CN108888258A - A kind of electrocardiosignal characteristic area detection method applied to wearable device - Google Patents
A kind of electrocardiosignal characteristic area detection method applied to wearable device Download PDFInfo
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- CN108888258A CN108888258A CN201810485226.3A CN201810485226A CN108888258A CN 108888258 A CN108888258 A CN 108888258A CN 201810485226 A CN201810485226 A CN 201810485226A CN 108888258 A CN108888258 A CN 108888258A
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- 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
- A61B5/24—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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Abstract
Present disclose provides a kind of electrocardiosignal characteristic area detection methods applied to wearable device；This be applied to wearable device electrocardiosignal characteristic area detection method include：Calculate the coefficient of wavelet decomposition WT (n) of input ecg signal；The coefficient of wavelet decomposition WT (n) is handled using Empirical mode decomposition, determination includes the simple component sequence I (t) of energy distribution information in electrocardiosignal；Envelope demodulation is carried out using simple component sequence I (t) described in Hilbert transform pairs, thus detection obtains the distributed intelligence of the electrocardiosignal characteristic area.The electrocardiosignal characteristic area detection method complexity that the disclosure is applied to wearable device is low, and accuracy rate is high, realizes signal noise silencing and feature extraction integration, reduces processing expense.
Description
Technical field
This disclosure relates to field of signal processing, more specifically to the technical field of electro-physiological signals processing, specifically
It is related to a kind of electrocardiosignal characteristic area detection method applied to wearable device, is that one kind is suitble to wearable portable system
The detection method of the low complex degree of realization, high-accuracy.
Background technique
ECG signal, hereinafter referred to as electrocardiosignal are to be produced in the physiological activity of heart by the electrical activity superposition of cardiac muscle
Raw.In the scene of daily monitoring, being adopted based on the double leads of the wall of the chest and single lead with advantage small in size, without external lead wire
The electrocardio collection device of mode set is more widely applied, and its processing circuit has both toward contact convenient for integrated excellent
Gesture, and such acquisition node often has that Signal-to-Noise is poor, it is therefore desirable to by characteristic area on-line checking into
The mode of row wireless transmission is handled, and under the scene of wearable device, such region is generally referred to as QRS wave cluster
Region with relatively large time domain energy distributed intelligence.
Currently used QRS wave method of identification mainly includes time domain method, neural network and Time-frequency Analysis.
Time domain method is, amplitude big this time domain waveform feature big for there are slopes in cardiac electrical characteristic area to carry out wave
The method of shape detection the advantage is that computational complexity is low, realizes convenient for Hardware, however since there are anti-interference energy for this method
The defect of power difference is not suitable for being applied in wearable device.
Neural network generally trains neural network recognizer according to training set, and the advantage of this method is to train
Collection selection can obtain very good accuracy of identification when proper, but since there is the differences between individual for physiological signal itself
It is different, thus such methods suffer from the limitation of training set.
Time-frequency Analysis is a kind of methods and instruments for gathering time and frequency domain analysis, and having can be to energy in signal and spy
Sign point accurately identify, the advantage that anti-noise jamming ability is strong.Usually such methods operand is also bigger, is less convenient for
It is realized on portable device.
To sum up, it is special to need the electrocardiosignal applied to wearable device that a kind of complexity is low, accuracy rate is high, operand is small
Levy method for detecting area.
Summary of the invention
(1) technical problems to be solved
Present disclose provides a kind of electrocardiosignal characteristic area detection methods applied to wearable device, at least partly
Solve technical problem set forth above.
(2) technical solution
According to one aspect of the disclosure, a kind of electrocardiosignal characteristic area detection applied to wearable device is provided
Method, including：
Calculate the coefficient of wavelet decomposition WT (n) of input ecg signal；
The coefficient of wavelet decomposition WT (n) is handled using Empirical mode decomposition, determination includes energy in electrocardiosignal
The simple component sequence I (t) of distributed intelligence；
Envelope demodulation is carried out using simple component sequence I (t) described in Hilbert transform pairs, thus detection obtains the electrocardio
The distributed intelligence in signal characteristic region.
In some embodiments, replace the side of cubic spline interpolation using linear interpolation in the Empirical mode decomposition
Formula is decomposed, that is, simplifies Empirical mode decomposition.
In some embodiments, it using the frequency band decline characteristic of empirical mode decomposition result, is based on and coefficient of wavelet decomposition
Correlation choose simple component sequence I (t).
In some embodiments, it chooses and the maximum intrinsic mode function of coefficient of wavelet decomposition WT (n) related coefficient
The output of single order is as the simple component sequence I (t) afterwards.
