Walter Welkowitz

Noninvasive acoustical detection of coronary artery disease: a comparative study of signal processing methods

Previous studies have indicated that, during diastole, the sounds associated with turbulent blood flow through partially occluded coronary arteries should be detectable. To detect such sounds, recordings of diastolic heart sound segments were analyzed using four signal processing techniques: the fast Fourier transform (FFT) autoregressive (AR), autoregressive moving-average (ARMA), and minimum-norm (eigenvector) methods. To further enhance the diastolic heart sounds and reduce background noise, an adaptive filter was used as a preprocessor. The power ratios of the FFT method and the poles of the AR, ARMA, and eigenvector methods were used to diagnose patients as having diseased or normal arteries using a blind protocol without prior knowledge of the actual disease states of the patients to guard against human bias. Of 80 cases, results showed that normal and abnormal records were correctly distinguished in 56 using the fast Fourier transform (FFT), in 63 using the AR, in 62 using the ARMA method, and in 67 using the eigenvector method. These results confirm that high-frequency acoustic energy between 300 and 800 Hz is associated with coronary stenosis.<<ETX>>

System and method for noninvasive detection of arterial stenosis

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 patients 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.

Coronary Artery Disease - Correlates Between Diastolic Auditory Characteristics and Coronary Artery Stenoses

A noninvasive approach to detecting coronary artery disease analyzes thoracic sounds to isolate acoustical correlates of stenosed coronary arteries. The analysis includes time windowing, frequency (power spectra) windowing, and averaging of thoracic sounds from normal and diseased patients. Initial results indicate that an above normal percentage of high-frequency (180-300 Hz) energy is closely associated with narrowed coronary arteries.

Noninvasive detection of coronary stenoses before and after angioplasty using eigenvector methods

Previous studies suggest that partially occluded coronary arteries may generate sounds due to turbulent blood flow. To support these findings, the frequency spectra of diastolic heart sounds are compared before and after angioplastic surgery. Since the low-level sounds associated with partially occluded coronary arteries are contaminated with considerable background noise, traditional FFT analysis may not produce accurate frequency spectra. In a previous study using the same data, no significant differences were found in the diastolic heart sounds before and after angioplastic surgery. In this study, three eigenvector methods (Pisarenko, MUSIC, and Minimum-Norm) have been selected to generate the frequency spectra because of their higher resolution, particularly in the presence of noise. Although the Pisarenko method produced spurious zeros and could not be used, the other two methods produced spectra showing, in most cases, a marked decrease in high-frequency spectral components following angioplasty.<<ETX>>

Detection of coronary occlusions using autoregressive modeling of diastolic heart sounds

Recordings of diastolic heart sound segments were modeled by autoregressive (AR) methods including the adaptive recursive least-squares lattice (RLSL) and the gradient lattice predictor (GAL). Application of the Akaike criterion demonstrated that between 5 and 15 AR coefficients are required to describe a diastolic segment completely. The reflection coefficients, prediction coefficients, zeros of the polynomial of the inverse filter, and AR spectrum were determined over a number (N=20-30) of diastolic segments. Preliminary results indicate that the averaged AR spectrum and the zeros of the inverse filter polynomial can be used to distinguish between normal patients and those with coronary artery disease.<<ETX>>

Application of adaptive filters to noninvasive acoustical detection of coronary occlusions before and after angioplasty

Isolated diastolic heart sounds taken from recordings made at the patients bedside were modeled using the autoregressive (AR) and autoregressive moving average (ARMA) methods after adaptive line enhancement (ALE). Decisions were made in a blind fashion without prior knowledge of whether a given recording was made before or after angioplasty. Resulting model frequency spectra showed greater high frequency components (between 400 and 800 Hz) in preangioplasty patients, and a consistent shift in amplitude of the second pole pairs of the AR and ARMA methods with surgery. Blind assessment based on frequency spectra and poles, correctly classified the diastolic recordings in 18 of 20 cases. These results provide evidence supporting the hypothesis that coronary stenoses produce detectable sounds during diastole.<<ETX>>

Noninvasive detection of coronary artery disease using parametric spectral analysis methods

The detection of coronary artery disease by noninvasive analysis of isolated diastolic heart sounds is considered. It is based on identifying features associated with turbulent blood flow in partially occluded coronary arteries. The application of two types of parametric spectral analysis-autoregressive methods and eigenvector methods-to identify the additional signal components is discussed. Results obtained with one eigenvector method, (the MUSIC method) for spectra obtained from an angioplasty patient and results obtained with the autoregressive model in a comparison study of ten diseased and five normal patients are presented and discussed.<<ETX>>

Application of the ARMA method to acoustic detection of coronary artery disease

To further explore the application of advanced signal processing techniques to the noninvasive detection of coronary artery disease, 30 patients (10 angioplasty and 20 normal or abnormal) were tested using autoregressive moving average (ARMA) modelling of the disastolic heart sound data. It is during diastole that coronary blood flow is maximum and sounds associated with turbulent blood flow through partially occluded coronary arteries would be loudest. Model parameters (the power spectral density (PSD) functions and the poles of the ARMA method) were used to separate the normal patients from the abnormal patients in the normal/ abnormal study, or to decide whether the recordings were made before or after angioplasty in the angioplasty study. The decisions were made ‘blind’, without knowledge of the actual disease states of the patients for the normal/abnormal study and without prior knowledge of whether a given recording was made before or after angioplasty for the angioplasty study. Results from the angioplasty and the normal/abnormal studies showed that pre- and post-angioplasty records were correctly distinguished in 8 out of 10 cases, and normal and abnormal records were correctly distinguished in 17 of 20 cases. These results also confirmed that high frequency energy above 400 Hz is probably associated with coronary stenosis.

Accelerometer type cardiac transducer for detection of low-level heart sounds

The authors compare several different cardiac transducers and describe the development of a low-mass, accelerometer-type cardiac transducer which fulfils these objectives. The accelerometer weighs approximately 5 g and has a theoretical sensitivity of 125 mV/g in the frequency range 200-800 Hz. The basic design allows for easy modification of sensitivity and resonant frequency. This transducer has been effective in detecting sound associated with turbulent blood flow in partially occluded coronary arteries.<<ETX>>

Incremental network analogue model of the coronary artery

From Newtons equation and the continuity equation, an equivalent analogue circuit model can be derived for each small segment of the coronary arteries. Sapoznikov divided the coronary artery tree into 116 segments. By replacing each segment with its analogue circuit model, a final incremental network model was derived. The model was tested using typical physical parameters under normal conditions, as well as in the presence of coronary artery stenosis. In the case of stenosis, the arteriolar flow with and without autoregulation were compared. The model shows good agreement with the reported effects of stenoses and heart rate on coronary blood flow.