CN104224140A - Method for filtering baseline drift by using lifting wavelet transformation - Google Patents
Method for filtering baseline drift by using lifting wavelet transformation Download PDFInfo
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Abstract
The invention discloses a method for filtering a baseline drift by using lifting wavelet transformation. The method comprises the following steps: filtering a volume pulse wave signal through a low-pass FIR (far-infrared) filter; carrying out six-layer decomposition on pulse wave data which is stored in a TXT document by using a bior2.2 wavelet basis function and a db4 wavelet basis function by virtue of lifting wavelet transformation; respectively clearing low-frequency approximation coefficients in the sixth scale, which are obtained by decomposition twice, and reconstructing approximation coefficients in the sixth scale and high-frequency detail coefficients in the first scale to the sixth scale; utilizing pulse wave signal data which is reconstructed by the bior2.2 wavelet basis function in the front 0.6 part, and utilizing the pulse wave data which is reconstructed by the db4 wavelet basis function in the rear 0.4 part; reconstructing the signal which is equal to the original volume pulse wave signal in length by combining the pulse wave signal data with the pulse wave data. The method has the beneficial effects that: the method has an obvious effect on filtration of the baseline drift in the volume pulse wave signal, and the main details of the signal are well reserved when the baseline drift is filtered.
Description
Technical field
The invention belongs to signal processing technology field, relate to a kind of method utilizing lifting wavelet transform filtering baseline drift.
Background technology
Baseline drift in pulse wave belongs to low-frequency disturbance, general at below 1Hz, scholars propose baseline drift way to solve the problem in many suppression pulse waves, conventional method has method of offset and filter method two kinds, method of offset is that the estimated signal utilizing former pulse wave signal to deduct baseline drift reaches the object removed and eliminate baseline drift, and the baseline drift that this method obtains has very large error.Filter method has average filter, medium filtering, curve fitting, high-pass filtering method, Morphological scale-space and wavelet decomposition method, high-pass filtering method can cause the wave distortion of pulse wave, morphological approach slightly offsets, method poor effect when processing larger drift pulse wave signal of curve fitting, when wavelet decomposition method is run, the processing time is long, and inconvenience is applied in real time.Lifting Wavelet maintains the characteristic of first generation small echo (classical wavelet analysis), but its wavelet construction method has been completely free of Fourier conversion, overcome the condition of classical small echo translation and flexible invariance, the small echo of acquisition has all advantages such as the Time-Frequency Localization character of classical small echo and fast transform algorithm.
Summary of the invention
The object of the present invention is to provide a kind of method utilizing lifting wavelet transform filtering baseline drift, solve the problem that baseline drift that existing method obtains has very large error.
The technical solution used in the present invention is carried out according to following steps:
Step 1: adopt the infrared pulse wave sensor of HKG-70 to gather the volume pulsation wave signal of finger tip, the sample frequency adopted is 200Hz, circuit and the cut-off frequency that writes on MSP430 single-chip microcomputer inside are gather 2400 discrete pulse wave data after the 16 rank low-pass FIR filter filtering of 10Hz altogether, by pulse wave data by USB transmission line to PC stores up into TXT document after filtering;
Step 2: utilize bior2.2 wavelet function to adopt lifting wavelet transform to carry out 6 layers of decomposition to the pulse wave data be kept in TXT document, its detailed process is: first, the lifting function lifewave utilizing Matlab instrument to provide obtains the lifting scheme LS of wavelet function bior2.2, secondly, add the lifting scheme of original ELS, again, the addlift function utilizing Matlab instrument to provide increases basic lifting scheme ELS to lifting scheme LS, thus return a new lifting scheme LSN, finally, the lwt function provided by Matlab instrument is carried out 6 dimension Lifting Wavelet to the pulse wave signal be kept in TXT document and decomposes, the lwtcoef function provided by Maltlab instrument obtains the lifting wavelet transform coefficient that the high frequency detail part of pulse wave signal and low frequency approach part respectively,
Db4 wavelet function is adopted to adopt lifting wavelet transform to carry out 6 layers of decomposition to the pulse wave signal be kept in TXT document, its detailed process is: first, the lifting function lifewave utilizing Matlab instrument to provide obtains the lifting scheme LS of wavelet function db4, secondly, add the lifting scheme of original ELS, again, the addlift function utilizing Matlab instrument to provide increases basic lifting scheme ELS to lifting scheme LS, thus return a new lifting scheme LSN, finally, the lwt function provided by Matlab instrument is carried out 6 dimension Lifting Wavelet to the pulse wave signal be kept in TXT document and decomposes, the lwtcoef function provided by Maltlab instrument obtains the lifting wavelet transform coefficient that the high frequency detail part of pulse wave signal and low frequency approach part respectively,
