Patent Abstract:
a method and a device for the acoustic detection of a one lung intubation situation in a human subject are disclosed . according to some embodiments , the disclosed method includes computing an autoregressive moving average or an autoregressive function of electrical signals received from acoustic detectors placed at different locations on the body of the subject . appropriate locations for acoustic detectors include the back region and the chest region . the disclosed method and apparatus are insensitive to uncancelled , random background noise with a loudness associated with an operating room or intensive care ward . the disclosed device is configurable so that the relative occurrence rate of missed detections or false negatives and false positive alarms can be modified . in one exemplary embodiment , the device is adapted such that at most 9 % of identifications are false positive identifications , and at most 2 % of identifications are false negative identifications .

Detailed Description:
it has been discovered in accordance with some embodiments of the present invention that computing certain autoregression functions of a detected acoustic signal enables detection of a one lung intubation situation , even in the presence of background noise associated with operating rooms and intensive care wards . in particular , an algorithm for detecting the number of ventilated lungs from recorded breathing sounds has been developed . in some embodiments , this algorithm assumes a mimo ( multiple input multiple output ) system , in which a multi - dimensional ar ( auto - regressive ) model relates the input ( lungs ) and the output ( recorded sounds ). the unknown ar parameters are estimated , and a detector based on the estimated eigenvalues of the residual covariance matrix is developed , in order to detect a one lung ventilation situation . in the examples presented , a number of noise sources is estimated using measures such as the akaike information criterion ( aic ) or the minimum description length ( mdl ). all of these measures make several assumptions about the sources . unfortunately , the problem at hand does not obey these assumptions , the most notorious of which is the assumption of coherent distributed noise source . the lung is a diffused source rather than a point source . under the assumption of coherent distributed sources the large eigenvalues of the residual covariance matrix correspond to the sources and the small ones correspond to the diffuse noise . in the case of diffuse ( distributed ), non - coherent sources , the distinction between these two groups is not clear . threshold methods are used in order to estimate the number of sources . although in some embodiments this algorithm derives from a blind source separation model , a presently - disclosed algorithm estimates only the number of active sources or lungs , and does not require estimation of the source signal itself . the source signal is transmitted via the chest or back of the patient to the sensor . although there are non - linearities associated with transmission channel , it has been discovered by the present inventors that assuming a linear transmission channel is functional for detecting one lung intubation . furthermore , although the most general discretion of a linear channel is an arma ( auto regressive moving average ) with poles and zeros , it is known that a less general high - order ar ( auto regressive ) model including only poles provides an approximation to an arma model . in accordance with some embodiments of the present invention , it is disclosed that the ar model is functional for detecting one lung intubation . the methods of the present invention are appropriate for any distributed noise source . in some embodiments , a “ distributed noise source ” as used herein is composed of point noise sources distributed in an area of at least 75 cm 2 . in some embodiments , a “ distributed noise source ” as used herein is composed of point noise sources distributed in an area of at least 200 cm 2 . in some embodiments , a “ distributed noise source ” as used herein is composed of point noise sources distributed in an area of at least 400 cm 2 . according to some embodiments , spatial and / or temporal statistics of indigenous lung sounds are computed . examples of computed spatial include but are not limited to a cross / joint covariance matrix or cross / joint spectrum between the processes at different sensors . alternatively , it can be the cross / joint cumulant of any order or cross / joint higher - order spectra of any order greater than two between the processes of different sensors . alternatively , it can be joint probability density function of the measurement processes at the different sensors . the following examples are to be considered merely as illustrative and non - limiting in nature . it will be apparent to one skilled in the art to which the present invention pertains that many modifications , permutations , and variations may be made without departing from the scope of the invention . in the present example , the breathing sound signals are recorded by 4 microphones attached to the patient &# 39 ; s