CN105982664B - Cardiopulmonary coupling analysis method based on single-lead ECG - Google Patents
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Abstract
A method for single lead ECG based cardiopulmonary coupling analysis, comprising: extracting an RR interval sequence and an EDR signal from a body surface single-lead ECG signal; then using the adaptive filter and the RR interval sequence as a reference input signal of the adaptive filter, using the EDR signal as an ideal signal of the adaptive filter, and eliminating an EDR signal noise component by utilizing the correlation between the RR interval sequence and the EDR signal to obtain an enhanced EDR signal; performing empirical mode decomposition on the RR interval sequence and the enhanced EDR signal to obtain a plurality of intrinsic mode functions of the RR interval sequence and a plurality of intrinsic mode functions of the enhanced EDR signal; and respectively selecting the intrinsic mode function of the RR interval sequence corresponding to the preset frequency segment and the intrinsic mode function of the enhanced EDR signal, and performing cardiopulmonary coupling analysis by using the intrinsic mode function of the RR interval sequence corresponding to the preset frequency segment and the intrinsic mode function of the enhanced EDR signal to obtain a coupling result.
Description
Technical field
The present invention relates to a kind of physiological parameter analysis method more particularly to a kind of cardiopulmonary couplings point based on single lead ECG
Analysis method.
Background technology
Cardiopulmonary coupling (cardiopulmonary coupling) is also commonly referred to as Cardiopulmonary interaction
(cardiorespiratory interaction), refer to the coordination system inherent between cardiovascular system and respiratory system and
Effect, research are related to extensive physiology, pathology and clinical application field.Cardiopulmonary coupling phenomenon can be used for obtaining strong with body
The relevant physiology of health state and pathological information, such as evaluate autonomic nervous system function by cardiopulmonary coupling analysis, and quantitative assessment is slept
Dormancy quality, detection sleep-respiratory event etc..This physiological phenomenon is coupled by cardiopulmonary, and can adjust and intervene body health shape
State, for example by the respiratory movement of AD HOC, beneficial cardiopulmonary coupling effect is generated, it is autonomous that cardiovascular system is adjusted
Function adjusts physical and mental statuse.Therefore cardiopulmonary coupling analysis technology both can be used for health state identification, can be used for people
The intervention of body health status and adjusting.
Multi-signal analytical technology has been developed at present, cardiopulmonary coupling analysis is used for, such as causality analysis, symbol power
, Phase synchronization, mutual information etc..In terms of signal processing angle, cardiopulmonary coupling analysis is a kind of bivariate analysis technology, is usually used
To heart rate time sequence and breathing time sequence two paths of signals, with the correlation between description system.Compared to single argument point
Analysis technology, such as heart rate variability analysis technology, cardiopulmonary coupling analysis tends to overcome certain deficiencies of univariate analysis technology, carries
For more about disease and the relevant information of health status.
In practical applications, since cardiopulmonary coupling analysis needs while measuring electrocardio and breathing two paths of signals, electrocardiosignal
It measures to use respectively with breath signal and not enough facilitate.Later it is proposed that extracting the side of breath signal from body surface ecg
Method is referred to as ECG-Derived Respiration：EDR, as shown in Figure 1, in Fig. 1 the envelope variation of ECG be breathing at
Point.Researcher further uses phase signal between EDR signals and cardiac electrical RR and does coupling analysis.In the prior art, it directly uses
EDR does cardiopulmonary coupling analysis with cardiac RR intervals, and there are cardiopulmonary to couple the inaccurate technical barrier of estimation, influences cardiopulmonary coupling
Analytical effect.
Invention content
The present invention is directed to propose a kind of new cardiopulmonary coupling analytical method, can realize reliable cardiopulmonary coupling.