In some embodiments, the electrocardiosignal characteristic area detection method applied to wearable device is described
Further include after the step of carrying out envelope demodulation using simple component sequence I (t) described in Hilbert transform pairs：To the envelope solution
Obtained signal is adjusted to carry out phase only pupil filter and characteristic area division.
In some embodiments, envelope demodulation is caused according to the principle being aligned nearby with vertex to be detected in electrocardiosignal
Phase drift be modified.
In some embodiments, correction amount is the mean value of each apex offset amount in each frame.
In some embodiments, the coefficient of wavelet decomposition WT of input ecg signal is calculated based on displacement and addition and subtraction operation
(n)。
In some embodiments, intermediate value is obtained by shifting function, then by carrying out the period to the intermediate value
Delay accumulation is the coefficient of wavelet decomposition WT (n) for obtaining specific rank.
(3) beneficial effect
It can be seen from the above technical proposal that the disclosure is applied to the electrocardiosignal characteristic area detection side of wearable device
Method at least has the advantages that one of them：
(1) disclosure is applied to the electrocardiosignal characteristic area detection method of wearable device, and complexity is low, accuracy rate
Height, operand are small, and the application be conducive in wearable portable equipment is realized.
(2) Time-frequency Analysis is utilized, specially simplifies Empirical mode decomposition, realizes signal noise silencing and feature extraction one
Body.
(3) it is decomposed in such a way that linear interpolation replaces cubic spline interpolation in the Empirical mode decomposition,
The Time-frequency Analysis for simplifying Empirical mode decomposition alternate standard, further reduced the computing overhead of disposed of in its entirety.
(4) the existing narrow-band characteristic of the intrinsic mode function itself obtained using empirical mode decomposition, in conjunction with Hilbert
The characteristics of transformation can filter out stationary noise, while realizing and being filtered out in electrocardiosignal with what frequency steadily interfered, and to energy
Measure effective detection of time domain distribution.
Detailed description of the invention
By the way that shown in attached drawing, above and other purpose, the feature and advantage of the disclosure will be more clear.In all the attached drawings
Identical appended drawing reference indicates identical part, does not deliberately draw attached drawing by actual size equal proportion scaling, it is preferred that emphasis is show
The purport of the disclosure out.
Fig. 1 is the electrocardiosignal characteristic area detection method process for being applied to wearable device according to the embodiment of the present disclosure
Figure.
Fig. 2 is the pseudocode flow chart for simplifying empirical mode decomposition according to the embodiment of the present disclosure.
Specific embodiment
For the purposes, technical schemes and advantages of the disclosure are more clearly understood, below in conjunction with specific embodiment, and reference
The disclosure is further described in attached drawing.
It should be noted that similar or identical part all uses identical figure number in attached drawing or specification description.It is attached
The implementation for not being painted or describing in figure is form known to a person of ordinary skill in the art in technical field.In addition, though this
Text can provide the demonstration of the parameter comprising particular value, it is to be understood that parameter is equal to corresponding value without definite, but can connect
It is similar to be worth accordingly in the error margin or design constraint received.The direction term mentioned in embodiment, for example, "upper", "lower",
"front", "rear", "left", "right" etc. are only the directions with reference to attached drawing.Therefore, the direction term used is for illustrating not to use
To limit the protection scope of the disclosure.
The present disclosure proposes a kind of complexities, and the electrocardio applied to wearable device low, that accuracy rate is high, operand is small is believed
Number characteristic area detection method, based on the method combination of waveform energy distribution information, simplified and approximate processing in signal, to have
The reduction operand of effect is preferably suitable for wearable demand.
The electrocardiosignal characteristic area detection method that the disclosure is applied to wearable device mainly includes the following steps that：
The coefficient of wavelet decomposition WT (n) of input ecg signal is calculated based on displacement and addition and subtraction operation, that is to say, that in reality
In the calculating process of existing coefficient of wavelet decomposition WT (n), it is based only upon displacement and addition and subtraction calculating can be (specifically, can passes through displacement
Operation obtains intermediate value, then by carrying out the wavelet decomposition system that period delay accumulation obtains specific rank to the intermediate value
Number WT (n)), it is not directed to multiplication calculating, (disclosure detection method both can be by hardware realization, can also without using multiplier
With by software realization, when hardware realization, can save multiplier, and when software realization can avoid multiplication and calculate), it realizes to input
Electrocardiosignal medium-high frequency glitch noise and the preliminary of low frequency baseline drift filter out；
The coefficient of wavelet decomposition WT (n) is handled using Empirical mode decomposition is simplified, determination includes in electrocardiosignal
The simple component sequence I (t) of energy distribution information；Wherein, simplify Empirical mode decomposition i.e. to standardized Time-frequency Analysis into
Certain simplification is gone, specifically, it replaces the side of cubic spline interpolation in Empirical mode decomposition using linear interpolation
Formula is decomposed, and the simple component sequence I for carrying energy distribution information in original electro-cardiologic signals so as to low overhead is obtained
(t), the preliminary de-noising to signal may be implemented in the process itself for seeking simple component sequence I (t)；And
Above-mentioned simple component sequence I (t) is regarded as the narrow band signal by amplitude-modulating modulation, using described in Hilbert transform pairs
Simple component sequence I (t) carries out envelope demodulation, the demodulating information sought, the distribution letter of characteristic area as in input ecg signal
Breath.
