and decomposition
Layer waveform carries out signal reconstruction, obtains the second breath signal；
Second time interval extraction module, for from being extracted in second breath signal between be newly generated two wave crests
Time interval；
Second respiratory rate computing module is exhaled for obtaining the second of current time according to time interval progress sampling rate conversion
Suction rate.
11. according to respiratory rate extraction element according to any one of claims 8, which is characterized in that the respiratory rate computing unit specifically includes:
First judgment module judges that first weight factor is greater than preset a reference value and second weight factor for working as
When less than a reference value, it sets first respiratory rate to the respiratory rate at current time；
Second judgment module judges that first weight factor is less than preset a reference value and second weight factor for working as
When greater than a reference value, it sets second respiratory rate to the respiratory rate at current time；
Third judgment module judges that first weight factor and second weight factor are all larger than preset benchmark for working as
When value, first respiratory rate and the second respiratory rate are added according to first weight factor and second weight factor
Power summation, is calculated the respiratory rate at current time.
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