CN106344022A - Respiratory rate extraction method and device - Google Patents
Respiratory rate extraction method and device Download PDFInfo
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- CN106344022A CN106344022A CN201610828637.9A CN201610828637A CN106344022A CN 106344022 A CN106344022 A CN 106344022A CN 201610828637 A CN201610828637 A CN 201610828637A CN 106344022 A CN106344022 A CN 106344022A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7221—Determining signal validity, reliability or quality
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7225—Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
Abstract
The invention discloses a respiratory rate extraction method. The method comprises steps as follows: acquiring an original ECG (electrocardiogram) signal, and performing power frequency notching on the original ECG signal to obtain a to-be-processed ECG signal; performing Kalman filtering on the to-be-processed ECG signal, performing extraction to obtain a first respiration signal, and calculating a first respiratory rate at the current moment according to the first respiration signal; performing wavelet transformation on the to-be-processed ECG signal to obtain a second respiration signal, and calculating a second respiratory rate at the current moment according to the second respiration signal; calculating a first weight factor corresponding to the first respiration signal on the basis of a Kalman residual generated in the Kalman filtering process; analyzing the second respiration signal on the basis of a signal quality index to obtain a corresponding second weight factor; calculating the respiratory rate at the current moment. The invention further discloses a respiratory rate extraction device. The respiration signals can be extracted conveniently and effectively and the accurate and stable respiratory rates can be calculated.
Description
Technical field
The present invention relates to respiration detection field, more particularly, to a kind of breathing rate extracting method and device.
Background technology
Breathing is the important physiological process of human body, and the monitoring detection to human body respiration is also the one of modern medicine monitoring technology
Individual important component part.Patient whether the pathological changes of respiratory system itself or the pathological development of other important organs to certain journey
Degree all can affect respiratory center.The exhaustion of respiratory function is often involved in multi viscera systemic-function exhaustion, and the exhaustion of respiratory function is again
Lead to the exhaustion of other organs function, reciprocal causation.
Prior art is mainly detected using following method to respiratory movement: impedance volumetric method: measure breast with high-frequency constant current source
The change of portion's impedance is extracting respiration information；Sensor method: be used temperature, pressure, humidity and pneumatic sensor to pass as nostril
Sensor；Capacitance method: lead to capacitance to produce corresponding change when breathing；Respiratory murmur method: by picking up respiratory murmur identification of breathing；
Ultrasonic method: produce Doppler phenomenon using ultrasound wave, detect respiratory frequency.Increase signal is not only needed to adopt using these methods
Collection part, and it is subject to the shadow noon of motion and environment, be not suitable for daily monitoring.
A large amount of clinical datas show, respiratory movement can cause Electrocardiographic change.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 change within the breathing cycle.This is due to breathing
In cycle, the heart electric axis rotation in description heart electric wave main propagation direction causes qrs wave group form generation to change.Qrs ripple
Refer to the maximum wave group of amplitude in normal ECG, the overall process of reflection sequences of ventricular depolarization.Normal ventricle depolarization starts from interventricular septum
Portion, direction depolarization from left to right, therefore qrs wave group first assumes a little downward q ripple.Normal chest lead qrs wave group form is more permanent
Fixed.Extracting breath signal (ecg-derivedrespiration, edr) from electrocardiosignal is a kind of breath signal detection skill
Art, this technology does not need sensor special and hardware module detection breath signal it is only necessary to obtain electrocardio with electrocardiogram monitor
Signal, it is to avoid the constraint to human body for the above two detection method, makes the dynamic respiration detection be possibly realized.
But the existing technology extracting breath signal from electrocardiosignal, mainly adopts Waveform Method when calculating, and the method is led to
The meansigma methodss (i.e. baseline value) of interior waveform, to judge that current respiratory wave is in the trend of rising or falling, to use extreme value after a while
Method try to achieve the crest of waveform, trough.Effective crest or trough are judged according to certain threshold condition, further according to effective
The computation of Period wave period of crest or trough, thus obtain breathing rate.Although this algorithm has, comparison is directly perceived, operand is little
Advantage, but in real process obtain respiratory waveform more or less can be affected by electrocardio-activity, when base in waveform
During line drift, the baseline value of calculating cannot update quickly, waveform missing inspection can be led to cause breathing rate value low, its result has relatively
Large deviation.