In view of above-mentioned purpose, the present invention proposes a kind of cardiopulmonary coupling analytical method based on single lead ECG comprising：From
RR interval series and EDR signals are extracted in body surface list lead ECG signal；Then sef-adapting filter is used, with RR interval series
As the reference-input signal of sef-adapting filter, using EDR signals as the ideal signal of sef-adapting filter, the phase between RR is utilized
Correlation between sequence and EDR signals eliminates EDR signal noise ingredients, improves EDR Signal-to-Noises, obtains enhanced EDR
Signal；Multiple intrinsic mode letters of RR interval series are obtained as empirical mode decomposition to RR interval series and enhanced EDR signals
Multiple intrinsic mode functions of number and enhanced EDR signals；Multiple intrinsic mode functions from the RR interval series and increasing
Multiple intrinsic mode functions of strong type EDR signals choose the intrinsic mode letter of RR interval series corresponding with preset frequency section respectively
The intrinsic mode function of number and enhanced EDR signals, with the intrinsic mode of the RR interval series corresponding with preset frequency section
The intrinsic mode function of function and enhanced EDR signals carries out cardiopulmonary coupling analysis, obtains coupling result.
Preferably, the cardiopulmonary coupling analysis is realized by Phase synchronization indices P；Wherein,
For the phase of the intrinsic mode function of RR interval series corresponding with preset frequency section and the intrinsic mode letter of enhanced EDR signals
Phase difference between several phases,WithFor the mean value of predetermined time interval.
Preferably, the phase of the intrinsic mode function of the RR interval series corresponding with preset frequency section and enhanced
Intrinsic mode function and enhancing of the phase of the intrinsic mode function of EDR signals by RR interval series corresponding with preset frequency section
The intrinsic mode function of type EDR signals obtains after Hilbert is converted.
Preferably, when the Phase synchronization index is less than predetermined threshold, it is believed that there is no apparent coupling between cardiopulmonary, be
Noise contribution；When the Phase synchronization index is more than the predetermined threshold, it is believed that cardiopulmonary coupling is existing, reliable.It is excellent
Selection of land, the preset frequency section are 0.15~0.4Hz.
Preferably, it is to pass through that RR interval series and enhanced EDR signals are extracted in the list lead ECG signal from body surface
The signal of the selected predetermined time length of predetermined time window.Preferably, the length of the time window is 2 minutes.It is further excellent
Selection of land, in a sliding manner, pre- timing is extracted in segmentation to the predetermined time window from RR interval series and enhanced EDR signals
Between length time series, complete signal between coupling analysis.
The cardiopulmonary coupling analytical method based on single lead ECG of the present invention eliminates EDR's by sef-adapting filter first
Noise contribution improves signal-to-noise ratio, improves EDR signal qualities, obtains enhanced EDR signals EDR'；Then use experience pattern point
Solution (EMD/EEMD) technology RR interval series signal and enhanced EDR signals (EDR') are decomposed, extraction it is interested (that is,
It is corresponding with preset frequency section) signal component, for cardiopulmonary coupling analysis, thus to obtain accurately and reliably cardiopulmonary coupling knot
Fruit.
Description of the drawings
Fig. 1 is EDR signals；
Fig. 2 is the schematic diagram that enhanced EDR signals are obtained by sef-adapting filter；
Fig. 3 is that the EEMD of RR interval series signals decomposes example；
Fig. 4 is intrinsic mode function and the breathing that RR interval series corresponding with preset frequency section are extracted using EEMD methods
The intrinsic mode function of signal, an example for calculating cardiopulmonary coupling.Wherein (a) be original RR interval series signal,
The breath signal really measured (passes through respiratory inductive plethysmography：RIP)；(b) corresponding with preset frequency section to be extracted
The intrinsic mode function of RR interval series and the intrinsic mode function of breath signal represented by signal.
Fig. 5 is all kinds of cardiopulmonary physiological signals involved in embodiment, wherein being followed successively by between RIP, RR from top to bottom in (a)
Phase, EDR_area, EDR_RS, EDR_area_filt, EDR_RS_filt, (b) each signal is signal in (a) from top to bottom in figure
The intrinsic mode function of special frequency band ingredient is extracted by EEMD.EDR_area is obtained by the variation of the area of QRS
EDR signals；EDR_RS is the EDR signals obtained by the variation of the amplitude difference of the R-S waves of QRS；EDR_area_filt is
The enhanced EDR signals that EDR_area is obtained by adaptive-filtering；EDR_RS_filt is that EDR_RS is obtained by adaptive-filtering
The enhanced EDR signals arrived.