Step 3: the 6th layer of low frequency that the lwtcoef function provided by Matlab work in step 2 obtains volume pulsation wave signal approaches lifting wavelet transform coefficient partly, the zeros function adopting Matlab instrument to provide is to the low frequency Coefficients of Approximation zero setting process respectively on the 6th yardstick to obtain for twice, the Lifting Wavelet inverse function ilwt (CA provided by Matlab instrument, CD, W) the low-frequency approximation coefficient on the 6th yardstick of the volume pulsation wave signal after zero setting process and the high frequency detail coefficient on 1 ~ 6 yardstick are reconstructed until the 1st layer reconstructs and terminate step by step from the 6th layer, in formula, CA is low frequency Coefficients of Approximation on pulse wave signal the 6th yardstick, wherein, the low frequency Coefficients of Approximation CA of the 6th layer is the coefficient on pulse wave signal the 6th yardstick after low frequency Coefficients of Approximation zero setting process, the low frequency Coefficients of Approximation CA of the 5th layer to the 1st layer is followed successively by: the volume pulsation wave signal after the 6th yardstick to the 2nd yardstick reconstruct, CD is the high frequency detail coefficient from the 6th layer to the 1st layer, W is Lifting Wavelet title,
Step 4: the volume pulsation wave signal adopting bior2.2 and db4 two kinds of wavelet functions to reconstruct in step 3 to obtain is done at 0.6 place and combines process, wherein, front 0.6 part uses the pulse wave signal data of bior2.2 wavelet function reconstruct, the pulse wave data that rear 0.4 part utilizes db4 wavelet basis function to reconstruct; Both combine the reformulation signal isometric with former volume pulsation wave signal, thus reach the object removing baseline drift.
In volume pulsation wave signal acquisition process, due to the dynamic change of human body, so the volume pulsation wave signal collected often is subject to having a strong impact on of noise, the baseline drift wherein caused by human body respiration etc. is exactly one wherein.The present invention summary existing going, the basis of the shortcoming of arteries and veins volume pulsation wave baseline drift method poor effect or processing time length proposes, be intended to, can the processing time short, well can remove again baseline drift in pulse wave signal and obtain good pulses ripple signal.
The invention has the beneficial effects as follows that the baseline drift in filtering volume pulsation wave signal has obvious effect, while filtering baseline drift, well remain the main details of signal.
Accompanying drawing explanation
Fig. 1 is the oscillogram of the volume pulsation wave comprising baseline drift;
Fig. 2 is the spectrogram of the volume pulsation wave comprising baseline drift;
Fig. 3 is the spectrogram of volume pulsation wave after bior2.2 wavelet function promotes;
Fig. 4 is the spectrogram of volume pulsation wave after db4 wavelet basis function promotes;
Fig. 5 is the spectrogram of volume pulsation wave after bior2.2 and db4 wavelet basis function promotes;
Fig. 6 is the spectrogram of volume pulsation wave-wave after db4 wavelet function wavelet transformation;
Fig. 7 is the spectrogram of volume pulsation wave after bior2.2 wavelet function small echo changes.
Detailed description of the invention
Below in conjunction with detailed description of the invention, the present invention is described in detail.
For the feature of traditional filtering volume pulsation wave baseline drift poor effect, propose a kind of method utilizing lifting wavelet transform filtering baseline drift, carry out according to following steps:
Step 1: adopt the infrared pulse wave sensor of HKG-70 to gather the volume pulsation wave signal of finger tip, the sample frequency adopted is 200Hz, circuit and the cut-off frequency that writes on MSP430 single-chip microcomputer inside are gather 2400 discrete pulse wave data after the 16 rank low-pass FIR filter filtering of 10Hz altogether, by pulse wave data by USB transmission line to PC stores up into TXT document after filtering.
Step 2: utilize bior2.2 wavelet function to adopt lifting wavelet transform to carry out 6 layers of decomposition to the pulse wave data be kept in TXT document, its detailed process is: first, and the lifting function lifewave utilizing Matlab instrument to provide obtains the lifting scheme LS of wavelet function bior2.2.Secondly, the lifting scheme of original ELS is added.Again, the addlift function utilizing Matlab instrument to provide increases basic lifting scheme ELS to lifting scheme LS, thus returns a new lifting scheme LSN.Finally, the lwt function provided by Matlab instrument is carried out 6 dimension Lifting Wavelet to the pulse wave signal be kept in TXT document and decomposes, and the lwtcoef function provided by Maltlab instrument obtains the lifting wavelet transform coefficient that the high frequency detail part of pulse wave signal and low frequency approach part respectively.
