CN102217932B - Brand-new algorithm for ABR (auditory brainstem response) signal crest detection - Google Patents
Brand-new algorithm for ABR (auditory brainstem response) signal crest detection Download PDFInfo
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- CN102217932B CN102217932B CN 201110126653 CN201110126653A CN102217932B CN 102217932 B CN102217932 B CN 102217932B CN 201110126653 CN201110126653 CN 201110126653 CN 201110126653 A CN201110126653 A CN 201110126653A CN 102217932 B CN102217932 B CN 102217932B
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Abstract
The invention relates to a brand-new algorithm for ABR (auditory brainstem response) signal crest detection. A superposition average technology is adopted to denoise an acquired original ABR signal, then automatic identification is carried out on crest detection of an ABR wave, no matter whether the ABR signal is a sensation level signal or a hearing level signal, all the crests in a wave form can be easily and accurately detected by adopting the method, each ABR subwave can be independently analyzed, and effective ABR parameters are provided for hearing threshold evaluation and surgical operation monitoring.
Description
Technical field
The present invention relates to a kind of detection technique, particularly a kind of algorithm of ABR signal wave crest detection.
Background technology
Brainstem auditory evoked potential ABR uses the most widely one of auditory evoked potential clinically.ABR is the bioelectric of Auditory neuropathway before brain stem and the brain stem, and it is to stimulate afterwards and induce a series of current potentials incubation period in 10ms lack sound, belongs to short delitescence and brings out current potential.Can be recorded to 7 positive phase waves the adult, usually use the Roman number I, II, III, IV, V, VI, VII represent, and be wherein the most obvious with I, III, V ripple.These ripples are with the degree that incubation period of determining and uncertain amplitude embody the main body threshold of audibility, hearing loss, perhaps are used for real-time monitored auditory nerve situation.In the threshold of audibility evaluate application, we need Automatic Logos ABR waveform, for clinical practice provides assistance in diagnosis.
At present, the method that detects the positive crest of ABR has: the wave filter that the local maximum, design of directly searching ABR ripple after the filtering and interested crest mate, utilize neutral net, also have the most frequently used method to be: the wavestrip of restriction ripple, search the zero crossing of waveform first derivative, the different Wave crest and wave troughs of distinguishing of direction according to zero crossing have namely found crest.In addition, also proposed many decomposition detection method, at first narrow frequency band filter detects crest, then obtains more accurate crest time Estimate with a wider bandpass filters.Because narrow band filter has the signal to noise ratio (SNR) of an improvement, simultaneously, the broad frequency band wave filter has improved the definition of crest time, has therefore thisly really improved detection under the low stimulus intensity by coarse to accurate method.Yet, these detection methods mainly are applicable to the estimation of threshold of audibility, and be merely able to detect limited crest, as detecting amplitude and incubation period more stable I all, III, the V ripple, perhaps sometimes detecting the V ripple is the stack ripple of original IV and V ripple, this can bring to diagnosis clinically very big inconvenience unavoidably.
Summary of the invention
The present invention be directed to the circumscribed problem of system detection method, proposed the algorithm that a kind of ABR signal wave crest detects, the crest that is applied to the ABR ripple based on the parser of vector detects and Automatic Logos, realizes simply, has high accuracy.
Technical scheme of the present invention is: the algorithm that a kind of ABR signal wave crest detects, and use vector operation that the ABR crest is carried out Automatic Logos, comprise following concrete steps:
1) denoising of original ABR signal:
Adopt the superposed average technology that the original ABR signal that collects is carried out denoising, namely apply N stimulation, the discretization data were adjusted into the moment of each stimulation appearance align, then average.Be that these adopt ABR signal that superposed average methods draw all based on following hypothesis:
A) under identical stimulation, the waveform of brain evoked response signal is constant, and namely response signal is deterministic signal;
B) background noise has randomness, through repeatedly superposeing and can eliminating.And, the pass of it and ABR signal is f (t)=s (t)+n (t), s in the formula (t) is the real not ABR signal of Noise, n (t) is background noise, f (t) is the measurement data of ABR signal+noise, under such hypothesis, obtained relatively level and smooth ABR signal;
2) the ABR ripple is 2D signal, establishes the sampling time to be
x, sample magnitude is
y, namely sampled point be expressed as (
x,
y), for the signal curve that collects, get window area, be made as A, value is respectively
y 1 ,
y 2.。。, y m ,, the sampling time is respectively
x 1 , x 2 ..., x m , altogether get
mIt is individual,
mBe odd number, the coordinate of each point be (
x n ,
y n ), wherein
n=1,2...
