CN102217931A - Method and device for acquiring heart rate variation characteristic parameter - Google Patents
Method and device for acquiring heart rate variation characteristic parameter Download PDFInfo
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Abstract
The embodiment of the invention discloses a method and device for acquiring a heart rate variation characteristic parameter. The method comprises the following steps of: calculating and obtaining a heart rate variation signal in a time domain according to a human physiological signal; carrying out time-frequency conversion on the heart rate variation signal in the time domain, and selecting a power spectrum subsequence reflecting heart rate variation from a converted power spectrum sequence according to a frequency characteristic of the heart rate variation; accumulating power values at frequency points upwards from a lowest frequency point of the power spectrum subsequence, and accumulating power values at frequency points downwards from a highest frequency point to obtain a first frequency point when the ratio of the accumulated power value to the total power value is more than a low frequency search ratio and a second frequency point when the ratio of the accumulated power value to the total power value is more than a high frequency search ratio; and obtaining the difference between the second frequency point and the first frequency point to obtain the frequency band width of the power spectrum subsequence, and the frequency band width is the characteristic parameter. According to the embodiment of the invention, the characteristic parameter can reflect periodic variation of the heart rate variation and also can reflect a position relationship between a main peak value and a secondary peak value.
Description
Technical field
The present invention relates to the signal processing technology field, particularly a kind of acquisition methods of heart rate variability characteristic parameter and device.
Background technology
(Heart Rate Variability, HRV) signal is meant the variation of the heart rate of moment between successive heartbeat to heart rate variability.At present, when heart rate variability is analyzed, mainly use temporal analysis and frequency domain analysis to obtain the characteristic parameter of reflection heart rate variability form, two kinds of methods can be obtained different characteristic parameters respectively.
Interim 2004 the total the 134th of periodical " Swiss Medical Weekly ", disclose one piece of " Heart rate variability:a noninvasive electrocardio graphic method to measure the automatic nervous system " article, in the literary composition time-domain analysis method and frequency-domain analysis method have been introduced.Wherein, the time-domain analysis method is on time domain eartbeat interval to be carried out statistical analysis over time, the characteristic parameter that obtains comprises: SDNN (Standard Diviation ofNN intervals, all standard deviation of normal sinus heartbeat RR interval), SDSD (standard deviation of the difference of whole adjacent NN interval), RMSSD (root-mean-square value of the difference of whole adjacent NN interval) and pNN50 (the heart rate percentage ratio of poor>50ms of whole adjacent NN interval).Frequency-domain analysis method is to adopt means such as Fourier transformation or autoregression estimation that the heart rate variability signal is carried out time-frequency conversion, obtain corresponding characteristic parameter by spectrum analysis, these characteristic parameters comprise: TP (general power), ULF (very low frequency power component), VLF (extremely low frequency power component), LF (low frequency power component), HF (high frequency power component) and LF/HF (low frequency high frequency ratio) etc.
Although adopt temporal analysis and frequency domain analysis can obtain the characteristic parameter of various reflection heart rate variability forms, but, these characteristic parameters can't reflect the cyclically-varying of heart rate variability exactly, have been subjected to a lot of limitations in applications such as biofeedbacies.
Be CN1358074A, be in the Chinese invention patent on July 10th, 2002 in open day at publication number, disclose a kind of characteristic parameter acquisition methods based on frequency-domain analysis.This method is at first determined the distribute power spectrum of heart rate variability signal, selects the frequency of maximum energy peak value then in spectrum, and determines the ENERGY E of the frequency correspondence of this maximum energy peak value respectively
Peak, be lower than the energy summation E of all frequency correspondences of the frequency of this maximum energy peak value
BelowAnd the energy summation E of all frequency correspondences that is higher than the frequency of this maximum energy peak value
Above, at last with E
PeakWith E
BelowAnd E
PeakWith E
AboveRatio as characteristic parameter, this characteristic parameter can reflect the cyclically-varying of heart rate variability, when especially heart rate variability is sinusoidal change of state.
But the inventor finds that under study for action the method for foregoing invention patent disclosure still comes with some shortcomings.ENERGY E in the frequency of the main peak value of having determined ceiling capacity
PeakAfter, the energy of the frequency correspondence of other minor peaks except that the main peak value in the distribute power spectrum is accumulated to E uniformly
AboveAnd E
BelowIn.Therefore, this method has only been paid close attention to the frequency location of main peak value, and the frequency location of other minor peaks is not differentiated, make the characteristic parameter that calculates can't reflect the position relation between main peak value and other minor peaks, and then can not reflect the form of heart rate variability exactly.
