Rutgers, the State University of New Jersey

Metin Akay

Walter Welkowitz

John B Kostis

John Leonard Semmlow

Yasemin M Akay

A system and method for noninvasively detecting coronary artery disease. The system and method utilize a vasodilator drug to increase the signal-to-noise ratio of an acoustic signal that represents diastolic heart sounds of a patient. A wavelet transform is performed on the acoustic signal to provide parameters for a feature vector. Scaled clinical examination parameters such as a patient's sex, age, body weight, smoking condition, blood pressure, and family history are also included in the feature vector. The feature vector is used as an input pattern to neural networks. The output of the neural networks represent a diagnosis of coronary stenosis in a patient.

System and method for noninvasive detection of arterial stenosis

A method and system for non-invasively detecting Coronary Artery Disease. The method comprises analyzing the diastolic heart sounds detected from a patient's chest cavity during the diastolic portion of the heart cycle in order to identify a low level auditory component associated with turbulent blood flow in partially occluded coronary arteries. These diastolic heart sounds are modeled using advanced signal processing techniques such as Autoregressive (AR), Autoregressive Moving Averaging (ARMA) and Eigenvector methods, so that the presence of such an auditory component may be reliably indicated even under high noise conditions. The system includes an acoustic transducer, pulse sensor device, signal processor means and a diagnostic display. Additionally, the system includes a controller for automatically sequencing data collection, analysis and display stages, therefore requiring a minimum of operator interaction. The system further discloses a piezoelectric type low mass accelerometer for use as an acoustic transducer providing higher sensitivity at the high frequencies necessary for detecting coronary artery disease, specifically between 300 to 1200 Hz. In addition, the system discloses a method for automatically identifying and isolating a diastolic segment of a heart sound recording through a 'window' placement technique. A window is placed relative to the second heart sound and various criteria are implemented to evaluate the recording for artifacts, i.e., data due to breath sounds, stomach growls, external ambient noise.

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Piscataway, NJ, United States of America

Metin Akay