back . previous attempts to detect oli by comparing the amplitude of the recorded sounds in right and left sides did not result in reliable methods , because each one of the microphones records sounds generated by both lungs . in order to overcome this problem , a convolutive mixture model approach is presented . in the current examples , an ar model that relates the lungs and the microphones is assumed . the ar model was chosen because it is commonly used in applications of speech and audio processing and its computational complexity is relatively simple . in this model , each ventilated lung represents a source . our goal is to detect a situation of which only one lung is ventilated , from the received signals by the sensors . it is assumed that the signals generated by the ventilated lungs are independent . fig1 shows a block diagram of the proposed mimo - ar model , in which x [ n ] represents the sources ( lungs ), and y [ n ] represents the sensor ( microphones ) measurements . let k and l denote the number of sources ( lungs ) and sensors ( microphones ), respectively ( k & lt ; l ). therefore , the vector of source signals , x [ n ], is defined as a k × 1 vector as follows : x [ n ]=[ x 1 [ n ] x 2 [ n ] . . . x k [ n ]] t . ( 1 ) y [ n ]=[ y 1 [ n ] y 2 [ n ] • • • y l [ n ]] t ( 2 ) the relation between the source signals and the measurements is assumed to be given by a mimo - ar model : where y ( m ) [ n ] is an ml × 1 vector defined as follows : y ( m ) [ n ]=[ y 1 ( m ) t [ n ] y 2 ( m ) t [ n ] • • •] y l ( m ) t [ n ]] t ( 4 ) and y i ( m ) [ n ] is an m × 1 vector which contains the past values of the i - th sensor , y i [ n ], up to sample m : y i ( m ) [ n ]=[ y i [ n − 1 ] y i [ n − 2 ] • • • y i [ n − m ]] t . ( 5 ) where a ij is an m × 1 vector , which relates the samples of the i - th sensor , y i [ n ], with the past values of the j - th sensor , y j [ n − 1 ], . . . , y j [ n − m ]. c is an l × k matrix whose i , j - th element relates the samples of source j and sensor i . finally , e [ n ] is an l × 1 vector representing additive white noise . it is assumed that the noise and source signals are independent , zero - mean , gaussian with covariance matrices σ 2 i and i , respectively . the last assumption can be employed with no loss of generality , because the covariance of the sources is determined by the matrix c , as it can clearly be seen from ( 3 ). as a result , it is obtained that the conditional distribution of y [ n ]| y ( m ) [ n ] is gaussian : y [ n ]| y ( m ) [ n ]˜ n ( ay ( m ) [ n ], r ), where r is defined as : it is noted that the unknown parameters : a , r , m and k must be estimated from a set of n measurements , y [ 1 ], . . . , y [ n ]. it is also assumed that all the initial conditions are zero , i . e . e [ n ], x [ n ]= 0 for n & lt ; 0 , and that the input and noise signals are stationary . in fact , successful estimation of k , the number of sources ( lungs ), is the key for the oli detection . in order to determine the number of sources , k , we need first to estimate the unknown matrices , a and r , from the n samples of the data : y [ 1 ], . . . , y [ n ]. for this purpose , the maximum - likelihood ( ml ) estimator is used . the ml estimator of the matrices a and r , is obtained by maximizing the logarithm of the conditional probability density function ( pdf ) of the output samples given the unknown matrices , which is : the log - likelihood function can be maximized by equating its derivatives with respect to a and r , and solving the two resulting matrix equations . this process yields ( proof : see appendix a ): the use of model order selection methods based on information theoretic criteria [ 11 ]-[ 14 ] seems to be the natural method in order to estimate the model order , m , and the number of sources , k . this method was developed and tested during our work , but did not show a reliable result when applied to real breathing sound signals . therefore , a generalized likelihood ratio test based method was developed and tested as shown in the next section . in the private case of lungs as sources , the number of sources can be only one or two . therefore , for the purpose of decision of between tri case and oli case , the glrt is used [ 15 ]. this test is based on the ratio between the probability density function under each hypothesis , while the maximum likelihood estimator is used to estimate the unknown parameters under each hypothesis . let us denote the following hypothesis : h 1 : only one source exists for the system ( oli case , k = 1 ) h 2 : there are two sources for the system ( tri case , k = 2 ) the development of the log - likelihood function under the i - th hypothesis , leads to the following expression ( assuming the noise variance , σ 2 , is known ): where { l i } i = 1 2 are the two highest eigenvalues of { circumflex over ( r )} ( l 1 ≧ l 2 ), and l is the number of sensors ( proof : see appendix b ). as a result , the glrt for decision between h 1 and h 2 is as follows : as can clearly be seen from ( 11 ), the second highest eigenvalues of { circumflex over ( r )} is actually the detector of oli situation , under the assumption that σ 2 is known , and that the sources are point sources . simulation results given in the next section show the performance of the proposed detector under coherent and incoherent distributed sources assumption , while the last assumption is a more accurate model for lungs sources . in order to evaluate the performance of the estimators as a function of the parameters of the model , simulations with synthetic data were performed . the mimo - ar system as defined in ( 3 ), was simulated and the ml estimators of r and a were calculated according to ( 9 ). in the simulations the parameters of the system were as follows ( unless otherwise is indicated ): the number of sources was k = 2 , the number of sensors was l = 4 with ar order , m = 5 and noise variance , σ 2 = 1 . the matrix c was chosen to be : and the matrix a was chosen to be stable representing system poles inside the unit circle . simulation results of the ml estimators of the matrices a and r , can be found in . the behavior of the glrt as a function of the number of independent samples , n , is examined . the second highest eigenvalue , l 2 , was extracted and compared to a threshold value of the noise level , σ 2 . a total number of j = 1000 iterations for each n were performed , and the probability of error of k is defined as : fig2 shows the probability of error , p e , as function of number of samples , n . it can be seen that probability of error decrease as the number of samples grows . this fact justifies the use of a threshold value of the noise level when the sources are coherent . it is well known that each lung is composed of several independent point sources , and therefore should be treated as distributed sources . in order to evaluate the performance of the algorithm under this assumption , spatially distributed sources were synthesized . in this simulation , two distributed sources were synthesized . each distributed source was composed of four independent point sources with close spatial signatures . therefore , the columns of c were chosen to be : where c 1 and c 2 are orthogonal , and { c i δc i1 δc i2 δc i3 } is an orthonormal group . ε is a constant which determines the distribution width . the vector of sources x [ n ] represents eight independent sources . therefore , the product cx [ n ] represents two distributed sources , where each one is constructed by 4 independent sources . in order to examine the performance of the glrt under incoherent distributed sources assumption , a comparison between the eigenvalues of { circumflex over ( r )} for cases of one and two distributed sources is given . in fig3 the eigenvalues of { circumflex over ( r )} are drawn as function of ε for k = 1 and k = 2 . it can be seen from fig3 that even when the sources are widely distributed , the eigenvalues of the source signals sub - space are separated from the eigenvalues of the noise sub - space . therefore , despite the fact that the lungs do not function as a coherent sources model , it was decided to examine the second highest eigenvalue of { circumflex over ( r )} as a detector for oli situation in real breathing sound signals . in addition , it can be also be seen the threshold value in the case where the sources are widely distributed should be higher than the noise level , σ 2 = 1 . in order to examine the disclosed model for oli detection , a database of recorded breathings was established . the database was composed of 24 patients which were recorded in a surgery room in both situations : during correct ventilation , when the tip of the tube is placed above the carina , and during a situation of oli when the tip of the tube is under the carina and only one lung is ventilated . during each experiment , the microphones were attached to the patient &# 39 ; s back , as shown in fig4 , recorded the breathing sounds of the patients in both situations . the ventilations were performed manually and not mechanically , in order to achieve higher signal - to - noise ratio in the recorded sounds , and the real position of the tube was validated each time by fiber - optic . the experiments were performed in the main surgery room of medical center soroka — israel , during the anesthesia part in the beginning of the surgeries . in order to attenuate some of the irregular background noises outside of the spectral range for breathing signals , such as monitor beeps , doctors &# 39 ; discussions , etc . the recorded signals were band - pass filtered by a butterworth filter with a bandwidth of 100 hz - 600 hz . the recorded sounds were sampled at 4 khz . because of the cut - off frequency of 600 hz , down - sampling operation with a factor of 0 . 