Summary of the invention
In order to solve the problems of the technologies described above, the embodiment of the invention provides a kind of acquisition methods and device of heart rate variability characteristic parameter, so that this characteristic parameter in the cyclically-varying that can reflect heart rate variability simultaneously, also can reflect the position relation between main peak value and the minor peaks.
The embodiment of the invention discloses following technical scheme:
A kind of acquisition methods of heart rate variability characteristic parameter comprises:
Calculate the heart rate variability signal that obtains on the time domain according to physiology signal;
Heart rate variability signal on the described time domain is carried out time-frequency conversion, choose the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency domain characteristic after conversion according to heart rate variability;
From the minimum frequency of described power spectrum subsequence begin upwards the to add up performance number of each frequency, and, from high frequency points begin to add up the downwards performance number of each frequency, the ratio of performance number and total power value first frequency during greater than low frequency search ratio and second frequency during greater than high frequency search ratio obtain respectively adding up, wherein, described low frequency search ratio and high frequency search ratio all are inversely proportional to the distribution concentration degree of the power spectrum subsequence of heart rate variability analysis system requirements;
Described second frequency and first frequency are asked poor, obtain the band bandwidth of described power spectrum subsequence, described band bandwidth is the characteristic parameter of reflection heart rate variability.
A kind of deriving means of heart rate variability characteristic parameter comprises:
Signature computation unit is used for calculating heart rate variability signal on the time domain according to physiology signal;
The time-frequency conversion unit is used for the heart rate variability signal on the described time domain is carried out time-frequency conversion, chooses the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency characteristic after conversion according to heart rate variability;
The frequency computing unit, be used for from the minimum frequency of described power spectrum subsequence begin upwards the to add up performance number of each frequency, and, from high frequency points begin to add up the downwards performance number of each frequency, the ratio of performance number and total power value first frequency during greater than low frequency search ratio and second frequency during greater than high frequency search ratio obtain respectively adding up, wherein, described low frequency search ratio and high frequency search ratio all are inversely proportional to the distribution concentration degree of the power spectrum subsequence of heart rate variability analysis system requirements;
The calculation of characteristic parameters unit is used for described second frequency and first frequency are asked poor, obtains the band bandwidth of described power spectrum subsequence, and described band bandwidth is the characteristic parameter of reflection heart rate variability.
As can be seen from the above-described embodiment, compared with prior art, the present invention has following advantage:
The characteristic parameter that utilizes the scheme in the embodiment of the invention to obtain not only can be described the cyclically-varying of heart rate variability, and power spectrum is being carried out in the process of bidirectional research, each Frequency point is searched for successively according to its position relation, when the primary and secondary peak is adjoining, bandwidth (characteristic parameter) is less, otherwise characteristic parameter is then bigger.Therefore, characteristic parameter can also be differentiated the relation of the position between the primary and secondary peak value in the power spectrum subsequence, makes the approaching primary and secondary peak Distribution in position obtain better metric evaluation.This method has solved prior art to the insensitive deficiency in Power Spectrum Distribution position, has expressed the physiology and the psychological meaning of heart rate variability signal representative more accurately.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, to do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
Fig. 1 is the flow chart of an embodiment of the acquisition methods of a kind of heart rate variability characteristic parameter of the present invention;
Fig. 2 is for having determined the pulse wave signal sketch map of reference zero among the present invention;
Fig. 3 is for searching for the process sketch map of pulse starting point in pulse wave signal among the present invention;
Fig. 4 is the heart rate variability signal schematic representation on the time domain among the present invention;
Fig. 5 carries out the process sketch map that the band bandwidth at main power component place is determined in bidirectional research among the present invention to the power spectrum subsequence;
Fig. 6 is the flow chart of another embodiment of the acquisition methods of a kind of heart rate variability characteristic parameter among the present invention;
Fig. 7 is the flow chart of another embodiment of the acquisition methods of a kind of heart rate variability characteristic parameter among the present invention;
Fig. 8 is the structure chart of an embodiment of the deriving means of a kind of heart rate variability characteristic parameter among the present invention;
Fig. 9 is the structure chart of another embodiment of the deriving means of a kind of heart rate variability characteristic parameter among the present invention;
Figure 10 is the structure chart of another embodiment of the deriving means of a kind of heart rate variability characteristic parameter among the present invention;
Figure 11 is the unitary structural representation of time-frequency conversion among the present invention.
The specific embodiment
Below in conjunction with drawings and Examples, the embodiment of the invention is described in detail.Should be appreciated that specific embodiment described herein only in order to explanation the present invention, and be not used in qualification the present invention.