Same employing db4 wavelet function adopts lifting wavelet transform to carry out 6 layers of decomposition to the pulse wave signal be kept in TXT document, method adopts the method for lifting wavelet transform substantially identical with employing bior2.2, and different places is for change bior2.2 wavelet function as db4 wavelet function into.
Step 3: the 6th layer of low frequency that the lwtcoef function that can be provided by Matlab work in step 2 obtains volume pulsation wave signal approaches lifting wavelet transform coefficient partly, the zeros function adopting Matlab instrument to provide is to the low frequency Coefficients of Approximation zero setting process respectively on the 6th yardstick to obtain for twice, the Lifting Wavelet inverse function ilwt (CA provided by Matlab instrument, CD, W) the low-frequency approximation coefficient on the 6th yardstick of the volume pulsation wave signal after zero setting process and the high frequency detail coefficient on 1 ~ 6 yardstick are reconstructed until the 1st layer reconstructs and terminate step by step from the 6th layer.In formula, CA is low frequency Coefficients of Approximation on pulse wave signal the 6th yardstick, wherein, the low frequency Coefficients of Approximation CA of the 6th layer is the coefficient on pulse wave signal the 6th yardstick after low frequency Coefficients of Approximation zero setting process, the low frequency Coefficients of Approximation CA of the 5th layer to the 1st layer is followed successively by: the pulse wave signal after the 6th yardstick to the 2nd yardstick reconstruct, CD is the high frequency detail coefficient from the 6th layer to the 1st layer, and W is Lifting Wavelet title.
Step 4: the volume pulsation wave signal adopting bior2.2 and db4 two kinds of wavelet functions to reconstruct in step 3 to obtain is done at 0.6 place and combines process.Wherein, front 0.6 part uses the pulse wave signal data of bior2.2 wavelet function reconstruct, the pulse wave data that rear 0.4 part utilizes db4 wavelet basis function to reconstruct;
Both combine the reformulation signal isometric with former volume pulsation wave signal, thus reach the object removing baseline drift.
The above is only to better embodiment of the present invention, not any pro forma restriction is done to the present invention, every any simple modification done above embodiment according to technical spirit of the present invention, equivalent variations and modification, all belong in the scope of technical solution of the present invention.Show by experiment, the baseline drift in the method filtering volume pulsation wave signal has obvious effect, while filtering baseline drift, well remains the main details of signal.
The inventive method is verified: in order to the method obtaining utilizing lifting wavelet transform to adopt bior2.2 and db4 wavelet function to combine removes the effect of baseline drift in volume pulsation wave, result adopts bior2.2 and db4 wavelet function to utilize the method for Lifting Wavelet to the result of volume pulsation wave signal by this experiment respectively with separately, and adopt separately bior2.2 and db4 wavelet function to utilize the method for traditional wavelet to compare the result after volume pulsation wave signal processing, the result of their processing volume pulse wave signal as shown in figs. 1-7, Fig. 1 is the oscillogram of the volume pulsation wave comprising baseline drift, Fig. 2 is the spectrogram of the volume pulsation wave comprising baseline drift, Fig. 3 is the spectrogram of volume pulsation wave after bior2.2 wavelet function promotes, Fig. 4 is the spectrogram of volume pulsation wave after db4 wavelet basis function promotes, Fig. 5 is the spectrogram of volume pulsation wave after bior2.2 and db4 wavelet basis function promotes.Fig. 6 is the spectrogram of volume pulsation wave-wave after db4 wavelet function wavelet transformation, and Fig. 7 is the spectrogram of volume pulsation wave after bior2.2 wavelet function small echo changes.The signal to noise ratio removed after baseline drift with these sides is as shown in table 1.
