the present invention discloses a method for automated screening congenital heart diseases in children through heart sound analysis techniques . the method relies on categorizing the pathological murmurs based on the heart sections initiating the murmurs . these pathological murmur categories can be identified by examining the heart sound energy over specific frequency bands , which will be referred hereafter as arash - bands . depending on the type of heart defect , murmurs may appear in different segments of the heart sound . however , most frequently observed diseases cause some kind of murmur in the systolic segments . therefore , it is preferred to use the systolic segments of the heart sound for diagnosis . for determining the arash - bands for each category , the energy of the heart sound is evaluated over all possible frequency bands . the arash - band is then defined as the band that provides the lowest clustering error as compared to the normal heart sound . the energy content of the arash - bands then constitutes a feature vector suitable for classification using a neural network . the approach of the present invention in determining the arash - band for each disease is such that the energy contents of the heart sound over the arash - band for a disease provides the maximal distinction between normal and the abnormal cases . a multi layer perceptron ( mlp ) neural network is advantageously to classify the feature vectors constructed from the arash - band energies to normal and abnormal cases . a multilayer perceptron is a feedforward artificial neural network model that maps sets of input data onto a set of appropriate output . it is a modification of the standard linear perceptron in that it uses three or more layers of neurons ( nodes ) with nonlinear activation functions , and is more powerful than the perceptron , because it can advantageously distinguish data that is not linearly separable , or separable by a hyperplane . accurate specification of the arash bands , through the minimization of the feature vector dimension , makes the classifier more robust against possible false diagnosis ( positive or negative ) for such cases . the proposed screening method relies on classification of congenital heart diseases based on the heart parts that cause the pathological murmurs , rather than standard medical classifications . it was found that the heart sound of children with congenital heart diseases shows energy concentrations over specific frequency bands that are different from those of the normal children . these frequency bands are called arash - bands . the spectral energies of the arash - bands can thus be used as a feature vector to screen congenital heart diseases in children . these arash - bands depend on the heart sections causing the murmurs . some of the congenital heart diseases make murmurs which are not corresponding to the defective heart sections . there are also congenital heart diseases where the murmurs are produced by the same heart sections ( e . g ., asd and ps diseases ). the murmurs in asd and ps diseases are caused by the overflow of the pulmonary valve . as will be shown , the arash - bands for these two diseases advantageously coincide , which confirms the link between arash - bands and the heart sections that initiate murmurs . the final disease screening is performed by a neural network that acts upon a feature vector constructed from the energy contents of the arash - bands for all the diseases of interest . the arash - band for a specific congenital heart disease is the frequency band that provides the maximal distinction between the spectral energy of the normal and abnormal heart - sounds . to determine the arash - band , the power spectral density function ( psd ) is used . the analysis of the spectral energy distribution , for all available phonocardiogram , indicates that heart sound signal has a negligible energy for frequencies above 500 hz ( fmax ). therefore , it is preferred to limit the sound analysis to about 500 hz . in a first step , spectral energy over all possible frequency bands is calculated for all the cases in the training data - bank . with indices k = 1 , . . . , n ( n is the total number of sub - bands ) assigned to all possible sub - bands below the fmax , the energy of the sub - band k between the two arbitrary frequencies f 1 and f 2 is computed as : if f 1 = 0 and the frequency f 2 is increased by 1 hz steps ( f 2 = 1 , . . . , fmax ), all possible sub - band energies starting with f 1 = 0 are calculated as follow : in this case k = 1 , . . . , fmax and the total number of all possible sub - bands is fmax . if f 1 = 1 is assumed , then f 2 = 2 , . . . , fmax and sub - band energies are : in this case k =( fmax + 1 ), . . . , ( 2 * fmax − 1 ) which begins right after the previous case ( k = fmax ) and the total number of all possible frequency bands is ( fmax − 1 ). as the f 1 increases by one , the total number of the k decreases by one . the sub - band indices k , for each case of f 1 , should not overlap with the previous case . if f 1 = 0 , . . . , ( fmax − 1 ), computing all sub - band energies for each f 1 determines all possible sub - band energies below the frequency fmax . where k covers all possible sub - bands below the frequency fmax . the total number of sub - bands is the arash - band for a congenital heart disease is the frequency band with maximum spectral energy differences between the defected heart sounds and normal ones . the determination of the arash - band then comprises the following steps : define e l , i ( k ) to sub - band energy of a heart sound signal for the subject l in the class i for a specified sub - band k ; assign a discriminating threshold between the two classes for each e l , i ( k ) with respect to k ; determine the mean value and the variance of e l , i ( k ) for the two classes ( i = 1 , 2 ) for finding the thresholds : where μ i ( k ) represents mean value of a sub - band energy k , for normal ( i = 1 ) and a congenital heart disease ( i = 2 ), represents variance value of a sub - band energy k , for normal ( i = 1 ), and a congenital heart disease ( i = 2 ), n denotes the number of all possible sub - band , ni represents the number of cases in the data - bank . optionally , the two classes ( normal / abnormal ) can be considered as one and are later on clustered with a selected thresholds , in order to extract the bands with maximum energy difference between the two classes . the determination of the discriminating threshold for sub - band k , is calculated by : d ( k ), has a value with tendency toward the group with smaller variance . the error er ( k ), between the clusters for each sub - band k are : among the selected bands with the same minimum errors , the optimum one is selected by using classic scattering