Content of the invention
For the problems referred to above, it is an object of the invention to provide a kind of breathing extracting method and device, can not moved
In the case of the impact of environment, realize the detection of breathing rate accurate stable.
The invention provides a kind of breathing rate extracting method, methods described includes:
Obtain original electro-cardiologic signals, and described original electro-cardiologic signals are carried out after notch filter, obtain pending electrocardio letter
Number；
Described pending electrocardiosignal is carried out with Kalman filtering, extracts and obtain the first breath signal, and according to described the
One breath signal is calculated the first breathing rate of current time；
Wavelet transformation is carried out to described pending electrocardiosignal and obtains the second breath signal, and according to the described second breathing letter
Number it is calculated the second breathing rate of current time；
Based on the Kalman's residual error generating in Kalman filtering process, it is calculated corresponding with described first breath signal
The first weight factor；
Based on signal quality index, described second breath signal is analyzed, obtains and described second breath signal pair
The second weight factor answered；
According to described first breathing rate, the first weight factor, the second breathing rate and the second weight factor, it is calculated current
The breathing rate in moment.
Preferably, described pending electrocardiosignal is being carried out before wavelet transformation obtains the second breath signal, also include:
Described pending electrocardiosignal is carried out down-sampled.
Preferably, described described pending electrocardiosignal is carried out with Kalman filtering, extract and obtain the first breath signal, and
According to described first breath signal be calculated current time the first breathing rate particularly as follows:
Described pending electrocardiogram (ECG) data is carried out with Kalman filtering, extracts and obtain the first breath signal；
The time interval between two crests being newly generated is extracted from described first breath signal；
The first breathing rate that sampling rate conversion obtains current time is carried out according to described time interval.
Preferably, described first weight factor is by rightIt is normalized acquisition, wherein,For Kalman's residual error, kkGain coefficient for described Kalman's residual error.
Preferably, described wavelet transformation is carried out to described pending electrocardiosignal obtain the second breath signal, and according to institute
State the second breath signal be calculated current time the second breathing rate particularly as follows:
Frequency range layering is carried out according to the sample frequency of Shannon-nyquist sampling principle and described pending electrocardiosignal,
It is calculated every layer of frequency range；
Determine wavelet decomposition and reconstruct institute according to every layer in the layering of described frequency range of frequency range and preset band connection frequency
The number of plies needing；
The number of plies according to needed for described wavelet decomposition and the morther wavelet being pre-selected carry out signal decomposition, obtain by frequency range
The multi-layer corrugated dividing；
The corresponding coefficient of the number of plies according to needed for described wavelet reconstruction and decompose the described multi-layer corrugated that obtains and carry out letter
Number reconstruct, obtain the second breath signal；
The time interval between two crests being newly generated is extracted from described second breath signal；
The second breathing rate that sampling rate conversion obtains current time is carried out according to described time interval.
Preferably, the upper cut-off frequency of described band connection frequency is 0.8hz, and the lower-cut-off frequency of described band connection frequency is
0.1hz.
Preferably, described according to described first breathing rate, the first weight factor, the second breathing rate and the second weight factor,
Be calculated the breathing rate of current time particularly as follows:
When judging that described first weight factor is more than default reference value and described second weight factor and is less than described benchmark
During value, described first breathing rate is set to the breathing rate of current time；
When judging that described first weight factor is less than default reference value and described second weight factor and is more than described benchmark
During value, described second breathing rate is set to the breathing rate of current time；
When judging described first weight factor and described second weight factor is all higher than default reference value, according to described
First weight factor and described second weight factor are weighted to described first breathing rate and the second breathing rate suing for peace, and calculate
Breathing rate to current time.
The invention provides a kind of breathing rate extraction element, comprising:
Described original electro-cardiologic signals for obtaining original electro-cardiologic signals, and are carried out after notch filter by notch filter unit
Obtain pending electrocardiosignal；
Filter unit, carries out Kalman filtering to described pending electrocardiosignal and obtains the first breath signal, and according to institute
State the first breathing rate that the first breath signal is calculated current time；
Wavelet transform unit, carries out wavelet transformation to described pending electrocardiosignal and obtains the second breath signal, and according to
Described second breath signal is calculated the second breathing rate of current time；
First weight factor and calculation unit, for being obtained based on the Kalman's residual computations generating in Kalman filtering process
Arrive first weight factor corresponding with described first breath signal；
Second weight factor and calculation unit, for being analyzed to described second breath signal based on signal quality index,
It is calculated second weight factor corresponding with described second breath signal；
Breathing rate computing unit, for according to described first breathing rate, the first weight factor, the second breathing rate and the second power
Repeated factor, is calculated the breathing rate of current time.