Table 1 several method removes the signal to noise ratio after baseline drift
As can be seen from experimental result, volume pulsation wave can remove the baseline drift in pulse wave after bior2.2 wavelet basis function promotes, but there is the situation of drift in the waveform afterbody after its reconstruct, volume pulsation wave is through db4 wavelet basis function after promoting, and the waveform after its reconstruct is thrown away in stem exists baseline drift; As can be seen from Figure 5, volume pulsation wave utilizes bior2.2 and db4 wavelet basis function after promoting, can be good at removing the composition of baseline drift, and its useful signal part have also been obtained good reservation.Although volume pulsation wave signal also can be given and remove its baseline drift part after bior2.2 and db4 wavelet function wavelet transformation, comparison sheet 1 can be found out, volume pulsation wave utilizes bior2.2 and db4 wavelet basis function after promoting and removing baseline drift, its signal to noise ratio is not only higher than the signal to noise ratio of volume pulsation wave signal through bior2.2 and db4 wavelet function wavelet transformation, also higher than the signal to noise ratio of volume pulsation wave signal after bior2.2 and db4 wavelet function promotes, absolutely prove that utilizing bior2.2 and db4 wavelet function to combine removes reasonability and the effectiveness of this method of baseline drift in volume pulsation wave through lifting wavelet transform.
Claims (1)
1. utilize a method for lifting wavelet transform filtering baseline drift, carry out according to following steps:
Step 1: adopt the infrared pulse wave sensor of HKG-70 to gather the volume pulsation wave signal of finger tip, the sample frequency adopted is 200Hz, circuit and the cut-off frequency that writes on MSP430 single-chip microcomputer inside are gather 2400 discrete pulse wave data after the 16 rank low-pass FIR filter filtering of 10Hz altogether, by pulse wave data by USB transmission line to PC stores up into TXT document after filtering;
Step 2: utilize bior2.2 wavelet function to adopt lifting wavelet transform to carry out 6 layers of decomposition to the pulse wave data be kept in TXT document, its detailed process is: first, the lifting function lifewave utilizing Matlab instrument to provide obtains the lifting scheme LS of wavelet function bior2.2, secondly, add the lifting scheme of original ELS, again, the addlift function utilizing Matlab instrument to provide increases basic lifting scheme ELS to lifting scheme LS, thus return a new lifting scheme LSN, finally, the lwt function provided by Matlab instrument is carried out 6 dimension Lifting Wavelet to the pulse wave signal be kept in TXT document and decomposes, the lwtcoef function provided by Maltlab instrument obtains the lifting wavelet transform coefficient that the high frequency detail part of pulse wave signal and low frequency approach part respectively,
Db4 wavelet function is adopted to adopt lifting wavelet transform to carry out 6 layers of decomposition to the pulse wave signal be kept in TXT document, its detailed process is: first, the lifting function lifewave utilizing Matlab instrument to provide obtains the lifting scheme LS of wavelet function db4, secondly, add the lifting scheme of original ELS, again, the addlift function utilizing Matlab instrument to provide increases basic lifting scheme ELS to lifting scheme LS, thus return a new lifting scheme LSN, finally, the lwt function provided by Matlab instrument is carried out 6 dimension Lifting Wavelet to the pulse wave signal be kept in TXT document and decomposes, the lwtcoef function provided by Maltlab instrument obtains the lifting wavelet transform coefficient that the high frequency detail part of pulse wave signal and low frequency approach part respectively,
Step 3: the 6th layer of low frequency that the lwtcoef function provided by Matlab work in step 2 obtains volume pulsation wave signal approaches lifting wavelet transform coefficient partly, the zeros function adopting Matlab instrument to provide is to the low frequency Coefficients of Approximation zero setting process respectively on the 6th yardstick to obtain for twice, the Lifting Wavelet inverse function ilwt (CA provided by Matlab instrument, CD, W) the low-frequency approximation coefficient on the 6th yardstick of the volume pulsation wave signal after zero setting process and the high frequency detail coefficient on 1 ~ 6 yardstick are reconstructed until the 1st layer reconstructs and terminate from the 6th layer step by step, in formula, CA is low frequency Coefficients of Approximation on pulse wave signal the 6th yardstick, wherein, the low frequency Coefficients of Approximation CA of the 6th layer is the coefficient on pulse wave signal the 6th yardstick after low frequency Coefficients of Approximation zero setting process, the low frequency Coefficients of Approximation CA of the 5th layer to the 1st layer is followed successively by: the pulse wave signal after the 6th yardstick to the 2nd yardstick reconstruct, CD is the high frequency detail coefficient from the 6th layer to the 1st layer, W is Lifting Wavelet title,
Step 4: the volume pulsation wave signal adopting bior2.2 and db4 two kinds of wavelet functions to reconstruct in step 3 to obtain is done at 0.6 place and combines process, wherein, front 0.6 part uses the pulse wave signal data of bior2.2 wavelet function reconstruct, the pulse wave data that rear 0.4 part utilizes db4 wavelet basis function to reconstruct; Both combine the reformulation signal isometric with former volume pulsation wave signal, thus reach the object removing baseline drift.
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