The Bland-Altman for the cardiopulmonary coupling that Fig. 6 is estimated by different EDR algorithms schemes, wherein (a) be EDR_area with
RIP results contrasts；(b) it is EDR_RS and RIP results contrasts；(c) EDR_area_filt and RIP results contrasts；(d)EDR_RS_
Filt and RIP results contrasts；
Fig. 7 is the flow chart of the cardiopulmonary coupling analytical method based on single lead ECG of the present invention.
Specific implementation mode
In the following, in conjunction with attached drawing to the cardiopulmonary coupling analytical method based on single lead ECG of the present invention.
As shown in fig. 7, the cardiopulmonary coupling process committed step of the present invention is 1) to obtain enhanced EDR signals；2) pass through
EMD/EEMD technologies carry out empirical mode decomposition to RR interval series and enhanced EDR signals, then by the decomposition result of the two
Interested eigenfunction coupled to obtain the result of cardiopulmonary coupling.Here " interested " refers to " particular frequency range "
The meaning.For example, when making breast rail, the interested 0.15~0.4Hz ranging from breathed residing for frequency band.
Specifically, heartbeat interval sequence (RR interval series) and EDR signals are extracted from body surface ECG signal, then with RR
Interval series are the reference-input signal of sef-adapting filter, and ideal signal of the EDR signals as sef-adapting filter utilizes RR
Correlation between interval series and EDR signals eliminates noise contribution, improves signal-to-noise ratio, improves EDR signal qualities, is increased
Strong type EDR signal EDR', sef-adapting filter structural schematic diagram are as shown in Figure 2.
Then, use experience Mode Decomposition (EMD/EEMD) technology is to RR interval series and enhanced EDR signals (EDR')
It decomposes, the signal component of (i.e. preset frequency) interested is extracted, for cardiopulmonary coupling analysis.
Cardiopulmonary coupling phenomenon itself shows the features such as non-linear, unstable state and multiple dimensioned, discontinuity, conventional letter
Number treatment technology can not effectively analyze such signal.Previous signal analysis technology mostly uses the bandpass filter of fixed frequency band,
After being pre-processed respectively to RR interval series and breath signal, then do coupling analysis.By research and it is demonstrated experimentally that bandpass filter
The accuracy estimated for cardiopulmonary coupling analysis of bandwidth have a significant impact, be difficult to determine bandpass filter in practical application
Parameter, therefore influence the accuracy of cardiopulmonary coupling analysis, and interested ingredient can not be coupled on different scale
Analysis.
From interim extraction between RR breathe relevant oscillationg component using EEMD methods as shown in figure 3, in Fig. 3 the first behavior it is former
The RR interval series of beginning, show apparent Unsteady characteristics, different intrinsic mode function IMF after EEMD is decomposed (4~
6) in, IMF5 is concentrated on relevant oscillationg component is breathed, therefore the ingredient for extracting IMF5 is used for cardiopulmonary coupling analysis.
Fig. 4 illustratively shows the two paths of signals for carrying out cardiopulmonary coupling, wherein (a) upper and lower figure be followed successively by it is original
RR interval series signal and the breath signal that really measures (pass through respiratory inductive plethysmography：RIP)；(b) upper and lower figure
Be followed successively by EEMD methods extract corresponding with preset frequency section RR interval series intrinsic mode function and breath signal it is intrinsic
Mode function.(b) two paths of signals in is for calculating cardiopulmonary coupling.Compared to the method for bandpass filter, EEMD can be adaptive
The completion signal decomposition answered, and therefrom the ingredient of extraction scheduled frequency range is calculated for cardiopulmonary coupling.And bandpass filter
Then there is filter parameter On The Choice in method, false cardiopulmonary coupling (over-evaluating), passband mistake are easy to cause if passband is narrow
It is wide then cause cardiopulmonary stiffness of coupling too low (underestimating) comprising other non-coupled ingredients.It is difficult setting bandpass filtering in practical application
The ideal parameters of device, to obtain accurate cardiopulmonary coupling estimation.