Preferably, described filter unit specifically includes:
Filtering submodule, for being filtered to described pending electrocardiosignal using Kalman filter, extraction obtains
First breath signal；
The very first time be spaced extraction module, for extract from described first breath signal two crests being newly generated it
Between time interval；
First breathing rate computing module, for carry out that sampling rate conversion obtains current time according to described time interval
One breathing rate.
Preferably, described wavelet transform unit specifically includes:
Frequency range is divided into module, for the sampling according to Shannon-nyquist sampling principle and described pending electrocardiosignal
Frequency carries out frequency range layering, is calculated every layer of frequency range；
Number of plies determining module, for being layered every layer of frequency range according to described frequency range and band connection frequency determines wavelet decomposition
The required number of plies with reconstruct；
Signal decomposition module, for according to the number of plies needed for described wavelet decomposition and the morther wavelet that is pre-selected carries out letter
Number decompose, obtain by frequency range divide multi-layer corrugated；
Signal reconstruction module, for the institute being obtained according to coefficient corresponding with the number of plies needed for described wavelet reconstruction and decomposition
State multi-layer corrugated and carry out signal reconstruction, obtain the second breath signal；
Second time interval extraction module, for extract from described second breath signal two crests being newly generated it
Between time interval；
Second breathing rate computing module, for carry out that sampling rate conversion obtains current time according to described time interval
Two breathing rates.
Preferably, described breathing rate computing unit specifically includes:
For working as, first judge module, judges that described first weight factor is more than default reference value and described second weight
When the factor is less than described reference value, described first breathing rate is set to the breathing rate of current time；
For working as, second judge module, judges that described first weight factor is less than default reference value and described second weight
When the factor is more than described reference value, described second breathing rate is set to the breathing rate of current time；
3rd judge module, for when judge described first weight factor and described second weight factor be all higher than default
During reference value, according to described first weight factor and described second weight factor, described first breathing rate and the second breathing rate are entered
Row weighted sum, is calculated the breathing rate of current time.
Breathing rate extracting method and device that the present invention provides, by using Kalman Filter Technology and wavelet transformation technique
The mode processing cardioelectric signals combining obtain the first breathing rate and the second breathing rate, and according to corresponding with described first breathing rate
The first weight factor and second weight factor corresponding with described second breathing rate obtain the breathing rate of current time, compared to
The existing scheme being obtained breath signal by monotechnics from electrocardiosignal, result of calculation more accurately and reliably, and can mitigate due to outer
The interference of boundary or environment and the measurement fluctuation or the error that cause are such that it is able to obtain the measurement result of more accurate stable.
Brief description
Fig. 1 is the flow chart of breathing rate extracting method according to an embodiment of the invention.
Fig. 2 is the oscillogram 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 oscillogram extracting the first breath signal obtaining after Kalman filtering according to an embodiment of the invention.
Fig. 5 is the oscillogram of the second breath signal that wavelet transformation extraction according to an embodiment of the invention obtains.
Fig. 6 is the structural representation of breathing rate extraction element according to an embodiment of the invention.
Fig. 7 is the structural representation of filter unit according to an embodiment of the invention.
Fig. 8 is the structural representation of wavelet transform unit according to an embodiment of the invention.
Fig. 9 is the structural representation of breathing rate computing unit according to an embodiment of the invention.
Specific embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation description is it is clear that described embodiment is only a part of embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, it is every other that those of ordinary skill in the art are obtained under the premise of not making creative work
Embodiment, broadly falls into the scope of protection of the invention.
The invention provides a kind of breathing rate extracting method, for extracting respiration information from electrocardiosignal, due to exhaling
Baseline drift in the electrocardiogram that suction effect causes, regards respiration information as the low-frequency component of electrocardiosignal, is exhaled by removing
Signal beyond suction frequency, thus obtain the respiration information of required extraction.