Embodiment one
See also Fig. 1, it is the flow chart of an embodiment of the acquisition methods of a kind of heart rate variability characteristic parameter of the present invention, and this method may further comprise the steps:
Step 101: calculate the heart rate variability signal that obtains on the time domain according to physiology signal;
For example, can adopt any one art methods to gather necessary physiology signal, calculate the heart rate variability signal that obtains on the time domain according to the physiology signal of gathering then.Wherein, the physiological parameter of human body can be the pulse wave signal of human body, also can be the electrocardiosignal of human body.
For example, to gather the pulse wave signal of human body, be example below from calculating the heart rate variability signal that obtains on the time domain according to pulse wave signal, describe the implementation of this step in detail.Utilize infrared photoelectric sensor to gather the pulse wave signal of human body earlier, as shown in Figure 2, collect the pulse wave signal reference signal as a reference in the certain hour section, as the pulse wave signal of collecting for 5 seconds extracts several peak points T as a reference signal from reference signal
1,2 ..., nWith several valley points B
1,2 ..., n, calculate the meansigma methods T of all peak points
AvgMeansigma methods B with all valley points
Avg, get T
AvgAnd B
AvgIntermediate value (T
Avg+ B
Avg)/2 are as the reference zero P that calculates eartbeat interval
0As shown in Figure 3, the pulse wave signal that obtains is in real time searched for, when the curve of pulse wave signal from top to bottom by reference zero P
0The time, count the starting point of a pulse, the interval between the starting point of adjacent twice pulse is the interval between twice heart beating.Owing between a bit of, once new heart beating occurs, so the data of acquisition disperse on time domain, discrete eartbeat interval numerical point is connected in turn, obtained the heart rate variability signal on the time domain, as shown in Figure 4.
Need to prove, in technical scheme of the present invention, gather physiology signal, and the heart rate variability signal how to calculate on the time domain according to the physiology signal of gathering does not limit adopting which kind of method.
Step 102: the heart rate variability signal on the described time domain is carried out time-frequency conversion, choose the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency domain characteristic after conversion according to heart rate variability;
Wherein, after the power spectrum sequence after the time-frequency conversion acquisition conversion, frequency domain characteristic according to heart rate variability, only the power spectrum subsequence of some can be reflected heart rate variability in the power spectrum sequence, has the actual physical meaning, the power spectrum subsequence of remainder can not be reflected heart rate variability, does not have the actual physical meaning.Therefore, before extracting parameter, need choose the power spectrum subsequence of reflecting heart rate variability the power spectrum sequence after conversion.Can adopt a kind of generally acknowledged method in technical scheme of the present invention, that is, selecting frequency is less than the power spectrum subsequence of 0.4Hz in the power spectrum sequence after conversion.Certainly, except with 0.4Hz as the screening threshold value, also can utilize other frequency values as the screening threshold value, technical solution of the present invention to the screening threshold value do not do qualification.
When the heart rate variability signal data amount on the time domain is big,, can carry out time-domain sampling to the heart rate variability signal on the discrete time domain in order further to reduce the data processing amount that time-frequency changes.
Based on this situation, preferably, described heart rate variability signal on the time domain is carried out time-frequency conversion, the power spectrum subsequence of choosing the reflection heart rate variability the power spectrum sequence of frequency domain character after conversion according to heart rate variability comprises: the heart rate variability signal on the described time domain is sampled, obtain the discrete series of heart rate variability signal; Described discrete series is carried out time-frequency conversion, choose the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency domain character after conversion according to heart rate variability.
In addition, in some cases, the user only pays close attention to the heart rate variability characteristic ginseng value in a period of time, as in some certain period of time or in the time period recently.In order to satisfy this demand, can also further intercept the heart rate variability signal of sampling.
Step 103: from upwards the add up performance number of each frequency of the minimum frequency examination of described power spectrum subsequence, and, from high frequency points begin to add up the downwards performance number of each frequency, the ratio of performance number and total power value first frequency during greater than low frequency search ratio and second frequency during greater than high frequency search ratio obtain respectively adding up, wherein, described low frequency search ratio and high frequency search ratio all are inversely proportional to the distribution concentration degree of the power spectrum subsequence of heart rate variability analysis system requirements;
For example, if selecting frequency is less than the power spectrum subsequence of 0.4Hz in the power spectrum sequence after conversion, then the minimum frequency in this power spectrum subsequence is 0Hz, and high frequency points is 0.4Hz.From minimum frequency 0Hz begin upwards the to add up performance number of each frequency, simultaneously, also from high frequency points 0.4Hz begin to add up the downwards performance number of each frequency, the ratio of total power value that accounts for the power spectrum subsequence up to the performance number that upwards adds up is greater than low frequency search ratio R
Low, the performance number that adds up downwards accounts for the ratio of the total power value of power spectrum subsequence and searches for ratio R greater than high frequency
HighAt this moment, when determining that the ratio of total power value that the performance number upwards add up accounts for the power spectrum subsequence is greater than low frequency search ratio at last by pairing first frequency of the performance number on adding up, and when determining that the ratio of total power value that the performance number that adds up accounts for the power spectrum subsequence is greater than high frequency search ratio downwards at last by second frequency of the performance number correspondence on adding up.