In this embodiment, RIP represents respiratory inductive plethysmography, is the true breath signal for measuring and obtaining, because
The cardiopulmonary Coupled Numerical obtained using RIP signals in this this embodiment is goldstandard, to examine all kinds of EDR algorithms and its enhanced
EDR algorithms estimate the effect of cardiopulmonary coupling.
To examine method proposed by the present invention to the consistency of all kinds of EDR algorithms, we have selected two classes widely used
EDR algorithms：Algorithm (being denoted as EDR_area) based on ECG signal QRS wave area and based on R-S wave amplitudes in ECG signal QRS wave
The algorithm (being denoted as EDR_RS) of difference, is tested.Two class EDR algorithms generate the enhanced EDR of two classes respectively by adaptive-filtering
It (calculates separately and is denoted as EDR_area_filt and EDR_RS_filt, respectively the corresponding algorithm based on area and the calculation based on amplitude
Method).The cardiopulmonary coupling goldstandard that cardiopulmonary coupling result obtained by 4 class EDR algorithms is all obtained with RIP is compared.
Fig. 5 (a) shows two class basic model EDR algorithms (EDR_area and EDR_RS) and enhanced EDR algorithms (EDR_
Area_filt and EDR_RS_filt), it is calculated there it can be seen that the signal quality of enhanced EDR is substantially better than basic model EDR
Method, EDR signal qualities are enhanced after sef-adapting filter.Fig. 5 (b) is the sheet that special frequency band ingredient is extracted by EEMD
Sign mode function, rather than bandpass filter avoid generating false estimation since Signal Pretreatment process couples cardiopulmonary.
As an example, cardiopulmonary coupling analysis can be realized by Phase synchronization index, Phase synchronization Index Definition
For：For the phase difference between RR interval series and EDR' signals,With
For the mean value being sometime spaced.According to the definition of P, for the numerical value of P between 0~1, P values are bigger, the phase between two signals
Bit synchronization is higher.Here, only exemplary illustration, cardiopulmonary of the invention coupling are not limited to phase coupling estimation.
As an example, the phase signal of RR interval series and EDR' two paths of signals after respective signal EEMD decomposition by carrying
It is obtained after the Hilbert transformation of the ingredient interested (sum of one or several IMF) taken.Here, only exemplary illustration, ability
The technical staff in domain can also obtain above-mentioned phase difference by other methods.
To interested time series, such as phase between RR and EDR', signal decomposition is multiple intrinsic mode letters by EMD/EEMD
Number.We select corresponding IMF by average frequency from interested signal.Frequency band is such as breathed to fall in [0.15~0.4] Hz
Between, we choose in RR sequences and EDR' sequences and breathe relevant oscillationg component accordingly, are used for cardiopulmonary coupling analysis.
Example：
Analyzing guiding breathing, (respiratory rate is according to 14 beats/min -12.5 beats/min -11 beats/min -9.5 beats/min -8 beats/min -7
Beat/min pattern, change from high in the end, each stage continues 3 minutes) under cardiopulmonary coupling estimation and the websites Physionet
The cardiopulmonary in old age and young people in the database Fantasia used of increasing income couple.It is as a result as shown in Table 1 and Table 2 respectively,
Middle RIP indicates that the true breath signal obtained by respiratory movement transducer measurement, EDR_area expressions are acquired with area algorithm
EDR signals, the EDR_RS expressions EDR signals that R-S wave amplitudes difference algorithm acquires in QRS wave, EDR_area_filt indicate
EDR_area passes through the enhancing signal that RLS adaptive algorithms obtain, and EDR_RS_filt indicates that EDR_RS is adaptively calculated by RLS
The enhancing signal that method obtains.The estimation of cardiopulmonary stiffness of coupling uses Phase synchronization index to estimate, the numerical value be located at [0~1] it
Between, numerical value shows that more greatly the stiffness of coupling between cardiorespiratory system is bigger.The calculating process of Phase synchronization index uses us and carries
The strategy based on EMD signal decompositions gone out extracts respiration vibrating ingredient from cardiopulmonary physiological signal, is got in return using Hilbert changes
To respective accurate phase information, Phase synchronization index is calculated.