Referring to Fig. 1, embodiments provide a kind of breathing rate extracting method, comprise the steps:
S1, obtains original electro-cardiologic signals, and described original electro-cardiologic signals is carried out obtain after notch filter with pending electrocardio
Signal.
Referring to Fig. 2, it is the oscillogram of the original electro-cardiologic signals according to one embodiment of the invention.Described original electro-cardiologic signals bag
Containing substantial amounts of Hz noise, need to carry out 50hz notch filter, to filter Hz noise, referring to Fig. 3, be real according to the present invention one
Apply electrocardiosignal figure after the notch filter of example.
It should be noted that in embodiments of the present invention, carry out wavelet transformation due to needing to treat processing cardioelectric signals, if
The sample frequency of described original electro-cardiologic signals is larger (usually 500hz), then the number of plies of wavelet decomposition can be led to too high, amount of calculation
Increase, be unfavorable for follow-up wavelet transformation.Therefore, down-sampled process need to be carried out to electrocardiosignal pending after notch filter.
In embodiments of the present invention, can be by down-sampled for described pending electrocardiosignal to 100hz.Certainly, also dependent on reality
The needs on border down-sampled to other frequencies, such as 50hz, 200hz etc., the present invention is not specifically limited.
S2, carries out Kalman filtering to described pending electrocardiosignal, extracts and obtains the first breath signal, and according to described
First breath signal is calculated the first breathing rate of current time.
In embodiments of the present invention, can be realized by being input to described pending electrocardiosignal in Kalman filter
Kalman filtering is carried out to described pending electrocardiosignal, the work process of described Kalman filter substantially:
Estimate process, set up the prior estimate to current state using time update equation, calculate forward current shape in time
State variable and the value of error covariance estimation, construct priori estimates for next time state.
Trimming process, utilization state renewal equation is on the basis of the priori estimates and current measurand of process of estimating
Set up the improved Posterior estimator to current state.
Wherein, the time update equation of described Kalman filter is:
pk-=apk-1at+q (2)
Wherein, described kalman filter state renewal equation is:
kk=pk-ht(hpk-ht+r)-2(3)
pk=(1-kkh)pk- (5)
Wherein,For kth step before state known in the case of kth step prior state estimated value (- represent priori,
^ represents and estimates)；
A is to act on xk-1On n × n-state transformation matrix；
B is to act on dominant vector uk-1On n × 1 input control matrix；
H is m × n observation model matrix, and it becomes observation space time of day space reflection；
pk- it 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；
kkFor n × m rank matrix, referred to as Kalman gain or mixing factor, it is the gain coefficient of Kalman's residual error, its effect
It is to make Posterior estimator error covariance minimum.
In embodiments of the present invention, after obtaining described first breath signal, referring to Fig. 4, it is according to the present invention one enforcement
The oscillogram of the first breath signal that the wave filter extraction of example obtains.Carry out process to described first breath signal can calculate
To the first breathing rate r1, particularly as follows:
Find crest (or the ripple of the first breath signal by seeking extremum method in the oscillogram of described first breath signal
Paddy), referring to the point labelling in Fig. 4.
By extracting the time interval between two crests being newly generated, to obtain the cycle t1 of current time.
The described cycle is carried out with the first breathing rate r1 that sampling rate conversion can get current time.
For example, r1=60/t1.
S3, carries out wavelet transformation to described pending electrocardiosignal and obtains the second breath signal, and exhales according to described second
Inhale the second breathing rate that signal of change obtains current time.
In embodiments of the present invention, step s3 specifically includes:
S31, carries out frequency range according to the sample frequency of Shannon-nyquist sampling principle and described pending electrocardiosignal and divides
Layer, is calculated every layer of frequency range.
According to Shannon-nyquist sampling principle, if the sample frequency of pending electrocardiosignal is fs, target frequency bands are
F1-f2 (hz), the number of plies decomposed with wavelet transformation is n, from Nyquist law, then has:
F1=(fs/2)/2n1(6)
F2=(fs/2)/2n2(7)
n>n1(n1>n2) (8)
The number of plies needing the small echo reconstructing is n2～n1 layer.