In above-mentioned steps, using the frequency band decline characteristic of empirical mode decomposition result, it is based on and coefficient of wavelet decomposition
Correlation choose simple component sequence I (t)；Preferably, it chooses and maximum of coefficient of wavelet decomposition WT (n) related coefficient
The output of single order is as the simple component sequence I (t) after sign mode function.
Optionally, described the step of carrying out envelope demodulation using simple component sequence I (t) described in Hilbert transform pairs
Later, further include：Phase only pupil filter is carried out to the signal that the envelope demodulation obtains and characteristic area divides.Wherein, according to the heart
Principle phase drift caused by envelope demodulation that vertex to be detected is aligned nearby in electric signal is modified.Correction amount can be
The mean value of each apex offset amount in each frame.
As shown in Figure 1, in one embodiment, the electrocardiosignal characteristic area applied to wearable device detects
Method includes the following steps：
S11, electrocardiosignal sampling.
The characteristics of electrocardiosignal, is frequency band Relatively centralized, generally about concentrates on big in the low-frequency range of 0~30Hz
In the frequency bandwidth of about 10Hz, but since the detailed information that electrocardiosignal carries is more, generally requires and selected in conjunction with practical application
Signal acquisition is carried out with suitable sample rate.
S12 seeks Harr wavelet conversion coefficient (also referred to as coefficient of wavelet decomposition).
The feature that disclosure detection method makes full use of its equivalence filter tap coefficient absolute value of Harr small echo consistent is led to
It crosses shifting function and obtains intermediate value, then can be obtained specific rank by way of carrying out period delay accumulation to intermediate value
Coefficient of wavelet decomposition WT (n) has evaded multiplication operation as a result, has greatly reduced operand.
S13, seek include energy distribution information in electrocardiosignal simple component sequence.
Specifically, being decomposed using Empirical mode decomposition is simplified to coefficient of wavelet decomposition WT (n)：On the one hand by making
Replace the mode of cubic spline interpolation with linear interpolation to improve the computing overhead of empirical mode decomposition, on the other hand, according to warp
The frequency band decline characteristic of mode decomposition is tested, and in order to evade the influence of the biggish high-frequency noise of content in coefficient of wavelet decomposition, is selected
It takes with the output of single order after the maximum intrinsic mode function of coefficient of wavelet decomposition WT (n) related coefficient of input as the list sought
Vector sequence I (t).Disclosure detection method both can be by hardware realization, can also be by software realization, when software realization pair
Answer the pseudocode of this step as shown in Figure 2.
The disclosure realizes signal noise silencing and feature extraction integration using Time-frequency Analysis, and uses simplified empirical modal
Decomposition method carrys out the Time-frequency Analysis of alternate standard, further reduced the computing overhead of disposed of in its entirety.
S14 carries out envelope demodulation to above-mentioned simple component sequence.
Specifically, carrying out envelope demodulation using simple component sequence I (t) described in Hilbert transform pairs, thus detection is obtained
The distributed intelligence of the electrocardiosignal characteristic area, Hilbert transform can be considered as the line for having pi/2 phase delay to input
Property filter, and when input be narrow band signal when, construct following formula：
Z (t)=X (t)+iY (t)
In formula, X (t) refers to the signal of Hilbert transform input, and Y (t) is the sequence obtained after converting.If correspondingly will
The narrow band signal of input is approximatively considered as amplitude-modulated signal, thenThe defeated of envelope demodulation as is carried out to signal
Out.
Narrow-band characteristic existing for the intrinsic mode function itself obtained using empirical mode decomposition, in conjunction with Hilbert transform
The characteristics of stationary noise can be filtered out, while realizing and being filtered out in electrocardiosignal with what frequency steadily interfered, and when to energy
Effective detection of domain distribution.