m, then the middle point coordinates in the window area is
, the vector between other points of window area and the mid point can be expressed as
, the difference of the amplitude between 2 is
, existing along y direction of principal axis amount of orientation (0 ,-△ y), be designated as
, compute vector then
With
The cosine value of angle is:
, vector between each point and the mid point in the calculation window zone
And vector
The included angle cosine value
, and among the accumulative total window A
Value is calculated SUM=
3) establish total N the data point of ABR data, window size is m point, and take step-length as 1 moving window, window size is constant, and the window number that then obtains is N-m+1, is designated as A
1, A
2A
N-m+1Accumulate successively in each window
Value calculates corresponding SUM
1, SUM
2SUM
N-m+1, and record the mid point coordinate figure of each window; By the characteristic of cosine function as can be known,
With
Cosine value is being for just,
Cosine value is worked as SUM for negative
nSUM
N-1, and SUM
nSUM
N+1, corresponding window A then
nThe interior maximum that occurred, maximum are window A
nMid point;
4) with the cosine value of each window and SUM by from big to small ordering, come the larger window of front SUM and just comprised all extreme points; In these points, select the crest of ABR ripple I, II, III, IV, V.
Beneficial effect of the present invention is: the algorithm that a kind of ABR signal wave crest of the present invention detects, the crest that is applied to the ABR ripple detects and Automatic Logos.No matter ABR is sensation level or the signal of hearing level, and the method can be easy to, very accurately detect all crests in the waveform, can analyze individually each ABR wavelet (such as the V ripple).Monitoring for threshold of audibility assessment and surgical operation provides effective ABR parameter.
Description of drawings
Fig. 1 is that vector calculates sketch map in the algorithm that detects of a kind of ABR signal wave crest of the present invention;
Fig. 2 is the ABR signal graph of removing in the algorithm that detects of a kind of ABR signal wave crest of the present invention behind the noise;
Fig. 3 is ABR peak value sign figure as a result in the algorithm that detects of a kind of ABR signal wave crest of the present invention.
The specific embodiment
The algorithm algorithm that a kind of ABR signal wave crest detects comprises the steps:
One, the denoising of original ABR signal:
Adopt the superposed average technology that the original ABR signal that collects is carried out denoising, namely apply N stimulation, the discretization data were adjusted into the moment of each stimulation appearance align, then average.Be that these adopt ABR signal that superposed average methods draw all based on following hypothesis:
A) under identical stimulation, the waveform of brain evoked response signal is constant, and namely response signal is deterministic signal.
B) background noise has randomness, through repeatedly superposeing and can eliminating.And the pass of it and ABR signal is f (t)=s (t)+n (t), and s in the formula (t) is real ABR signal (not Noise), and n (t) is background noise, and f (t) is measurement data (ABR signal+noise).Under such hypothesis, we have obtained relatively level and smooth ABR signal.
Two, the Automatic Logos of ABR crest:
The BR ripple is 2D signal, establishes the sampling time (discrete time) to be
x, sampled value (amplitude) is
y, namely sampled point be expressed as (
x,
y).
For the signal curve that collects, get window area, be made as A, value is respectively
y 1 ,
y 2.。。, y m , the sampling time is respectively
x 1 , x 2 ..., x m , altogether get
mIt is individual,
mBe odd number, the coordinate of each point be (
x n ,
y n ), wherein
n=1,2...
mThen the middle point coordinates in the window area is
, the vector between other points of window area and the mid point can be expressed as
, the difference of the amplitude between 2 is
, existing along y direction of principal axis amount of orientation (0 ,-△ y), be designated as
, compute vector then
With
The cosine value of angle is:
, vector between each point and the mid point in the calculation window zone
And vector
The included angle cosine value
, and among the accumulative total window A
Value is calculated SUM=
, vector calculates sketch map as shown in Figure 1.