S32, frequency range and preset band connection frequency according to every layer in the layering of described frequency range determine wavelet decomposition and weight
The number of plies needed for structure；
Assume that described pending electrocardiosignal is down-sampled to 100hz, then fs is 100hz, signal highest frequency is 50hz,
Understand, the corresponding frequency range of each layer is as follows according to formula (6), (7), (8):
| 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 |
Because the band limits of breath signal is usually 0.1～0.4hz it is contemplated that Tachypneic situation, frequency range is expanded
Open up as 0.1～0.8hz, cardiac electrical frequency range is 0.9～6hz, therefore, it is possible to separate breath signal and electrocardio letter well
Number, so, the approximation coefficient (d9/d8/d7) from the 9th, 8,7 layers carrys out reconstruction signal.
S33, the number of plies according to needed for described wavelet decomposition and the morther wavelet being pre-selected carry out signal decomposition, obtain by
The multi-layer corrugated that frequency range divides.
In embodiments of the present invention, empirical tests, the extraction effect of coifn small echo and dmey small echo is preferable, and preferably, with
When coif3 wavelet basiss are as morther wavelet, there is optimal extraction effect.Thus the embodiment of the present invention adopts coif3 wavelet basiss to make
Carry out wavelet decomposition 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 etc., the present invention is not specifically limited.
In embodiments of the present invention, when decomposing, can get n-layer waveform, now, the corresponding waveform of extractable n2～n1 layer
To be reconstructed.
The described multi-layer corrugated that s34, the corresponding coefficient of the number of plies according to needed for described wavelet reconstruction and decomposition obtain enters
Row signal reconstruction, obtains the second breath signal.
The number of plies needed for step s33, wavelet reconstruction be the 9th, 8,7 layers, now, you can according to described small echo weight
The corresponding coefficient of the number of plies needed for structure and decompose the described multi-layer corrugated that obtains and carry out signal reconstruction, obtains the second breath signal.
Referring to Fig. 5, it is the oscillogram of the second breath signal being obtained according to the wavelet transformation extraction of one embodiment of the invention.
In embodiments of the present invention, after obtaining described second breath signal, you can calculate the second breathing rate r2, specifically
For:
Find crest (or the ripple of the second breath signal by seeking extremum method in the oscillogram of described second breath signal
Paddy), referring to the point labelling in Fig. 5.
By extracting the time interval between two crests being newly generated, to obtain cycle t2.
Real-time second breathing rate r2 be can get according to sampling rate conversion.
S4, based on the Kalman's residual error generating in Kalman filtering process, is calculated and described first breath signal
Corresponding first weight factor；
In embodiments of the present invention, step s4 particularly as follows:
By rightIt is normalized described first weight factor of acquisition, wherein,
For described Kalman's residual error, kkGain coefficient for described Kalman's residual error.
S5, based on signal quality index, is analyzed to described second breath signal, obtains and described second breath signal
Corresponding second weight factor；
In embodiments of the present invention, specifically, institute can be analyzed by described second breath signal is carried out with power spectrumanalysises
State the Spectral structure of the second breath signal, obtain second weight factor corresponding with described second breath signal.
Certainly, also can peak value spectrum or other spectrums by analyzing described second breath signal distribution be calculated described
Second weight factor, the present invention is not specifically limited.
S6, according to described first breathing rate, the first weight factor, the second breathing rate and the second weight factor, is calculated
The breathing rate of current time.
In embodiments of the present invention, can be by being weighted averagely calculating to the first breathing rate r1 and the second breathing rate r2
Obtain the breathing rate r of 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 averagely, needing first μ 1 and μ 2 is normalized, specifically, false
If μ is 1+ μ 2=a, then needs respectively μ 1 and μ 2 is multiplied by with normalization coefficient 1/a and be normalized the μ 1+ μ 2 it is ensured that after normalization
=1.
The breathing rate extracting method that the present invention provides, is combined with wavelet transformation technique by using Kalman Filter Technology
Mode processing cardioelectric signals obtain the first breathing rate and the second breathing rate, and according to described first breathing rate corresponding first
Weight factor and second weight factor corresponding with described second breathing rate obtain the breathing rate of current time, compared to existing by
Monotechnics obtains the scheme of breath signal from electrocardiosignal, and result of calculation more accurately and reliably, and can mitigate due to extraneous or ring
The interference in border and the measurement fluctuation or the error that cause are such that it is able to obtain the measurement result of more accurate stable.