From table 1, it is apparent that original EDR algorithms either area algorithm or RS amplitude difference algorithms, all significantly
Underestimate cardiopulmonary stiffness of coupling (no matter EDR_area or EDR_RS:Paired-samples T-test, p<0.001, rank sum test, p<
0.001), and the coupling of cardiopulmonary that two enhanced EDR signals are estimated couple nothing with the cardiopulmonary of true breathing RIP estimation gained and shows
Sex differernce is write (for EDR_area_filt:Paired-samples T-test p=0.302, rank sum test, p=0.352；For EDR_RS_
filt:Paired-samples T-test, p=0.757, rank sum test, p=0.979).I.e. auto-adaptive filtering technique can effectively improve EDR letters
Number estimation cardiopulmonary coupling accuracy, to realize the reliable cardiopulmonary coupling analysis based on single lead ECG.
From the result of table 1, we can draw the following conclusions：The algorithm is consistent for the effect of two class basic model EDR algorithms,
The accuracy of cardiopulmonary coupling estimation, and cardiopulmonary coupling estimation obtained by two kinds of enhanced EDR algorithms can be significantly improved
Between significant difference (pairing T- examine, p=0.346, rank sum test, p=0.378) is also not present.
The cardiopulmonary coupling data in old age and young people in Fantasia is more as shown in table 2, and the heart under guiding breathing
Lung coupling obtains same conclusion.
Table 1:The cardiopulmonary of respiratory state difference respiratory rate level are guided to couple estimation condition
Table 2:The cardiopulmonary of normal adults and the elderly coupling estimation in Fantasia data sets
| Different crowd | RIP | EDR_area | EDR_RS | EDR_area_filt | EDR_RS_filt |
| Young man (n=20) | 0.80±0.10 | 0.58±0.20 | 0.56±0.14 | 0.80±0.11 | 0.77±0.12 |
| The elderly (n=20) | 0.69±0.19 | 0.48±0.20 | 0.50±0.18 | 0.71±0.13 | 0.70±0.12 |
| Overall (n=40) | 0.75±0.15 | 0.53±0.20 | 0.53±0.16 | 0.76±0.13 | 0.74±0.12 |
We see Fig. 6 institutes using the performance of the different EDR algorithms estimation cardiopulmonary couplings of Bland-Altman figure detailed analysis
Show.Can clearly it be found out by Bland-Altman figures, two class basic model EDR signals generate bright when doing cardiopulmonary coupling analysis
Aobvious wild point, and there are the ratio sexual deviations of conspicuousness.(breathing is generally also corresponded to when cardiopulmonary coupling intensity itself is larger
The relatively low situation of rate), the algorithm EDR_area of basic model EDR signals (EDR_area and EDR_RS_filt) either area is also
It is amplitude algorithm EDR_RS, the cardiopulmonary coupling of estimation and goldstandard are all smaller compared to deviation, and when cardiopulmonary stiffness of coupling is little
When (correspond to autonomous respiration or the higher situation of respiratory rate) more, the coupling of the cardiopulmonary of estimation and goldstandard are more larger than deviation.It is this
Proportional jitter is different from systematic error, can not be eliminated by the method for simple modifications systematic error.And pass through adaptive-filtering
Treated enhanced EDR (EDR_area_filt and EDR_RS_filt) signal, open country point significantly reduce, and system deviation obviously subtracts
Few, more superior is that proportional jitter disappears substantially.It can be obtained in Bland-Altman figures and paired-samples T-test and rank sum test one
The conclusion of cause.
