System and method for acoustic detection of coronary artery disease

Abstract

A system and method for acoustic detection of coronary artery disease (CAD) are provided. The system includes a transducer for acoustically detecting heart signals of a patient and a computer system which executes detection software for processing the detected heart signals to identify the presence of CAD from the heart signals. The software allows for the automatic definition of a diastolic “window” of the acoustic signal for analysis, and automatically edits the sampled acoustic signal to eliminate unwanted artifacts and/or noise in the acoustic signal. The edited signal is then processed by a plurality of signal processing algorithms, including spectral analysis algorithms, time-frequency algorithms, global feature algorithms, kurtosis algorithms, mutual information algorithms, negenthropy algorithms, and principal component analysis algorithms, to generate a disease vector. The disease vector is then classified to determine whether CAD is present in the patient. Classification can be accomplished using linear discriminant analysis or a support vector machine.

1 . A system for acoustic detection of coronary artery disease, comprising:
 a transducer for detecting acoustic heart signals;   a computer connected to the transducer, the computer executing software for detecting coronary artery disease by processing the detected acoustic heart signals, the software including:
 a first software module for identifying a diastolic segment of a detected acoustic heart signal for analysis; 
 a second software module for editing the diastolic segment to produce an edited signal; 
 a plurality of signal detection algorithms for processing the edited signal to produce a disease vector; and 
 a classifier for classifying the disease vector; and 
   means for indicating the presence or absence of coronary artery disease in a patient based upon an output of the classifier.   
   
   
       2 . The system of  claim 1 , wherein the plurality of signal detection algorithms generate a plurality of feature parameters and the classifier processes the plurality of feature parameters to determine the presence or absence of coronary artery disease in a patient. 
   
   
       3 . The system of  claim 2 , wherein the plurality of detection algorithms comprises a spectral analysis algorithm applied to the edited signal. 
   
   
       4 . The system of  claim 3 , wherein the plurality of detection algorithms comprises a time-frequency detection algorithm applied to the edited signal. 
   
   
       5 . The system of  claim 4 , wherein the plurality of detection algorithms comprises a global feature detection algorithm applied to the edited signal. 
   
   
       6 . The system of  claim 5 , wherein the plurality of detection algorithms comprises a kurtosis algorithm to the edited signal. 
   
   
       7 . The system of  claim 6 , wherein the plurality of detection algorithms comprises a mutual information processing algorithm applied to the edited signal. 
   
   
       8 . The system of  claim 7 , wherein the plurality of detection algorithms comprises a negentropy algorithm applied to the edited signal. 
   
   
       9 . The system of  claim 8 , wherein the plurality of detection algorithms comprises a principal component analysis algorithm applied to the edited signal. 
   
   
       10 . The system of  claim 1 , wherein the classifier comprises a linear discriminant analysis algorithm applied to the disease vector to determine the presence or absence of coronary artery disease. 
   
   
       11 . The system of  claim 1 , wherein the classifier comprises a support vector machine applied to the disease vector to determine the presence or absence of coronary artery disease. 
   
   
       12 . The system of  claim 1 , wherein the classifier comprises an adaptive neural network applied to the disease vector to determine the presence or absence of coronary artery disease. 
   
   
       13 . A method for acoustic detection of coronary artery disease, comprising the steps of
 detecting an acoustic heart signal;   defining a diastolic segment of the acoustic heart signal for analysis;   editing the diastolic segment to produce an edited signal;   processing the edited signal with a plurality of signal detection algorithms to produce a disease vector; and   classifying the disease vector to determine the presence or absence of coronary artery disease in a patient.   
   
   
       14 . The method of  claim 13 , wherein the step of processing the edited signal comprises applying a spectral analysis algorithm to the edited signal. 
   
   
       15 . The method of  claim 14 , wherein the step of processing the edited signal comprises applying a time-frequency detection algorithm to the edited signal. 
   
   
       16 . The method of  claim 15 , wherein the step of processing the edited signal comprises applying a global feature detection algorithm to the edited signal. 
   
   
       17 . The method of  claim 16 , wherein the step of processing the edited signal comprises applying a kurtosis algorithm to the edited signal. 
   
   
       18 . The method of  claim 17 , wherein the step of processing the edited signal comprises applying a mutual information processing algorithm to the edited signal. 
   
   
       19 . The method of  claim 18 , wherein the step of processing the edited signal comprises applying a negentropy algorithm applied to the edited signal. 
   
   
       20 . The method of  claim 19 , wherein the step of processing the edited signal comprises applying a principal component analysis algorithm applied to the edited signal. 
   
   
       21 . The method of  claim 13 , wherein the step of classifying the disease vector comprises processing the disease vector with a linear discriminant analysis algorithm to determine the presence or absence of coronary artery disease. 
   
   
       22 . The method of  claim 13 , wherein the step of classifying the disease vector comprises processing the disease vector with a support vector machine to determine the presence or absence of coronary artery disease. 
   
   
       23 . A method for reducing noise in an acoustic coronary artery detection system, comprising the steps of:
 acquiring an acoustic heart signal using a first transducer positioned near a heart;   acquiring a reference signal using a second transducer, the reference signal including a noise component;   processing the reference signal with an adaptive filter to produce a processed noise signal; and   removing noise from the acoustic heart signal by subtracting the processed noise signal from the acoustic heart signal.   
   
   
       24 . A method for detecting coronary artery disease, comprising:
 using a transducer to monitor acoustic signals from a patient's heart; and   analyzing nongaussian global feature parameters of the acoustic signals to determine the presence of heart or coronary artery disease.   
   
   
       25 . A method for detecting heart sounds, comprising the steps of:
 (A) placing first and second microphones on the chest of a patient at a position relatively away from the heart, for detecting internal patient and external noise signals;   (B) placing a third microphone on the chest of said patient in close proximity to the heart, for detecting heart sounds;   (C) using algorithms to program a computer to process the noise signals from said first and second microphones to compute a first artifact score;   (D) processing the weights from an adaptive noise cancellation filter utilizing signals from said first and second microphones for filtering said heart sound signals from said third microphone to obtain a second artifact score;   (E) combining the first and second artifact scores to obtain an overall artifact score; and   (F) successively repeating steps (A) through (E) for different placement of the first and second microphones to determine the placement that provides the lowest overall artifact score, for maximizing adaptive cancellation of noise from the heart sounds picked up by the third microphone.