It should be noted that in order to prevent the first breath signal or the second breath signal second-rate and lead to calculating knot
Really inaccurate, in a preferred embodiment of the invention, described step s6 can be also:
S61, when judge described first weight factor be more than default reference value and described second weight factor be less than described
During reference value, described first breathing rate is set to the breathing rate of current time.
When described second weight factor be less than described reference value when it is believed that the signal quality of the second breath signal relatively
Described first breathing rate r1 now, is directly set to the breathing rate r of current time by difference.
S62, when judge described first weight factor be less than default reference value and described second weight factor be more than described
During reference value, described second breathing rate is set to the breathing rate of current time.
When described first weight factor be less than described reference value when it is believed that the signal quality of the first breath signal relatively
Described first breathing rate r1 now, is directly set to the breathing rate r of current time by difference.
S63, when judging described first weight factor and described second weight factor is all higher than default reference value, according to
Described first weight factor and described second weight factor are weighted to described first breathing rate and the second breathing rate suing for peace, meter
Calculate the breathing rate obtaining current time.
That is: r=μ 1*r1+ μ 2*r2.
In this preferred embodiment, if weight factor is less, illustrates that corresponding breath signal is second-rate, then directly go
Fall breathing rate corresponding with second-rate breath signal it is ensured that result of calculation accurately and stably.
Refering to Fig. 6, the embodiment of the present invention also provides a kind of breathing rate extraction element 100, comprising:
Described original electro-cardiologic signals for obtaining original electro-cardiologic signals, and are carried out notch filter by notch filter unit 10
After obtain pending electrocardiosignal.
Filter unit 20, carries out Kalman filtering to described pending electrocardiosignal and obtains the first breath signal, and according to
Described first breath signal is calculated the first breathing rate of current time.
Wavelet transform unit 30, carries out wavelet transformation to described pending electrocardiosignal and obtains the second breath signal, and root
It is calculated the second breathing rate of current time according to described second breath signal.
First weight factor and calculation unit 40, for based on the Kalman's residual computations generating in Kalman filtering process
Obtain first weight factor corresponding with described first breath signal；
Second weight factor and calculate unit 50, for carrying out to described second breath signal point based on signal quality index
Analysis, is calculated second weight factor corresponding with described second breath signal；
Breathing rate computing unit 60, for according to described first breathing rate, the first weight factor, the second breathing rate and second
Weight factor, is calculated the breathing rate of current time.
Wherein it is preferred to, described breathing rate extraction element 100 also includes:
Down-sampled unit, down-sampled for carrying out to described pending electrocardiosignal.
Wherein, as shown in Figure 7 it is preferable that described filter unit 20 specifically includes:
Filtering submodule 21, for described pending electrocardiosignal is carried out with Kalman filtering, extracts and obtains the first breathing
Signal；
The very first time is spaced extraction module 22, for extracting two crests being newly generated from described first breath signal
Between time interval；
First breathing rate computing module 23, obtains current time for carrying out sampling rate conversion according to described time interval
First breathing rate.
Wherein, as shown in Figure 8 it is preferable that described wavelet transform unit 30 specifically includes:
Frequency range hierarchical block 31, for adopting according to Shannon-nyquist sampling principle and described pending electrocardiosignal
Sample frequency carries out frequency range layering, is calculated every layer of frequency range；
Number of plies determining module 32, determines little wavelength-division for being layered every layer of frequency range and band connection frequency according to described frequency range
The number of plies needed for solution and reconstruct；
Signal decomposition module 33, for according to the number of plies needed for described wavelet decomposition and the morther wavelet that is pre-selected is carried out
Signal decomposition, obtains the multi-layer corrugated dividing by frequency range；
Signal reconstruction module 34, for obtain according to coefficient corresponding with the number of plies needed for described wavelet reconstruction and decomposition
Described multi-layer corrugated carries out signal reconstruction, obtains the second breath signal；
Second time interval extraction module 35, for extracting two crests being newly generated from described second breath signal
Between time interval；
Second breathing rate computing module 36, obtains current time for carrying out sampling rate conversion according to described time interval
Second breathing rate.