Wherein, low frequency search ratio R
LowWith high frequency search ratio R
HighAll the distribution concentration degree with the power subsequence of heart rate variability analysis system requirements is inversely proportional to.For example, when the distribution concentration degree of the power subsequence of heart rate Variability Analysis system requirements is high, can low frequency search for ratio and high frequency search ratio is set to a less value, otherwise, when the distribution concentration degree of the power subsequence of heart rate Variability Analysis system requirements is low, can low frequency search for ratio and high frequency search ratio is set to a bigger value.And in technical scheme of the present invention, the distribution concentration degree that does not limit the power subsequence of heart rate variability analysis system requirements is done concrete qualification, in different systems, can set different numerical value according to demand.Certainly, low frequency search ratio can be set at identical value with high frequency search ratio, also can be set at different values.
A kind of preferred embodiment is that low frequency search ratio and high frequency search ratio are 0.05.
Step 104: described second frequency and first frequency are asked poor, obtain the band bandwidth of described power spectrum subsequence, described band bandwidth is the characteristic parameter of reflection heart rate variability.
According to formula f
Width=P
High-P
LowCalculate band bandwidth, wherein, P
LowBe first frequency, P
HighBe second frequency, f
WidthBe band bandwidth.
Obviously, by the band bandwidth that above processing obtained is main power component in the power spectrum subsequence, for the heart rate variability form, its form is a sine curve, main power component part is the peak value part in sine curve, as shown in Figure 5, Fig. 5 is for to carry out the process sketch map that the band bandwidth at main power component place is determined in bidirectional research to the power spectrum subsequence.
With the characteristic parameter of band bandwidth,, therefore, just reflected further also whether the heart rate variability form approaches sinuous pattern and change because it has reflected the peak Distribution of the modality curves of heart rate variability as heart rate variability.The tracing pattern of heart rate variability approaches sine more, and Power Spectrum Distribution is concentrated more, bandwidth f
WidthAlso just narrow more; Otherwise the tracing pattern of heart rate variability connects more and is not bordering on sine, and Power Spectrum Distribution is not concentrated more, bandwidth width f
WidthAlso just wide more.Therefore, by bandwidth width f
Width, can reflect whether the form of heart rate variability approaches the sinuous pattern variation.
In addition,, can also go the dimension processing, obviously, go the characteristic parameter after the dimension processing can reflect still whether the form of heart rate variability approaches sinuous pattern and change characteristic parameter as a kind of mode of texturing.
For example, can be according to formula
Band bandwidth is gone the dimension processing, wherein, F
wFor removing the characteristic parameter after the dimensionization.If selecting frequency is less than the power spectrum subsequence of 0.4Hz in the power spectrum sequence after conversion, the minimum frequency in the then above-mentioned formula is 0Hz, and high frequency points is 0.4Hz.Certainly, except above-mentioned formula, can also adopt of the prior art other to go the dimension formula to handle.In technical scheme of the present invention, do not limit removing dimension processing formula.
From the processing formula that goes dimensionization as can be seen, obtain F after the processing
wWith band bandwidth f
WidthThe relation of being inversely proportional to.Therefore, simple derivation can get: the tracing pattern of heart rate variability approaches sine more, and Power Spectrum Distribution is concentrated more, bandwidth f
WidthAlso just narrow more, F
wBig more; Otherwise the tracing pattern of heart rate variability connects more and is not bordering on sine, and Power Spectrum Distribution is not concentrated more, bandwidth width f
WidthAlso just wide more, F
wMore little.
That obtain by the way in addition, is pairing heart rate variability characteristic parameter F of some time periods
w, that is, and the F of a sample point
w, obtain the heart rate variability characteristic parameter of a complete test record if desired, be further with the F of each the sample point n in the record
wAdd with average, determine the heart rate variability characteristic parameter of whole record, its computing formula is as follows:
Wherein, F (n) is the F of n sample point
w, S is the heart rate variability characteristic parameter of whole record.
