Patent Application: US-25012503-A

Abstract:
a method and system for identifying a subject comprises obtaining a digitised recording of an electrocardiogram measurement of the subject to be identified , the digitised recording being a cyclic waveform having a peak amplitude . the digitised recording is normalised to reduce variations due to physiological effects , and the normalised recording is processed to determine a feature vector in the frequency domain . the distance between the determined feature vector and a predetermined feature vector is measured to identify the subject .

Description:
there have been a number of advances in ecg ( electrocardiogram ) measurement techniques recently which enhance both the accuracy and convenience of the data collection [ 1 ]. such techniques have made it possible to identify , for example , that an individual &# 39 ; s ecg depends upon the orientation of the individual &# 39 ; s heart [ 2 ]. such dependence suggests that an identifying signature based on ecg can be robust against fraudulent reproduction . the previous work in this direction [ 3 ] used supervised clustering techniques on time domain parameters and reported encouraging results . we employ robust frequency domain techniques after eliminating or minimizing known sources of variabilities . an electro - cardiogram is a representation of the heart &# 39 ; s electrical activities . typically , an ecg measures and records different electrical potentials on the surface of the human body . these potentials arise from the electrical activities of the heart . an ecg cycle may roughly be divided into the phases of depolarization and repolarization of the muscle fibers making up the heart . the depolarization phases correspond to the p - wave ( atrial depolarization ) and qrswave ( ventricles depolarization ). the repolarization phases make up the t - wave and u - wave ( ventricular repolarization ). the different peaks of the ecg - complex are shown in fig1 . an ecg is typically measured by placing ten electrodes on selected spots on the human body surface . the most common ecg measurement makes use of 10 electrodes placed on the body . out of these ten electrodes , six electrodes are placed on the chest , and four electrodes are placed on the extremities . a comprehensive discussion of electro - cardiogram and measurement principles can be found in [ 4 ]. the electrical potential differences in 12 different directions out of the ten electrodes are measured . these 12 different electrical views of the activity in the heart are normally referred to as leads . the 12 leads are made up of three bipolar and nine monopolar leads . the three bipolar leads are the electrical potentials between the right and left arm ( lead i ), the right arm and left foot ( lead ii ), and between the left arm and left foot ( lead iii ). for the monopolar leads , four different artificial reference points are constructed . these reference points are the average of the signals seen at two or more electrodes . using these reference points , the potentials appearing on the left arm ( avl ), the right arm ( avr ), the left foot ( avf ), and on the six chest electrodes ( v 1 - v 6 ) are measured . the right foot is normally used for grounding purposes only . it is true that there is substantial variability among individuals , as demonstrated by several studies [ 5 , 6 , 7 ]. this inter - individual variability is a problem from a medical diagnostic point of view . furthermore , there is an intra - individual variability . for example , one study [ 8 ] has reported the effects of exercises on the shape of the ecg cycle ( i . e . normalized slopes ) in addition to the more palpable variations in overall rate and amplitude . the method proposed by the present invention does not model the variations in slope of the ecg cycle , but concentrates on the qualitative constancy in the shape , and formalizes ways of reducing the intra - individual variability to a point where it is not significant . an ecg depends on physical conditions such as exertion , medical condition such as fever and emotional states such as anger or fear . in addition to such intrinsic variations , the measurement techniques introduce another set of uncertainties . noise pickup , electrode position and conductive differences all contribute to the intra - individual variability . there are also statistical effects to worry about . the statistical errors in the ecg signal are minimised by taking an average of a number of consecutive ecg cycles . note that a simple minded averaging based on the average time period does not work in the case of bio - electric signals because of the tiny uncertainties in the time period ( see fig2 ( b )). it is necessary to line up the different ecg cycles at some prominent feature point . in a preferred embodiment of the invention the qrs complex ( see fig1 ) is used to line up different cycles . the algorithm described in [ 9 ] is used to search for the qrs complex in a cycle . the systematic variations ( slow drifts , calibration issues ) are handled by normalizing the amplitude . the r peak is normalised to have a value of one and