Wherein, as shown in Figure 9 it is preferable that described breathing rate computing unit 60 specifically includes:
For working as, first judge module 61, judges that described first weight factor is more than default reference value and described second power
When repeated factor is less than described reference value, described first breathing rate is set to the breathing rate of current time；
For working as, second judge module 62, judges that described first weight factor is less than default reference value and described second power
When repeated factor is more than described reference value, described second breathing rate is set to the breathing rate of current time；
For working as, 3rd judge module 63, judges that described first weight factor and described second weight factor are all higher than presetting
Reference value when, according to described first weight factor and described second weight factor to described first breathing rate and the second breathing rate
It is weighted suing for peace, be calculated the breathing rate of current time.
The breathing rate extraction element 100 that the present invention provides, by using Kalman Filter Technology and wavelet transformation technique phase
In conjunction with mode processing cardioelectric signals obtain the first breathing rate and the second breathing rate, and current time is obtained by weighted sum
Breathing rate, compared to the existing scheme being obtained breath signal by monotechnics from electrocardiosignal, result of calculation more accurately and reliably, and
Measurement fluctuation or the error causing due to the interference of the external world or environment can be mitigated such that it is able to obtain the survey of more accurate stable
Amount result.
Above disclosed be only a kind of preferred embodiment of the present invention, certainly the power of the present invention can not be limited with this
Sharp scope, one of ordinary skill in the art will appreciate that realize all or part of flow process of above-described embodiment, and according to present invention power
Profit requires made equivalent variations, still falls within the scope that invention is covered.
One of ordinary skill in the art will appreciate that realizing all or part of flow process in above-described embodiment method, it is permissible
Instruct related hardware to complete by computer program, described program can be stored in a computer read/write memory medium
In, this program is upon execution, it may include as the flow process of the embodiment of above-mentioned each method.Wherein, described 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 breathing rate extracting method is it is characterised in that include:
Obtain original electro-cardiologic signals, and described original electro-cardiologic signals are carried out obtain after notch filter with pending electrocardiosignal；
Described pending electrocardiosignal is carried out with Kalman filtering, extracts and obtain the first breath signal, and exhale according to described first
Inhale the first breathing rate that signal of change obtains current time；
Wavelet transformation is carried out to described pending electrocardiosignal and obtains the second breath signal, and according to described second breath signal meter
Calculate the second breathing rate obtaining current time；
Based on the Kalman's residual error generating in Kalman filtering process, it is calculated corresponding with described first breath signal the
One weight factor；
Based on signal quality index, described second breath signal is analyzed, obtains corresponding with described second breath signal
Second weight factor；
According to described first breathing rate, the first weight factor, the second breathing rate and the second weight factor, it is calculated current time
Breathing rate.
2. breathing rate extracting method according to claim 1 is it is characterised in that carrying out to described pending electrocardiosignal
Before wavelet transformation obtains the second breath signal, also include:
Described pending electrocardiosignal is carried out down-sampled.
3. breathing rate extracting method according to claim 1 is it is characterised in that described enter to described pending electrocardiosignal
Row Kalman filtering, extracts and obtains the first breath signal, and is calculated the of current time according to described first breath signal
One breathing rate particularly as follows:
Described pending electrocardiogram (ECG) data is carried out with Kalman filtering, extracts and obtain the first breath signal；
The time interval between two crests being newly generated is extracted from described first breath signal；
The first breathing rate that sampling rate conversion obtains current time is carried out according to described time interval.
4. breathing rate extracting method according to claim 1 is it is characterised in that described first weight factor is by rightIt is normalized acquisition, wherein,For Kalman's residual error, kkFor described Kalman's residual error
Gain coefficient.
5. breathing rate extracting method according to claim 1 is it is characterised in that described enter to described pending electrocardiosignal
Row wavelet transformation obtains the second breath signal, and is calculated the second breathing rate of current time according to described second breath signal
Particularly as follows:
Frequency range layering is carried out according to the sample frequency of Shannon-nyquist sampling principle and described pending electrocardiosignal, calculates
Obtain every layer of frequency range；
Frequency range and preset band connection frequency according to every layer in the layering of described frequency range determine needed for wavelet decomposition and reconstruct
The number of plies；
The number of plies according to needed for described wavelet decomposition and the morther wavelet being pre-selected carry out signal decomposition, obtain dividing by frequency range
Multi-layer corrugated；
The corresponding coefficient of the number of plies according to needed for described wavelet reconstruction and decompose the described multi-layer corrugated that obtains and carry out signal weight
Structure, obtains the second breath signal；
The time interval between two crests being newly generated is extracted from described second breath signal；
The second breathing rate that sampling rate conversion obtains current time is carried out according to described time interval.