Pass through the Bland-Altman map analysis of Fig. 6, it can be seen that cardiopulmonary coupling analytical method of the invention not only may be used
To effectively improve the accuracy of the cardiopulmonary coupling estimation based on ECG, moreover it is possible to proportional jitter is effectively eliminated, to ensure to exhale various
In the case of suction rate, accurate cardiopulmonary coupling estimation can be obtained, can realize the steady cardiopulmonary coupling under different respiratory states
Close analysis.
Claims (8)
1. a kind of cardiopulmonary coupling analytical method based on single lead ECG comprising：
RR interval series and EDR signals are extracted from body surface list lead ECG signal；Then sef-adapting filter is used, between RR
Reference-input signal of the phase sequence as sef-adapting filter is utilized using EDR signals as the ideal signal of sef-adapting filter
Correlation between RR interval series and EDR signals eliminates EDR signal noise ingredients, improves EDR Signal-to-Noises, is increased
Strong type EDR signals；
Multiple intrinsic mode functions of RR interval series are obtained as empirical mode decomposition to RR interval series and enhanced EDR signals
With multiple intrinsic mode functions of enhanced EDR signals；Multiple intrinsic mode functions from the RR interval series and enhancing
Multiple intrinsic mode functions of type EDR signals choose the intrinsic mode function of RR interval series corresponding with preset frequency section respectively
With the intrinsic mode function of enhanced EDR signals, with the intrinsic mode letter of the RR interval series corresponding with preset frequency section
The intrinsic mode function of number and enhanced EDR signals carries out cardiopulmonary coupling analysis, obtains coupling result.
2. the cardiopulmonary coupling analytical method as described in claim 1 based on single lead ECG, it is characterised in that：
The cardiopulmonary coupling analysis is realized by Phase synchronization indices P；
Wherein, For the intrinsic mode function of RR interval series corresponding with preset frequency section
Phase and enhanced EDR signals intrinsic mode function phase between phase difference,WithFor pre- timing
Between the mean value that is spaced.
3. the cardiopulmonary coupling analytical method as claimed in claim 2 based on single lead ECG, it is characterised in that：
The sheet of the phase and enhanced EDR signals of the intrinsic mode function of the RR interval series corresponding with preset frequency section
Levy intrinsic mode function and enhanced EDR signal of the phase of mode function by RR interval series corresponding with preset frequency section
Intrinsic mode function obtains after Hilbert is converted.
4. the cardiopulmonary coupling analytical method as claimed in claim 2 based on single lead ECG, it is characterised in that：
When the Phase synchronization indices P is less than predetermined threshold, it is believed that there is no apparent couplings between cardiopulmonary, are noise contribution；
When the Phase synchronization indices P is more than the predetermined threshold, it is believed that cardiopulmonary coupling is existing, reliable.
5. the cardiopulmonary coupling analytical method as described in claim 1 based on single lead ECG, it is characterised in that：
The preset frequency section is 0.15~0.4Hz.
6. the cardiopulmonary coupling analytical method as claimed in claim 4 based on single lead ECG, it is characterised in that：
It is by pre- timing that RR interval series are extracted in the enhanced EDR signals and the list lead ECG signal from body surface
Between the selected predetermined time length of window signal.
7. the cardiopulmonary coupling analytical method as claimed in claim 6 based on single lead ECG, it is characterised in that：
The length of the predetermined time window is 2 minutes.
8. the cardiopulmonary coupling analytical method as claimed in claim 6 based on single lead ECG, it is characterised in that：
In a sliding manner, segmentation is from enhanced EDR signals and described from body surface list lead ECG for the predetermined time window
The time series for extracting predetermined time length in RR interval series is extracted in signal, completes the coupling analysis between signal.
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| CN103892812A (en) * | 2014-02-24 | 2014-07-02 | 南京丰生永康软件科技有限责任公司 | Matching degree analysis technology |
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