ensure that the average dc component is zero . it is important to have zero dc value as a fourier analysis is used in the frequency domain and non - zero dc value induces artificial differences in any distance measured between spectra . the variations due to ( non - medical ) physiological reasons typically show up as changes in the fundamental frequency , i . e . the heart beats faster or slower . in order to normalise this systematic variability , the averaged cycle is “ stretched ” or “ compressed ” to a constant time period using resampling techniques . resampling is trivial in the frequency domain . sampling up implies padding the frequency spectrum with zeros , and sampling down is the same as band - limiting . in order to reduce known variabilities further , ecg cycles are validated before averaging , i . e . ensuring that they are not very different from each other . this is done by studying the quadrature distance between each cycle used in computing the average ecg cycle and the running average . [ 0046 ] fig3 shows the distribution of the quadrature distance between an ecg cycle and the average ecg cycle used to compute the metric . based on this distribution , it is preferred that the distance be less than 1 before averaging is performed . this selection criterion removes about 10 % of the cycles and improves the statistical quality of the metric . the data sample used in this work was proposed by physionet [ 10 ] and the tools available therefrom for accessing the data were used in preferred embodiments of the present invention . the records were visually inspected and it was subjectively decided which records to use . the decision was based on the shape of the ecg being close to an ideal ecg ( fig1 ). a qualitatively reasonable definition of the qrs complex is required for the algorithm used in preferred embodiments of the present invention to work . for the general studies , the qt database was used , which contains a total of 105 fifteen - minute excerpts of two channel ecgs . a detailed description of this database is available in [ 11 ]. out of these , 84 samples were chosen for the following studies . from each ecg signal , multiple sections of 10 cycles were studied , each at about 40 second intervals . for robustness studies , the mit - bih normal sinus rhythm database was used . this database includes 18 long - term ecg recordings of subjects referred to the arrhythmia laboratory at boston &# 39 ; s beth israel hospital ( now the beth israel deaconess medical center ). subjects included in this database were found to have had no significant arrhythmias ; they include 5 men , aged 26 to 45 , and 13 women , aged 20 to 50 . 11 of these 18 data sets were used for the present robustness studies . multiple sections of about 10 cycles at more than 1 hour interval ( 100 times longer than the general studies ) were studied and the metrics between same and different persons were compared . an ideal cardiac cycle is shown in fig1 . a measured ecg cycle is quite a bit more noisy . the shape and constancy of a measured cycle ( e . g . fig2 ( a )) depend on measurement effects , electrode placement , noise , etc . the different steps in the analysis are aimed at getting to the ideal picture starting from a measurement . after reading the data , an attempt is made to minimize statistical effects by averaging over a number of cycles ( 10 in the current version ). as shown in fig2 ( b ), a straightforward wrapping around of the cycles using the average time period is disastrous . the qrs complex of each ecg cycle is lined up before taking an average . this results in a waveform shown in fig2 ( c ). note that even in this , there is some systematic variation between cycles , as evidenced by the thickness of the overlaid curves . this is minimized by normalising each cycle so that the r peak is at 1 and the average dc is zero . this is shown in fig2 ( d ). also shown on fig2 ( d ) is the averaged waveform . any selection on a statistical ensemble can skew the error distribution and bias the conclusions . hence , it is necessary to verify that the error distribution is normal after the selection in validating the ecg cycles . [ 0052 ] fig4 shows the distribution of normalized deviations from the mean for each point in the ecg cycles . if x is the measurement , î¼ the mean and ï · the estimated standard deviation , then the distribution plotted is that of î ′. if the ï · is well estimated and the errors are unbiased , then the distribution is supposed to be a normal gaussian . superimposed on the distribution in fig4 is a free fit to a gaussian . the fit values correspond well to a normal distribution , verifying that the errors are not skewed in anyway . once the average typical ecg cycle is obtained with normalised amplitude , it is necessary to consider the variations in the time axis . such variations are likely to come from physiological causes . in preferred embodiments of the present invention , time period variations are handled by stretching or compressing the wave to a standard time period using fft resampling techniques . then a metric is defined as the first 64 components of the