6. breathing rate extracting method according to claim 5 is it is characterised in that the upper cut-off frequency of described band connection frequency
For 0.8hz, the lower-cut-off frequency of described band connection frequency is 0.1hz.
7. breathing rate extracting method according to claim 1 it is characterised in that described according to described first breathing rate,
One weight factor, the second breathing rate and the second weight factor, be calculated the breathing rate of current time particularly as follows:
When judging that described first weight factor is more than default reference value and described second weight factor and is less than described reference value,
Described first breathing rate is set to the breathing rate of current time；
When judging that described first weight factor is less than default reference value and described second weight factor and is more than described reference value,
Described second breathing rate is set to the breathing rate of current time；
When judging described first weight factor and described second weight factor is all higher than default reference value, according to described first
Weight factor and described second weight factor are weighted to described first breathing rate and the second breathing rate suing for peace, and are calculated and work as
The breathing rate in front moment.
8. a kind of breathing rate extraction element is it is characterised in that include:
Described original electro-cardiologic signals for obtaining original electro-cardiologic signals, and are carried out obtaining after notch filter by notch filter unit
Pending electrocardiosignal；
Filter unit, carries out Kalman filtering to described pending electrocardiosignal and obtains the first breath signal, and according to described
One breath signal is calculated the first breathing rate of current time；
Wavelet transform unit, carries out wavelet transformation to described pending electrocardiosignal and obtains the second breath signal, and according to described
Second breath signal is calculated the second breathing rate of current time；
First weight factor and calculate unit, for based in Kalman filtering process generate Kalman's residual computations obtain with
Corresponding first weight factor of described first breath signal；
Second weight factor and calculation unit, for being analyzed to described second breath signal based on signal quality index, are calculated
Obtain second weight factor corresponding with described second breath signal；
Breathing rate computing unit, for according to described first breathing rate, the first weight factor, the second breathing rate and the second weight because
Son, is calculated the breathing rate of current time.
9. breathing rate extraction element according to claim 8 is it is characterised in that described filter unit specifically includes:
Filtering submodule, for being filtered to described pending electrocardiosignal using Kalman filter, is extracted and obtains first
Breath signal；
The very first time is spaced extraction module, for extracting from described first breath signal between two crests being newly generated
Time interval；
First breathing rate computing module, exhales for carrying out sampling rate conversion according to described time interval and obtaining the first of current time
Suction rate.
10. breathing rate extraction element according to claim 8 is it is characterised in that described wavelet transform unit specifically includes:
Frequency range is divided into module, for the sample frequency according to Shannon-nyquist sampling principle and described pending electrocardiosignal
Carry out frequency range layering, be calculated every layer of frequency range；
Number of plies determining module, determines wavelet decomposition and weight for being layered every layer of frequency range and band connection frequency according to described frequency range
The number of plies needed for structure；
Signal decomposition module, for according to the number of plies needed for described wavelet decomposition and the morther wavelet that is pre-selected carries out signal and divides
Solution, obtains the multi-layer corrugated dividing by frequency range；
Signal reconstruction module is described many for obtained according to coefficient corresponding with the number of plies needed for described wavelet reconstruction and decomposition
Layer waveform carries out signal reconstruction, obtains the second breath signal；
Second time interval extraction module, for extracting between two crests being newly generated from described second breath signal
Time interval；
Second breathing rate computing module, exhales for carrying out sampling rate conversion according to described time interval and obtaining the second of current time
Suction rate.
11. according to the breathing rate extraction element described in claim 8 it is characterised in that described breathing rate computing unit specifically includes:
For working as, first judge module, judges that described first weight factor is more than default reference value and described second weight factor
During less than described reference value, described first breathing rate is set to the breathing rate of current time；
For working as, second judge module, judges that described first weight factor is less than default reference value and described second weight factor
During more than described reference value, described second breathing rate is set to the breathing rate of current time；
For working as, 3rd judge module, judges that described first weight factor and described second weight factor are all higher than default benchmark
During value, described first breathing rate and the second breathing rate are carried out add according to described first weight factor and described second weight factor
Power summation, is calculated the breathing rate of current time.
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