S15, phase only pupil filter and characteristic area divide.
Specifically, may be implemented according to the principle being aligned nearby with vertex to be detected in original electro-cardiologic signals to demodulated signal
Phase only pupil filter.Correction amount is defined as the mean value of each its offset of vertex in each frame.Specific characteristic area is divided by user
It is drawn a circle to approve according to the actual demand of oneself.
The electrocardiosignal characteristic area detection method applied to wearable device that the disclosure proposes is a kind of energy
Sensitive detection method has significant compared to traditional detection method in the wearable application of noise circumstance complexity
Accuracy benefits can have the application of demand to provide better support to detect to characteristic area.
So far, attached drawing is had been combined the embodiment of the present disclosure is described in detail.According to above description, art technology
The electrocardiosignal characteristic area detection method that personnel should be applied to wearable device to the disclosure has clear understanding.
It should be noted that in attached drawing or specification text, the implementation for not being painted or describing is affiliated technology
Form known to a person of ordinary skill in the art, is not described in detail in field.In addition, the above-mentioned definition to each element and not only limiting
Various specific structures, shape or the mode mentioned in embodiment, those of ordinary skill in the art can carry out simply more it
Change or replaces.
Similarly, it should be understood that in order to simplify the disclosure and help to understand one or more of each open aspect,
Above in the description of the exemplary embodiment of the disclosure, each feature of the disclosure is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the disclosed method should not be interpreted as reflecting the following intention：It is i.e. required to protect
The disclosure of shield requires features more more than feature expressly recited in each claim.More precisely, as following
Claims reflect as, open aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following specific embodiment are expressly incorporated in the specific embodiment, wherein each claim itself
All as the separate embodiments of the disclosure.
Particular embodiments described above has carried out further in detail the purpose of the disclosure, technical scheme and beneficial effects
Describe in detail it is bright, it is all it should be understood that be not limited to the disclosure the foregoing is merely the specific embodiment of the disclosure
Within the spirit and principle of the disclosure, any modification, equivalent substitution, improvement and etc. done should be included in the guarantor of the disclosure
Within the scope of shield.
Claims (9)
1. a kind of electrocardiosignal characteristic area detection method applied to wearable device, including：
Calculate the coefficient of wavelet decomposition WT (n) of input ecg signal；
The coefficient of wavelet decomposition WT (n) is handled using Empirical mode decomposition, determination includes Energy distribution in electrocardiosignal
The simple component sequence I (t) of information；
Envelope demodulation is carried out using simple component sequence I (t) described in Hilbert transform pairs, thus detection obtains the electrocardiosignal
The distributed intelligence of characteristic area.
2. the electrocardiosignal characteristic area detection method according to claim 1 applied to wearable device, wherein in institute
It states in Empirical mode decomposition and is decomposed in such a way that linear interpolation replaces cubic spline interpolation, that is, simplify empirical modal point
Solution.
3. the electrocardiosignal characteristic area detection method according to claim 1 applied to wearable device, wherein utilize
The frequency band decline characteristic of empirical mode decomposition result, based on choosing simple component sequence I (t) with the correlation of coefficient of wavelet decomposition.
4. the electrocardiosignal characteristic area detection method according to claim 3 applied to wearable device, wherein choose
Output with single order after the maximum intrinsic mode function of coefficient of wavelet decomposition WT (n) related coefficient is as the simple component sequence
It arranges I (t).
5. the electrocardiosignal characteristic area detection method according to claim 1 applied to wearable device, described
After the step of carrying out envelope demodulation using simple component sequence I (t) described in Hilbert transform pairs, further include：To the envelope
It demodulates obtained signal and carries out phase only pupil filter and characteristic area division.
6. the electrocardiosignal characteristic area detection method according to claim 1 applied to wearable device, wherein according to
The principle phase drift caused by envelope demodulation being aligned nearby with vertex to be detected in electrocardiosignal is modified.
7. the electrocardiosignal characteristic area detection method according to claim 6 applied to wearable device, wherein amendment
Amount is the mean value of each apex offset amount in each frame.
8. the electrocardiosignal characteristic area detection method according to claim 1 applied to wearable device, wherein be based on
Displacement and addition and subtraction operation calculate the coefficient of wavelet decomposition WT (n) of input ecg signal.
9. the electrocardiosignal characteristic area detection method according to claim 8 applied to wearable device, wherein pass through
Shifting function obtains intermediate value, then by carrying out the small wavelength-division that period delay accumulation obtains specific rank to the intermediate value
It solves coefficient WT (n).
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