frequency spectrum . note that the spectrum is well behaved and has zero value at 0 hz since we normalised the ecg cycle . also , note that the spectrum is independent of time shifts in the averaged cycle , i . e . the qrs complex can be anywhere within the period without affecting the spectrum . despite this property , in a preferred embodiment of the present invention , the average cycle was rotated so that the qrs complex always falls at a certain fixed position . this is done so to enable the extraction of more information from the phase spectrum later . fig5 shows a typical metric . in order to estimate the statistical significance of any measure of distance between two metrics , it is essential to understand the errors associated with the metric . the standard deviation of the 10 cycles of ecg used to compute the metric is a measure of the statistical error associated with the ecg measurement . it can be shown that the errors on the metric are completely determined by the errors on the typical , averaged ecg cycle and the phase response of the fft . the following describes how these errors can be propagated to the metric . once the errors are understood , it is possible to construct an 2 and use it as a measure of distance between two metrics . consider a signal in the time domain defined by n time samples and the corresponding errors . using a vector notation , it can be represented as x â ± x . ( x is an n dimensional vector ). the fft of x is to be referred to as y . the fast fourier transform ( fft ) of x is defined as : y k ≡ ∑ j = 0 n - 1  x j    2π   k   j n ( 3 ) the errors on y are î · y . taking the differential of equation ( 3 ), these errors may be computed as δ   y = δ y k = δ  ∑ j = 0 n - 1  x j      2  π   kj n = ∑ j = 0 n - 1  δ   x j      2  π   k   j n = fft  ( δ   x ) ( 4 ) the metric is the frequency response f and the errors on the components fk are of interest . f can be expressed in n dimensional vector notation which translates to component notation as y k = ( y k )+ i ℑ ( y k )= f k cos φ k + if k sin φ k ( 5 ) equation ( 5 ) defines î ¦ k by equating real and imaginary parts . also , f ≡| y |={ square root }{ square root over ( ( y ) 2 + ℑ ( y ) 2 )} ( 6 ) equation ( 6 ) can be rewritten in terms of the components as the errors on y k may be propagated to those in f k by taking the differentials of equation ( 7 ) | f k δf k = ( y k ) δ ( y k )|+| ℑ ( y k ) δℑ ( y k )|= ( y k ) ( δ y k )|+| ℑ ( y k ) ℑ ( δ y k )|( using ( y k )= f k cos φ and ℑ ( y k )= f k sin φ k from equation ( 5 ) | δ f k |=| ( δ y k ) cos φ k |+| ℑ ( δ y k ) sin φ k | substituting for î ′ y k from equation ( 4 ), and going back to the vector notation : δ f = ( δ y ) cos φ + ℑ ( δ y ) sin φ = ( fftδx ) cos φ + ℑ ( fftδx ) sin φ thus , the errors on the metric ( f ) can be completely calculated from the errors on the typical , averaged ecg cycle ( x ) and the phase response of the fft . the standard deviation of the 10 cycles of ecg used to compute the metric is a measure of the statistical error î · x . this is propagated and î · f computed as described above . since the frequency components define an orthogonal basis , the metric may be thought of as a vector in a 64 dimensional space . then a measure of distance between two metrics may be defined as the quadrature distance in the 64 dimensional space . the quadratic distance ( qd ) between two metrics ( { right arrow over ( m )} 1 and { right arrow over ( m )} 2 whose components are m 1 j and m 2 j ) is defined as : q d ≡ ∑ j = 1 64  ( m 1  j - m 2  j ) 2 for ecgs from the same person at different times , the distribution of this metric distance should peak around zero . for different people , the peak should be at a positive value . fig6 shows these two distributions , which confirm expectations . as the statistical errors to the metric have been propagated , a more accurate measure of the significance of the distance may be defined as the 2 difference between the metrics . the 2 is defined as χ 2 ≡ ∑ j = 1 64  ( m 1  j - m 2  j ) 2 σ 1  j 2 + σ 2  j 2 ( 10 ) where , in addition to the symbols used in equation ( 1 ), the errors on the components of the two metrics ï · 1j and ï · 2j are included for two ecgs from the same person at different times , it is anticipated that the distribution of this metric distance will peak around zero . for different people , the peak should be at a positive value . fig7 a and 7 b show these two distributions , which confirm expectations . 2 . the time period is dynamically recomputed , and the beginning of each ecg cycle is identified by matching the qrs complex . 3 . the amplitude in each cycle is normalised so that the r peak is 1 . 0 and the dc value is zero . 4 . the data is wrapped at the dynamically recomputed periods so that the r peaks overlie each other . 5 . the average ecg ( and the statistical error on it ) is found by summing up the overlying ecg cycles . 7 . the frequency spectrum ( and the error ) of the average , normalised ecg cycle is computed . 