Rutgers, the State University of New Jersey

University of Medicine and Dentistry of New Jersey

Walter Welkowitz

John B Kostis

Steven P Petrucelli

John Leonard Semmlow

Stephen A Orbine

Metin Akay

Alan M Smith

Lisa K Liss

Yun Qi

Qing Cui

Oing Cui

Yasemin M Akay

Vasant Padmanabhan

Apparatus and methods for noninvasively measuring cardiovascular system parameters. According to a first preferred embodiment, the apparatus generates a time varying electrical voltage waveform having voltages corresponding to systolic and diastolic arterial pressures of the subject, the parameters being modelled by a lumped element electric circuit model analogous to the living subject's cardiovascular system, said circuit model including a systolic capacitor analogous to arterial compliance during systole, said apparatus comprising means for computing the value of said systolic capacitor from a measurement of an elapsed time between two voltage levels within a portion of said voltage waveform corresponding to systole, according to a predetermined criteria; and means for computing at least one said cardiovascular system parameter from said capacitor value. According to a second embodiment, a time-varying systolic arterial compliance is measured by measuring an arterial pulse pressure waveform. A third embodiment discloses a method of measuring cardiac output noninvasively from a cuff measurement of a patient's systolic minus diastolic blood pressure and a heart rate measurement. Cardiac output equations are derived from an unmodified Windkessel circuit model which models arterial compliance as a single lumped capacitance. General formulas for the value of the lumped capacitance are provided which are used to more accurately compute cardiac output from this circuit model.

Apparatus and methods for the noninvasive measurment of cardiovascular

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

Apparatus and methods for the noninvasive measurement of cardiovascular

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 high 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.

Noninvasive diagnostic system for coronary artery disease

Apparatus for noninvasively monitoring cardiovascular system parameters of a living subject comprises means for sensing waveforms externally of the body of the subject above the carotid and femoral arteries, means for non-invasively calibrating and digitizing the pulse waveforms, and a digital signal processor having means for converting the digitized, calibrated pulse information by Fast Fourier Transform to first and second sets of harmonically related blood pressure components in the frequency domain, means for comparing corresponding ones of the components in the first and second harmonically related sets to determine amplitude and phase transfer function components of the portion of the cardiovascular system between the carotid and femoral arteries, means for simulating the portion of the system by a hybrid electrical circuit model having at least three variable parameters, means for determining corresponding amplitude and phase transfer function components of the hybrid model and for adjusting the variable parameters so as to substantially match the transfer functions of the model and the portion of the cardiovascular system, and means for determining cardiac output utilizing the adjusted parameters of the hybrid model and the calibrated pulse information.

Methods and apparatus for nonivasive monitoring of dynamic cardiac

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.

A method and apparatus for monitoring the cardiac output of living subjects. Carotid pulse waveforms and femoral pulse waveforms are measured and converted to digitized signals. The carotid pulse signal or waveform is applied as a voltage to the simulated aorta circuit and the circuit component values varied to develop a waveform output best matching the femoral pulse electrical waveform.

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Metuchen, NJ, United States of America

Walter Welkowitz