3 was performed . the signal amplitude from each microphone depends on the particular location of the microphone on patient &# 39 ; s body , the anatomy of the particular patient , and on the gain of the sampling system . therefore , it turned out that each channel &# 39 ; s output had a different signal amplitude , and the noise variance , σ 2 , also differed between microphones . in order to treat this problem , a normalization of each channel according to the noise level on the channel was done . it is also noted that the aforementioned techniques were only sufficient to reduce some ambient noise associated with an operating room , and the algorithm itself was robust enough to determine a oli situation in a manner that was insensitive to irregular noise of an operating room . the recorded breathing signals contain both situations of oli and tri . the breathing signals were limited to cut - off frequency of 4 khz , and the data were divided into windows of 2000 samples each , with 80 % overlap . because of fact that aic and mdl have chosen the highest available ar model order when applied to real data , an arbitrary ar order of 15 was set considering the computation complexity and the available processing time . the unknown matrices a and r were estimated for each window , using the ml estimator developed in section ii . fig5 shows a few breathing cycles of both oli and tri situations , recorded by the four microphones after pre - processing . as it can be seen from this figure , determination between oli and tri cases by only the amplitude of the recorded sounds is not a simple task . fig6 shows the second highest eigenvalue of { circumflex over ( r )} ml as a function of time , as a result of processing the measurements shown in fig5 . as it can clearly be seen from fig6 , oli and tri cases can clearly be discriminated , by the second highest eigenvalue of { circumflex over ( r )} ml in every breathing cycle . the results of the proposed algorithm were consistent over the 24 experiments . estimation of the performance of the system was performed using “ leave some out method ” as follows . twenty different experiments were used to extract the histograms in order to train the system . the rest of the 4 experiments were used to validate the system and were tested according to the extracted statistics in the training process . this process was repeated 6 times , each time a different group of four validation experiment was used . as a result , a validation was performed using a total number of 24 experiments , in a “ patient independent ” mode . there are two types of errors in oli detection : p miss , the probability of a true oli to be wrongly detected as tri , and p fa , the probability of tri to be detected as oli . the detection error tradeoff ( det ) curve is a common mean to display these errors . the det curve provides information about the device &# 39 ; s performance , where each point on the curve shows the p fa and p miss for a given threshold . the threshold of a real monitoring system should be calculated according to the requested sensitivity of the system , while taking into consideration the allowed p miss of the system . fig7 shows the det curve of the proposed decision system , which was computed according to the 6 iterations described above . the equal error rate ( eer ) point is defined as the point on the det curve where p miss = p fa , is 4 . 8 . naturally , more importance should be given to p miss rather than to p fa . therefore , it is assumed that in a practical system the selected activity point on the det curve will be where p miss = 2 % and p fa = 9 %. from the practical point of view , these examples have illustrated methods and apparatus for detection of oli . an algorithm for detection of oli by monitoring lungs sounds was developed . in order to examine the algorithm performance , a database of recorded breathing sound signals of patients during oli and tri situations was established . it has been shown that assuming a mimo - ar model and selecting the second highest eigenvalue of the residual covariance matrix as a feature proves itself as a reliable method for detection of oli on real breathing sound signals . because of the fact that a pre - processing according to the surgery conditions has to be performed , it is disclosed that optional automatic training of the system before every surgery in order to enable it to set the optimal gain for each microphone is advantageous . maximization of ( 8 ) with respect to the unknown parameters , a , r , is achieved via equating the corresponding partial derivatives to zero . only the last term of ( 8 ) which is : is relevant to calculate the derivative of the log - likelihood with respect to a . the derivative of a scalar a with respect to a matrix b is defined as : if a is a square matrix and f ( a ) is a scalar function , then : in order to find the ml estimator of a and r , the above derivatives should be equated to zero . since r is a covariance matrix then , r = r t and r − 1 = r − t . therefore we obtain two matrix equations with two unknown matrix variables , a and r − 1 : is invertible , leads to ( 9a ). substituting ( 9a ) into the last term of ( 23 ), and extraction of r leads to ( 9b ). substituting â and { circumflex over ( r )} into ( 8 ) leads to ( 24 ): the sum term in ( 24 ) is a scalar , and therefore the trace operation can be performed on it : the determinant of matrix is the product of its eigenvalues . therefore , recalling { l i } i = l l the eigenvalues of { circumflex over ( r )} in descending order ( l 1 ≧ l 2 ≧ • • • ≧ l l ), |{ circumflex over ( r )}| can be simplified into : as appears in [ 16 ], the smallest l - 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