As can be seen from the above-described embodiment, compared with prior art, the present invention has following advantage:
The characteristic parameter that utilizes the scheme in the embodiment of the invention to obtain not only can be described the cyclically-varying of heart rate variability, and power spectrum is being carried out in the process of bidirectional research, each Frequency point is searched for successively according to its position relation, when the primary and secondary peak is adjoining, bandwidth (characteristic parameter) is less, otherwise characteristic parameter is then bigger.Therefore, characteristic parameter can also be differentiated the relation of the position between the primary and secondary peak value in the power spectrum subsequence, makes the approaching primary and secondary peak Distribution in position obtain better metric evaluation.This method has solved prior art to the insensitive deficiency in Power Spectrum Distribution position, has expressed the physiology and the psychological meaning of heart rate variability signal representative more accurately.
Embodiment two
Under the certain situation, the distribution of power spectrum subsequence concentrates on extremely low frequency (VLF, Very LowFrequency) part, because extremely low frequency partly are in the edge of whole power spectrum subsequence, its concentrated distribution is not a peak Distribution truly, do not represent the cyclically-varying of heart rate variability form, therefore, need revise the characteristic parameter of this moment.The difference of present embodiment and embodiment one is: whether the distribution of judging the power spectrum subsequence concentrates on the extremely low frequency part, if, further to going the characteristic parameter after the dimensionization to revise.See also Fig. 6, it is the flow chart of another embodiment of the acquisition methods of a kind of heart rate variability characteristic parameter of the present invention.Specifically may further comprise the steps:
Step 601: calculate the heart rate variability signal that obtains on the time domain according to physiology signal;
Step 602: the heart rate variability signal on the described time domain is carried out time-frequency conversion, choose the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency domain characteristic after conversion according to heart rate variability;
Step 603: from the minimum frequency of described power spectrum subsequence begin upwards the to add up performance number of each frequency, and, from high frequency points begin to add up the downwards performance number of each frequency, the ratio of performance number and total power value first frequency during greater than low frequency search ratio and second frequency during greater than high frequency search ratio obtain respectively adding up, wherein, described low frequency search ratio and high frequency search ratio all are inversely proportional to the distribution concentration degree of the power spectrum subsequence of heart rate variability analysis system requirements;
Step 604: described second frequency and first frequency are asked poor, obtain the band bandwidth of described power spectrum subsequence, described band bandwidth is the characteristic parameter of reflection heart rate variability;
The implementation of above-mentioned steps 601-604 can will repeat no more in the present embodiment referring to the step 101-104 among the embodiment one.
Step 605: the performance number component that calculates extremely low frequency part in the described power spectrum subsequence, if the performance number component of described extremely low frequency part accounts for the ratio of total power value greater than revising threshold value, with go after the dimensionization characteristic parameter divided by modifying factor square, obtain revised characteristic parameter, described modifying factor is the product of the performance number component of described extremely low frequency part divided by total power value and correction threshold value, and described correction threshold value is that the performance number component of extremely low frequency part accounts for the maximum normal distribution ratio in the total power value.
Usually, the frequency range of extremely low frequency part is that 0Hz is to 0.04Hz.Calculate the performance number component of this frequency range,, think that the distribution of power spectrum subsequence concentrates on the extremely low frequency part, need revise if the performance number component of extremely low frequency part accounts for the ratio of total power value greater than revising threshold value.This correction threshold value is that the performance number component of extremely low frequency part accounts for the maximum normal ratio in the total power value.Need to prove that because the specificity of human body, for each individuality, the maximum normal ratio that the performance number component of extremely low frequency part accounts in the total power value all is different.In embodiments of the present invention, can obtain one and revise threshold value by the method for data statistics, for example, preferred, this correction threshold value is 0.3.
When the characteristic parameter after dimensionization is gone out in judgement need be revised, calculate modifying factor by following formula:
Wherein,
Be the performance number component of described extremely low frequency part, TP is a total power value, and M is for revising threshold value, and C is a modifying factor.
At last, pass through formula
Revise removing the characteristic parameter after the dimensionization.
As can be seen from the above-described embodiment, compared with prior art, the present invention has following advantage:
The characteristic parameter that utilizes the scheme in the embodiment of the invention to obtain not only can be described the cyclically-varying of heart rate variability, and power spectrum is being carried out in the process of bidirectional research, each Frequency point is searched for successively according to its position relation, when the primary and secondary peak is adjoining, bandwidth (characteristic parameter) is less, otherwise characteristic parameter is then bigger.Therefore, characteristic parameter can also be differentiated the relation of the position between the primary and secondary peak value in the power spectrum subsequence, makes the approaching primary and secondary peak Distribution in position obtain better metric evaluation.This method has solved prior art to the insensitive deficiency in Power Spectrum Distribution position, has expressed the physiology and the psychological meaning of heart rate variability signal representative more accurately.