formula for each selected frequency band k , as : the optimum frequency band is then the band which results in the largest s ( k ). instead of using s ( k ) parameter , one can take advantage of statistical distribution of frequency band energies with the least errors . the determined arash - bands also provides the means for implementation of a smart stethoscope , which could be used for automated screening of patients such as children for congenital heart diseases , possibly via means using internet or telemedicine . as it is shown in table 1 , six diseases named vsd , asd , pda , tof , ps and as constitute more than 75 % of all congenital heart diseases . these congenital heart diseases were therefore chosen as case - studies to demonstrate how the person skilled in the art can use the method of the invention . the method for a determination of frequency band was applied on 40 subjects ( human patients ) presenting normal and abnormal signals in training phase . then the heart disease detection method was used with 50 subjects ( human patients ) presenting normal and abnormal cases out of training signals . the result of more than 94 % correct classification shows that the method of the invention is efficient in screening of patients , especially children with congenital heart diseases . in order to have a better prediction of the invention method performance with a larger number of data , the jack - knife validation method was applied on 90 available subjects ( phonocardiograms ) and achieved 100 % of accuracy . pcg and ecg signals from ninety children were collected . forty of these signals were used as training data - bank , and the remaining fifty signals were used as test data - bank . from the forty training signals , twenty - four are from children with vsd , asd , pda , tof , ps and as diseases ( four signals per each disease ). the remaining sixteen training signals are from children with normal pcg or with innocent murmurs . from the fifty test signals , thirty are from children with heart diseases ( five signal per disease ), and the remaining twenty signals are normal pcg . the signals in this data - bank were collected under the supervision of pediatric cardiologists , and using echocardiography and complementary tests courtesy of teheran children heart center . all pcg and ecg signals were recorded over a time - interval of ten seconds . a welchallyn meditron stethoscope and ecg were used . for data acquisition , a 1 . 8 ghz acer notebook with a 16 bit stereo soundcard and sampling rate 44 . 1 khz was used . applying the procedure on each congenital heart disease group with respect to the normal group , six optimized frequency bands were obtained . the arash - bands ( in hz ) determined by the method previously described , with the available set of data for each congenital heart disease are : as it is observed the two bands ( wasd , wps ) are almost overlapped . as it was mentioned in section 2 , the murmurs for the asd and the ps are originated by the same heart section . a neural network classifier have been used for distinguishing between children with heart defects and the normal children . since the two frequency bands , wasd and wps have a great overlap with each other , the two frequency bands as wasdps = 16 - 33 were combined . the spectral energies of 5 arash - bands ; wvsd , wtof , wpda , was and wasdps serve as the feature vector for the heart sound classification . a three layer mlp neural network was used to screen phonocardiograms from children with congenital heart disease . the neural network has 5 , 10 and 1 neurons in its input , mid and output layers respectively with tangent sigmoid transfer function . the neural network has been trained with back propagation error method . the training data - bank , as detailed in section 3 , was used to train the algorithm including arash - band calculations and neural network learning . to evaluate the performance the method of the present invention , the test data - bank , which was detailed in section 3 was used . the screening results were then compared against those performed by pediatric cardiologists who used echocardiography in conjunction with other complementary tests as the golden standards for their diagnosis . false negative error ( f . n . ): normal diagnosis for abnormal cases over the total number of abnormal cases ; false positive error ( f . p . ): abnormal diagnosis for normal cases over the total number of normal cases ; efficiency : total false diagnosis over the total number of cases . table 3 shows the result of our method according to the above criteria : as can be seen from table 3 , the efficiency of the method with the given set of data is more than ninety - four percent , which is significant . the arash - band for a congenital heart disease is a frequency band whose energy content provides the maximal distinction between the normal and abnormal cases . to demonstrate this , the inventors have calculated the energy of arash - bands , along with those of two other arbitrary bands , for the four instances of each of the six diseases in the training data base and plotted them against the energy contents for the normal cases ( fig8 ). it is easily seen from fig8 that the arash - band , as compared to the other two bands , provides a much better basis for distinction between the normal and abnormal cases . in order to have a better validation of the output , the jack - knife method was used . in this method , a data is taken off the data base and the rest is used to train the neural network . the excluded data is then used to evaluate ( and validate ) the output . this is repeated until all data in the data base is validated . fig9 shows the output of the proposed method for the data - bank , using the jack - knife method . as fig9 shows the false negative and false positive errors are both zero according to this validation . therefore , the arash - bands energies of the four diseases constitute suitable feature vectors for screening of congenital heart diseases in children through a mlp neural network . the determined arash - bands is also usefull in distinguishing pathological murmurs from innocent ones .
the present invention is related to a method for the determination of frequency band characteristic of a heart disease comprising the steps of : recording a first set of phonocardiograms from a first set of reference healthy patients , and a second set of phonocardiograms from a second set of patients suffering of said heart disease ; calculating spectral energies of all possible frequency bands ; comparing said spectral energies of all possible frequency bands for determining an optimized frequency band giving rise to the maximal distinction between spectral energies of the phonocardiograms from first and second set of phonocardiograms .