Therefore establish total N the data point of ABR data, window size is m point, and take step-length as 1 moving window, window size is constant, and the window number that then obtains is N-m+1, is designated as A
1, A
2A
N-m+1Accumulate successively in each window
Value calculates corresponding SUM
1, SUM
2SUM
N-m+1, and record the mid point coordinate figure of each window.By the characteristic of cosine function as can be known,
With
Cosine value is being for just,
Cosine value is worked as SUM for negative
nSUM
N-1, and SUM
nSUM
N+1, corresponding window A then
nThe interior maximum that occurred, maximum are window A
nMid point.So we if with the cosine value of each window and SUM by from big to small ordering, come the larger window of front SUM and just comprised all extreme points.Next step work is exactly to have selected ABR ripple I, II, III, IV, V wave-wave peak just in these points.
At Xinhua Hospital 3 individualities without dysaudia are carried out a large amount of ABR experiments, adopt short sound to stimulate, with stimulus frequency 23Hz, under 40dB, 45dB, 50dB, 55dB, 60dB, 65dB, each stimulation sound intensity of 70dB, 75dB, gather 1024 data points in time at 10ms, obtained the experimental data of priori.The original ABR signal that we at first will collect carries out denoising, then adopts new algorithm that it is carried out the crest Automatic Logos.
Rely on the preclinical clinical experience value of ABR wavelet, roughly determine the position at I, II, III, IV, V wave-wave peak according to the preclinical empirical value excursion of each ripple.Each PL reference value of ABR when only select to stimulate sound intensity 75dB here (
) be reference frame, the PL reference value of ABR when shown in following table one, stimulating sound intensity 75dB:
Table one
Corresponding we collect the ABR data at Xinhua Hospital, corresponding 1024 data points of 10ms.Each wavelet corresponding data point scope incubation period is following table two wavelet corresponding data points incubation period:
Table two
The ripple I | The ripple II | The ripple III | The ripple IV | The ripple V |
167±14 | 291±17 | 400±17 | 513±107 | 588±20 |
178±10 | 282±16 | 391±16 | 512±107 | 582±21 |
136±17 | 259±23 | 374±26 | 502±26 | 571±27 |
According to top form selection window length, length of window should be greater than the preclinical maximum changing range 54 of ripple, and less than the minimum range 101(of every adjacent two wave crest points here because the IV ripple is too unstable, do not consider the IV ripple).Therefore, be that 81 points are proper by our selection window length of upper table.Because I, III, V ripple absolute latency are more stable, we can at first find the wave crest point of I, III, V ripple, then between I and III crest, look for the II wave crest point, between III and V crest, look for the IV wave crest point, at last I, II, III, IV, V crest are identified.
Find the coordinate figure of all crests of ABR ripple according to the method, and then can calculate I, II, III, IV, accurate incubation period of V ripple and amplitude.
Carry out the date processing experiment with MATLAB7.0, MATLAB7.0 has powerful data-handling capacity.At first the ABR initial data is carried out superposed average and process to remove noise, ABR signal behind the removal noise as shown in Figure 2, ABR signal after the denoising Processing is very level and smooth as seen from Figure 2, below we use the algorithm of proposition that all crests of ABR in the experimental data are identified, result such as Fig. 3, a large amount of experiments show that the crest sign that we realize has very high correctness.
Claims (1)
1. the algorithm that the ABR signal wave crest detects is characterized in that, uses vector operation that the ABR crest is carried out Automatic Logos, comprises following concrete steps:
1) denoising of original ABR signal:
Adopt the superposed average technology that the original ABR signal that collects is carried out denoising, namely apply N stimulation, the discretization data were adjusted into the moment of each stimulation appearance align, then average;
Be that these adopt ABR signal that superposed average methods draw all based on following hypothesis:
A) under identical stimulation, the waveform of brain evoked response signal is constant, and namely response signal is deterministic signal;
B) background noise has randomness, through repeatedly superposeing and can eliminating;
And, the pass of it and ABR signal is f (t)=s (t)+n (t), s in the formula (t) is the real not ABR signal of Noise, n (t) is background noise, f (t) is the measurement data of ABR signal+noise, under such hypothesis, obtained relatively level and smooth ABR signal;
2) the ABR ripple is 2D signal, establishes the sampling time to be
x, sample magnitude is
y, namely sampled point be expressed as (
x,
y), for the signal curve that collects, get window area, be made as A, value is respectively
y 1 ,
y 2 , 。。 , y m ,, the sampling time is respectively
x 1 , x 2 ..., x m , altogether get
mIt is individual,
mBe odd number, the coordinate of each point be (
x n ,
y n ), wherein
n=1,2...