In addition, when the distribution of power spectrum subsequence concentrates on the extremely low frequency part, to going the characteristic parameter after the dimensionization to revise, can improve the accuracy of characteristic parameter by further.
Embodiment three
Describe the preferred implementation that obtains characteristic parameter below in detail.See also Fig. 7, it is the flow chart of an embodiment of the acquisition methods of a kind of heart rate variability characteristic parameter of the present invention, may further comprise the steps:
Step 701: utilize infrared photoelectric sensor to gather pulse wave signal on one's body from person to be measured;
In addition, also can gather electrocardiosignal on one's body from person to be measured.
Step 702: the reference zero of determining pulse wave signal;
For example, the pulse wave signal of collecting for 5 seconds extracts several peak points T as a reference signal from reference signal
1,2 ..., nWith several valley points B
1,2 ..., n, calculate the meansigma methods T of all peak points
AvgMeansigma methods B with all valley points
Avg, get T
AvgAnd B
AvgIntermediate value (T
Avg+ B
Avg)/2 are as the reference zero P that calculates eartbeat interval
0
In addition, the intravital every physiological feature of people is time dependent, and therefore, this reference zero is determined by dynamical fashion, that is, when obtaining characteristic parameter, determined reference zero one time at every turn.Like this, can improve the accuracy of the characteristic parameter that finally obtains.
Step 701 and step 702 can be carried out simultaneously, do not retrain its sequencing relation.
Step 703:, determine the pulse starting point of the pulse wave signal of collection by the zero crossing detection mode;
Wherein, zero crossing detects to be searched for the pulse wave signal that obtains in real time with exactlying, when the curve of pulse wave signal passes through reference zero P from top to bottom
0The time, count the starting point of a pulse.
Step 704: according to the heart rate variability signal that detects on the pulse wave starting point acquisition time domain that obtains;
Wherein, interval between the starting point of adjacent twice pulse is the interval between twice heart beating, owing between a bit of, once new heart beating occurs, so, the data that obtain disperse on time domain, discrete eartbeat interval numerical point is connected in turn, has obtained the heart rate variability signal on the time domain.
Step 705: to the heart rate variability signal on the time domain at the time domain up-sampling;
Step 706: the discrete heart rate variability signal that sampling is obtained intercepts, and obtains the discrete subsequence of a heart rate variability signal;
For example, as the discrete series f that obtains one group of heart rate variability signal by sampling
IBI(n) after, if the length N of this discrete series 〉=128 o'clock then intercept the subsequence f that current 128 nearest numerical points are formed
IBI(n '), n '=N-127 ..., N.To satisfy the demand that the user only pays close attention to the characteristic parameter of nearest time period.
Certainly, in order to satisfy other demand of user, can also adopt other interception way.
Step 707: the discrete subsequence to the heart rate variability signal carries out Fourier transformation, obtains the power spectrum sequence;
Step 708:, from the power spectrum sequence, choose the power spectrum subsequence of power less than 0.4Hz according to the frequency domain characteristic of heart rate variability;
Step 709: the power spectrum subsequence is carried out bidirectional research, determine band bandwidth;
Wherein, from upwards the add up performance number of each frequency of the minimum frequency examination of described power spectrum subsequence, and, from high frequency points begin to add up the downwards performance number of each frequency, the ratio of performance number and total power value first frequency during greater than low frequency search ratio and second frequency during greater than high frequency search ratio obtain respectively adding up, wherein, described low frequency search ratio and high frequency search ratio all are inversely proportional to the distribution concentration degree of the power spectrum subsequence of heart rate variability analysis system requirements.Described second frequency and first frequency are asked poor, obtain the band bandwidth of described power spectrum subsequence, described band bandwidth is the characteristic parameter of reflection heart rate variability.
Step 710: band bandwidth as characteristic parameter, is gone the dimension processing to characteristic parameter;
Step 711: the performance number component that calculates extremely low frequency part in the described power spectrum subsequence, if the performance number component of described extremely low frequency part accounts for the ratio of total power value greater than revising threshold value, with going the characteristic parameter after the dimensionization to revise, obtain revised characteristic parameter.
Wherein, the correcting process process is: will go after the dimensionization characteristic parameter divided by modifying factor square, modifying factor is the product of the performance number component of described extremely low frequency part divided by total power value and correction threshold value, and described correction threshold value is that the performance number component of extremely low frequency part accounts for the maximum normal distribution ratio in the total power value.
