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.

Description

RELATED APPLICATIONS[0001]The present application claims the benefit of U.S. Provisional Application Ser. No. 60 / 846,643, filed Sep. 22, 2006, and U.S. Provisional Application Ser. No. 60 / 846,573, filed Sep. 22, 2006, the entire disclosures of which are both expressly incorporated herein by reference.STATEMENT OF GOVERNMENT INTERESTS[0002]The present invention was made with support of the U.S. Government under NIH Contract Grant No. NIH-NIHLB, Grant Identification No. 1 R41 HL079672. Accordingly, the U.S. Government may have certain rights to the present invention.BACKGROUND OF THE INVENTION[0003]1. Field of the Invention[0004]The present invention relates to medical diagnostic systems, and more particular, to a system and method for acoustic detection of coronary artery disease.[0005]2. Related Art[0006]Coronary artery disease (CAD) is a major cause of death in industrialized nations, and approximately 13 million people in the United States are estimated to have the disease. CAD is...

AI Technical Summary

Benefits of technology

[0010]The present invention relates to a system and method for acoustic detection of coronary artery disease (CAD). The invention comprises a transducer for acoustically detecting heart signals of a patient, an amplifier for amplifying the detected heart signals, and a computer system which executes detection software for processing the detected heart signals using a plurality of signal detection algorithms which analyze a plurality of feature parameters of the acoustic signal, detecting the presence of CAD from the heart signals, and indicating the presence of CAD. The software provides for automatic detection of a diastolic “window” of the acoustic signal for analysis, and includes automated editing of 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, negentropy 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 (SVM).

Problems solved by technology

Over time, the plaque deposits narrow the arteries and deprive the heart of oxygen.

This can cause blood clots, and in some instances, can completely block arteries, causing blood flow to the heart to stop.

Reduced blood flow reduces the oxygen supply to the heart muscles, which can cause chest pain (angina), heart attack, heart failure, or arrhythmias.

Often, sudden death results.

To date, the only definitive test for CAD is coronary angiography, a procedure which is invasive, expensive, requires hospitalization, and carries health risks.

Newer technologies, such as electron beam Computer Tomography (ebCT), expose the patient to significant health risks from radiation and / or dye contrast agents, and require major capital investments and specialized operational staff.

Older technologies, such as stress electrocardiology (ECG), expose the patient to moderate risk, remain labor intensive, are still fairly expensive, and have uncomfortably low specificity and sensitivity, especially for women.

Unfortunately, such techniques analyzed only a very limited range of feature parameters associated with the acoustic signal, and often analyze only a limited range of frequencies of the acoustic signal.

Moreover, the presence of noise in the acoustic signal can significantly adversely affect the ability of existing techniques to accurately diagnose CAD in a patient.

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