m, then the middle point coordinates in the window area is
, the vector between other points of window area and the mid point can be expressed as
, the difference of the amplitude between 2 is
, existing along y direction of principal axis amount of orientation (0 ,-△ y), be designated as
, compute vector then
With
The cosine value of angle is:
, vector between each point and the mid point in the calculation window zone
And vector
The included angle cosine value
, and among the accumulative total window A
Value is calculated SUM=
3) establish total N the data point of ABR data, window size is m point, and take step-length as 1 moving window, window size is constant, and the window number that then obtains is N-m+1, is designated as A
1, A
2A
N-m+1Accumulate successively in each window
Value calculates corresponding SUM
1, SUM
2SUM
N-m+1, and record the mid point coordinate figure of each window; By the characteristic of cosine function as can be known,
With
Cosine value is being for just,
Cosine value is worked as SUM for negative
nSUM
N-1, and SUM
nSUM
N+1, corresponding window A then
nThe interior maximum that occurred, maximum are window A
nMid point;
4) with the cosine value of each window and SUM by from big to small ordering, come the larger window of front SUM and just comprised all extreme points; In these points, select the crest of ABR ripple I, II, III, IV, V.
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CN109431487B (en) * | 2018-09-28 | 2021-11-16 | 上海乐普云智科技股份有限公司 | Method for identifying typical data of ventricular heart beat in electrocardiogram dynamic real-time analysis data |
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CN110074782B (en) * | 2019-04-30 | 2020-07-31 | 上海交通大学医学院附属第九人民医院 | Data automatic processing method based on adaptive averaging method in auditory brainstem response test |
WO2020249069A1 (en) * | 2019-06-13 | 2020-12-17 | 上海交通大学医学院附属第九人民医院 | Electrophysiological test method for auditory brainstem implant and recording electrode used by method |
CN111623958B (en) * | 2020-05-18 | 2021-11-12 | 长春欧意光电技术有限公司 | Wavelet peak-peak value extraction method in interference signal |
CN112971776A (en) * | 2021-04-19 | 2021-06-18 | 中国人民解放军总医院第六医学中心 | Method and device for determining position of characteristic waveform in hearing detection waveform |
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US5697379A (en) * | 1995-06-21 | 1997-12-16 | Neely; Stephen T. | Method and apparatus for objective and automated analysis of auditory brainstem response to determine hearing capacity |
EP1788937A2 (en) * | 2004-09-16 | 2007-05-30 | Everest Biomedical Instruments | Method for adaptive complex wavelet based filtering of eeg signals |
CN101014283A (en) * | 2004-05-01 | 2007-08-08 | Bsp生物信号处理有限公司 | Apparatus and method for analysis of high frequency qrs complexes |
CN101856225A (en) * | 2010-06-30 | 2010-10-13 | 重庆大学 | Method for detecting R wave crest of electrocardiosignal |
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US7223246B2 (en) * | 2003-06-06 | 2007-05-29 | House Ear Institute | Diagnosis of the presence of cochlear hydrops using observed auditory brainstem responses |
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US5697379A (en) * | 1995-06-21 | 1997-12-16 | Neely; Stephen T. | Method and apparatus for objective and automated analysis of auditory brainstem response to determine hearing capacity |
CN101014283A (en) * | 2004-05-01 | 2007-08-08 | Bsp生物信号处理有限公司 | Apparatus and method for analysis of high frequency qrs complexes |
EP1788937A2 (en) * | 2004-09-16 | 2007-05-30 | Everest Biomedical Instruments | Method for adaptive complex wavelet based filtering of eeg signals |
CN101856225A (en) * | 2010-06-30 | 2010-10-13 | 重庆大学 | Method for detecting R wave crest of electrocardiosignal |
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