As can be seen from the above-described embodiment, compared with prior art, the present invention has following advantage:
The characteristic parameter that utilizes the scheme in the embodiment of the invention to obtain not only can be described the cyclically-varying of heart rate variability, and power spectrum is being carried out in the process of bidirectional research, each Frequency point is searched for successively according to its position relation, when the primary and secondary peak is adjoining, bandwidth (characteristic parameter) is less, otherwise characteristic parameter is then bigger.Therefore, characteristic parameter can also be differentiated the relation of the position between the primary and secondary peak value in the power spectrum subsequence, makes the approaching primary and secondary peak Distribution in position obtain better metric evaluation.This method has solved prior art to the insensitive deficiency in Power Spectrum Distribution position, has expressed the physiology and the psychological meaning of heart rate variability signal representative more accurately.
In addition, when the distribution of power spectrum subsequence concentrates on the extremely low frequency part, to going the characteristic parameter after the dimensionization to revise, can improve the accuracy of characteristic parameter by further.
Embodiment four
Corresponding with the acquisition methods of a kind of heart rate variability characteristic parameter among the present invention, the embodiment of the invention also provides a kind of deriving means of heart rate variability characteristic parameter, see also shown in Figure 8, it is the structure chart of an embodiment of the deriving means of a kind of heart rate variability characteristic parameter among the present invention, this device comprises: signature computation unit 801, time-frequency conversion unit 802, frequency computing unit 803 and calculation of characteristic parameters unit 804, wherein
Time-frequency conversion unit 802 is used for the heart rate variability signal on the described time domain is carried out time-frequency conversion, chooses the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency characteristic after conversion according to heart rate variability;
Calculation of characteristic parameters unit 804 is used for described second frequency and first frequency are asked poor, obtains the band bandwidth of described power spectrum subsequence, and described band bandwidth is the characteristic parameter of reflection heart rate variability.
Preferably, see also Fig. 9, it is the structure chart of another embodiment of the deriving means of a kind of heart rate variability characteristic parameter among the present invention, as shown in Figure 9, said apparatus also further comprises: dimension processing unit 805, go described characteristic parameter to the dimension processing.
Further preferred, see also Figure 10, it is the structure chart of another embodiment of the deriving means of a kind of heart rate variability characteristic parameter among the present invention, on apparatus structure basis shown in Figure 9, also further comprise: amending unit 806, be used for calculating the performance number component of described power spectrum subsequence extremely low frequency part, if the performance number component of described extremely low frequency part accounts for the ratio of total power value greater than revising threshold value, with go after the dimensionization characteristic parameter divided by modifying factor square, obtain revised characteristic parameter, described modifying factor is the product of the performance number component of described extremely low frequency part divided by total power value and correction threshold value, and described correction threshold value is that the performance number component of extremely low frequency part accounts for the maximum normal distribution ratio in the total power value.
Above-mentioned correction threshold value is preferably 0.3.
In addition, preferred, see also Figure 11, it is the unitary structural representation of time-frequency conversion among the present invention, as shown in figure 11, time-frequency conversion unit 802 comprises: sampling subelement 8021 and varitron unit 8022,
Low frequency search ratio in the frequency computing unit 803 and high frequency search ratio preferred 0.05.
As can be seen from the above-described embodiment, compared with prior art, the present invention has following advantage:
The characteristic parameter that utilizes the scheme in the embodiment of the invention to obtain not only can be described the cyclically-varying of heart rate variability, and power spectrum is being carried out in the process of bidirectional research, each Frequency point is searched for successively according to its position relation, when the primary and secondary peak is adjoining, bandwidth (characteristic parameter) is less, otherwise characteristic parameter is then bigger.Therefore, characteristic parameter can also be differentiated the relation of the position between the primary and secondary peak value in the power spectrum subsequence, makes the approaching primary and secondary peak Distribution in position obtain better metric evaluation.This method has solved prior art to the insensitive deficiency in Power Spectrum Distribution position, has expressed the physiology and the psychological meaning of heart rate variability signal representative more accurately.
In addition, when the distribution of power spectrum subsequence concentrates on the extremely low frequency part, to going the characteristic parameter after the dimensionization to revise, can improve the accuracy of characteristic parameter by further.