8 . the frequency response of the normalised average cycle is compared to study the inter - and intra - individual variability . [ 0080 ] fig6 and 8 show the results of the applicant &# 39 ; s studies as histogram distributions of the distance measures ( quadrature distance in fig6 and 2 distance in fig8 .) on the x axis is plotted the distance value and on the y axis is plotted the number of times such a distance value is obtained . since the areas under each histogram is normalized to unity , these distributions represent the probability density functions of qd and 2 . in each figure , the qd and 2 from same person &# 39 ; s ecg taken at different times and different people &# 39 ; s ecg have been super - imposed . with an arbitrary selection criterion ( the “ cut ” value chosen to coincide where the two curves intersect , for example ) on qd or 2 , it is possible to compute the efficiency , false acceptance rate and false rejection rate as defined below . efficiency describes how often the method embodying the present invention succeeds in identifying the right person using ecg metric . it is defined as the fraction of the right combinations accepted . it corresponds to the area of the right combination curve below the “ cut ” value . false acceptance rate far describes how often the method embodying the present invention falsely identifies a wrong person using ecg metric . it is the fraction of the wrong combinations accepted . it corresponds to the area of the wrong combination curve below the “ cut ” value . false rejection rate frr is a measure of the frequency of the right person being rejected . it corresponds to the area of the right combination curve above the “ cut ” value . equal error rate eer is traditionally used as a measure of the “ goodness ” of a biometric system . it is defined as the point where false acceptance rate equals the false rejection rate . [ 0085 ] fig6 and 8 establish the inter - individual variability that can be used for identification purposes . i . e ., these figures show that the ecgs from different individuals are different in a consistent way and that the feature metrics extracted from these ecgs amplify the characteristics , which can be used for identification . however , they do not establish that for the same subject , the ecg taken at different times under different conditions may not confuse the identification algorithms . in order to verify that this intra - individual variability does not pose a threat to results , a series of long term data files are analyzed . ( see the section on data sampling described above for details .) similar to the general analysis , multiple sections of about 10 cycles were chosen and the metrics compared between the same and different persons . however , for robustness studies , the interval between the chosen samples was increased by a factor of about 100 ( compared to our general analysis ). [ 0086 ] fig7 a and 7 b show the results of the robustness studies . the statistics available for long term data are limited . however , fig6 shows remarkable consistency with fig6 and 8 . the results obtained from the studies are tabulated in table 1 . the entries in this table are derived from fig6 a , 7 b and 8 and they summarize both the general analysis and the robustness studies . particular attention should be paid to the following : 1 . the 2 distance between the metric gives better results . this was expected because proper treatment of the measurement errors must reduce the erroneous estimate of significance in distance measurements . 2 . the long term data , though statistically limited , gives numbers similar to the short term data . this proves that even relatively long periods ( of almost 24 hours ), ecg waveform from a person retains distinctive features that the analysis embodying the present invention is able to extract . it has been shown in the present study that known variabilities in a user &# 39 ; s ecg signal may be normalized out to come up with a robust metric . this metric can be used to identify different users . by looking at the data to which access was obtained , it will be seen that about 77 % of the wrong combinations are rejected while keeping about 77 % of the right combinations ( i . e ., an equal error rate of 23 %). the data used contained medical pathologies where the ecg data really changed during the measurements ( i . e ., heart conditions manifesting themselves during the measurement time ). it is anticipated that the acceptance rate will go up for normal subjects . conversely , whereas the data used in the present experiments contains consecutive data for a number of patients under controlled conditions , it may be useful for the database to include data for normal healthy subjects under varying conditions . although only a single embodiment of the invention has been described , many variations are possible within the scope of the invention as will be evident to a skilled reader . the disclosure of the following references is incorporated herein in its entirety by reference : [ 1 ] c . j . harland , t . d . clark and r . j . prance . “ electrical potential probes new directions in the remote sensing of the human body ”. measurement science and technology , 13 : 163169 , 2002 . 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