More than the acquisition methods and the device of a kind of heart rate variability characteristic parameter provided by the present invention is described in detail, used specific embodiment herein principle of the present invention and embodiment are set forth, the explanation of above embodiment just is used for helping to understand method of the present invention and core concept thereof; Simultaneously, for one of ordinary skill in the art, under the principle prerequisite that does not break away from the present invention's description, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (12)
1. the acquisition methods of a heart rate variability characteristic parameter is characterized in that, comprising:
Calculate the heart rate variability signal that obtains on the time domain according to physiology signal;
Heart rate variability signal on the described time domain is carried out time-frequency conversion, choose the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency domain characteristic after conversion according to heart rate variability;
From the minimum frequency of described power spectrum subsequence begin upwards the to add up performance number of each frequency, and, from high frequency points begin to add up the downwards performance number of each frequency, the ratio of performance number and total power value first frequency during greater than low frequency search ratio and second frequency during greater than high frequency search ratio obtain respectively adding up, wherein, described low frequency search ratio and high frequency search ratio all are inversely proportional to the distribution concentration degree of the power spectrum subsequence of heart rate variability analysis system requirements;
Described second frequency and first frequency are asked poor, obtain the band bandwidth of described power spectrum subsequence, described band bandwidth is the characteristic parameter of reflection heart rate variability.
2. method according to claim 1 is characterized in that, described method also comprises: described characteristic parameter is gone the dimension processing.
3. method according to claim 2, it is characterized in that, described method also comprises: the performance number component that calculates extremely low frequency part in the described power spectrum subsequence, if the performance number component of described extremely low frequency part accounts for the ratio of total power value greater than revising threshold value, with go after the dimensionization characteristic parameter divided by modifying factor square, obtain revised characteristic parameter, described modifying factor is the product of the performance number component of described extremely low frequency part divided by total power value and correction threshold value, and described correction threshold value is that the performance number component of extremely low frequency part accounts for the maximum normal distribution ratio in the total power value.
4. method according to claim 3 is characterized in that, described correction threshold value is 0.3.
5. according to any described method among the claim 1-4, it is characterized in that, described heart rate variability signal on the time domain is carried out time-frequency conversion, the power spectrum subsequence of choosing the reflection heart rate variability the power spectrum sequence of frequency domain character after conversion according to heart rate variability comprises:
Heart rate variability signal on the described time domain is sampled, obtain the discrete series of heart rate variability signal;
Described discrete series is carried out time-frequency conversion, choose the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency domain character after conversion according to heart rate variability.
6. according to any described method among the claim 1-4, it is characterized in that described low frequency search ratio and high frequency search ratio are 0.05.
7. the deriving means of a heart rate variability characteristic parameter is characterized in that, comprising:
Signature computation unit is used for calculating heart rate variability signal on the time domain according to physiology signal;
The time-frequency conversion unit is used for the heart rate variability signal on the described time domain is carried out time-frequency conversion, chooses the power spectrum subsequence of reflection heart rate variability the power spectrum sequence of frequency characteristic after conversion according to heart rate variability;
The frequency computing unit, be used for from the minimum frequency of described power spectrum subsequence begin upwards the to add up performance number of each frequency, and, from high frequency points begin to add up the downwards performance number of each frequency, the ratio of performance number and total power value first frequency during greater than low frequency search ratio and second frequency during greater than high frequency search ratio obtain respectively adding up, wherein, described low frequency search ratio and high frequency search ratio all are inversely proportional to the distribution concentration degree of the power spectrum subsequence of heart rate variability analysis system requirements;
The calculation of characteristic parameters unit is used for described second frequency and first frequency are asked poor, obtains the band bandwidth of described power spectrum subsequence, and described band bandwidth is the characteristic parameter of reflection heart rate variability.
8. device according to claim 7 is characterized in that, described device also comprises: the dimension processing unit is used for described characteristic parameter is gone the dimension processing.
9. device according to claim 8 is characterized in that, described device also comprises:
Amending unit, be used for calculating the performance number component of described power spectrum subsequence extremely low frequency part, if the performance number component of described extremely low frequency part accounts for the ratio of total power value greater than revising threshold value, with go after the dimensionization characteristic parameter divided by modifying factor square, obtain revised characteristic parameter, described modifying factor is the product of the performance number component of described extremely low frequency part divided by total power value and correction threshold value, and described correction threshold value is that the performance number component of extremely low frequency part accounts for the maximum normal distribution ratio in the total power value.
10. method according to claim 9 is characterized in that, described correction threshold value is 0.3.
11., it is characterized in that described time-frequency conversion unit comprises according to any described device among the claim 7-10:
The sampling subelement is used for the heart rate variability signal on the described time domain is sampled, and obtains the discrete series of heart rate variability signal;
The varitron unit is used for described discrete series is carried out time-frequency conversion, chooses the power subsequence of reflection heart rate variability the power spectrum sequence of frequency domain character after conversion according to heart rate variability.
12., it is characterized in that described low frequency search ratio and high frequency search ratio are 0.05 according to any described device among the claim 7-10.
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