Autoregressive spectral analysis of phrenic neurogram during eupnea and gasping.

During hypoxic gasping, the phrenic neurogram (PN) has a steeper rate of rise, an augmented amplitude, and a shorter duration than is seen during eupnea. Because hypoxia reduces neuronal activity, we hypothesized that gasping would be characterized in the frequency domain by enhanced low-frequency power compared with eupnea. Autoregressive (AR) spectral analysis of the PN in chloralose-anesthetized, vagotomized, peripherally chemodenervated cats was performed during eupnea and hypoxic gasping. During eupnea, significant spectral peaks were seen at 41 +/- 2 and 93 +/- 2 (SE) Hz. In all cats, the 41-Hz spectral peak disappeared during hypoxic gasping and was replaced by a high-power, low-frequency peak at 26 +/- 1 Hz. No consistent change in the frequency or power of the high-frequency spectral peak was seen during gasping. To determine whether changes in the AR spectrum of the PN during gasping result from augmented respiratory output, we compared the AR spectra of the PN during gasping, hypercapnia (end-tidal CO2 fraction = 0.09), and carotid sinus nerve stimulation. Unlike during gasping, there was no shift in power toward lower frequencies during hypercapnia and carotid sinus nerve stimulation. We conclude that the spectral characteristics of gasping, loss of the medium-frequency peak and the appearance of low-frequency (< 30-Hz) power, are unique to this respiratory pattern.

Investigating the effects of vasodilator drugs on the turbulent sound caused by femoral artery stenosis using short-term Fourier and wavelet transform methods.

In this study, the effects of vasodilator drugs on the turbulent sound generation mechanisms during femoral artery stenoses were investigated using the wavelet analysis of the turbulent sounds to characterize these sounds before and after the injection of vasodilator drugs. Results showed that the injection of drugs drastically improved the diagnostic performance of the turbulent sounds in detecting stenoses by increasing the signal-to-noise ratio of the sounds. Results also suggested that the sound above 250 Hz was drastically increased in response to the injection of the vasodilator drug for the partially occluded cases. The turbulence sounds caused by partially occluded femoral arteries are directly related to the slope of baseline of blood flow and to the velocity of the flow. For the 0% occlusion case, initially, sounds were produced with the injection of drugs. However, the sounds totally disappeared when the slope of average blood flow was zero. These results show that the diagnostic performance of diastolic heart sounds associated with occluded arteries can be improved by using vasodilator drugs, which increase the acoustic energy in the first and second wavelet bandwidths due to the turbulence. The short-term Fourier transform (STFT) method was also applied to the same data base. Results using the STFT showed somewhat similar power distributions in that the acoustical power above 250 Hz was increased after the injection of drugs for the occluded cases. However, the WT method provided better time-frequency resolution than the STFT method, showing details of the change in the frequency characteristics with respect to time after the injection of drug.

Dynamics of the sounds caused by partially occluded femoral arteries in dogs.

Previous studies have indicated that partially occluded arteries produce sounds due to turbulence. If these sounds from the coronary arteries could be detected externally, they would provide a simple approach to the detection of coronary artery disease. To confirm the hypothesis that coronary stenosis produces detectable acoustic correlates, sounds caused by a controlled occlusion of the femoral artery of dogs were detected and analyzed using both the fast Fourier transform (FFT) and the autoregressive (AR) methods. The femoral artery was chosen, since its size and flow approximate those of coronary arteries in humans. The poles of the AR spectra and the power ratios of different sections of the FFT and AR spectra were used to differentiate the degree of the stenosis. The results showed that high frequency acoustical power between 200 and 800 Hz is associated with the turbulence produced by the partially occluded femoral arteries of the dogs. Using the AR method, high acoustic power above 200 Hz increased when the degree of the occlusions increased. The poles and power ratios of the AR spectra differed according to the degree of stenosis. However, the high frequency acoustical power above 200 Hz did not increase above the 85% occlusion.

Acoustical detection of coronary occlusions using neural networks.

A nonlinear neural network classifier was applied to noninvasive acoustic detection of coronary artery disease; the classifier included a feature vector, derived from diastolic heart sounds, and a multi-layered network trained by the backpropagation. The feature vector is based on the linear prediction coefficients of the autoregressive method after an adaptive line enhancement method was used as the input pattern to the neural network. One hundred and twelve recordings (70 abnormal, 42 normal) were studied and the network was trained on a randomly chosen set of six abnormal and six normal patients. It was tested on a database consisting of 100 recordings to which it had not been exposed. The network correctly identified 50 of the 64 patients with coronary artery disease and 32 of the 36 patients without any coronary artery occlusions. These results showed that this neural network is capable of distinguishing normal patients from abnormal patients. In addition, the diagnostic capability of this approach is much better than any other available noninvasive approach.

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

Previous studies have indicated heart sounds may contain information useful in the detection of occluded coronary arteries. During diastole, coronary blood flow is maximum, and the sounds associated with turbulent blood flow through partially occluded coronary arteries should be detectable. In order to detect such sounds, recordings of diastolic heart sound segments were analyzed by using four signal processing techniques; the Fast Fourier Transform (FFT), the Autoregressive (AR), the Autoregressive Moving Average (ARMA), and the Minimum-Norm (Eigen-vector) 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 Eigen-vector methods were used to diagnose patients as diseased or normal arteries using a blind protocol without prior knowledge of the actual disease states of the patients to guard against human bias. Results showed that normal and abnormal records were correctly distinguished in 56 of 80 cases using the Fast Fourier Transform (FFT), in 63 of 80 cases using the AR, in 62 of 80 cases using the ARMA method, and in 67 of 80 cases using the Eigenvector method. Among all four methods, the Eigenvector methods showed the best diagnostic performance when compared with the FFT, AR, and ARMA methods. These results confirm that high frequency acoustic energy between 300 and 800 Hz is associated with coronary stenosis.

Noninvasive characterization of the sound pattern caused by coronary artery stenosis using FTF/FAEST zero tracking filters: normal/abnormal study.

In this article, a new approach has been proposed to investigate the extraction of useful information from diastolic heart sounds caused by partially occluded coronary arteries. This method, which estimates and tracks the zeros (poles) of the diastolic heart sounds directly, takes advantage of the FTF/FAEST (Fast Transversal Filters/Fast a Posteriori Error Sequential) technique which possesses the fast convergence property of the Recursive Least Square (RLS) method and the computational simplicity of the Least Mean Square (LMS) method. In previous studies, the main assumption was that the diastolic heart sounds were a stationary process. Since the production of the heart sounds were a stationary process. Since the production of the heart sounds is not a stationary process, a new approach that performs well not only for stationary but also for nonstationary processes can be required. This requirement can be satisfied by the adaptive FTF/FAEST zero tracking method which provides fast and stable convergence as well as computational efficiency since the adaptive FTF/FAEST zero tracking method is based on the exact minimization of least squares criteria and the filter weights of this method are optimal at each time instant. The zero trajectories of the diastolic heart sounds were used to diagnose patients as diseased or normal. Results showed that the normal and abnormal records were incorrectly distinguished in only 6 of 35 cases using a blind protocol where analysis was done without knowledge of the actual disease states of the patients. The most discriminant time region of the zero trajectories of the diastolic heart sounds associated with coronary artery disease was between 200 and 300 msec after the second heart sound during the diastolic period.

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

New applications in phonocardiography require transducers with high sensitivity, low noise, and extended frequency range. This paper compares several different cardiac transducers and describes the development of a low-mass, accelerometer type cardiac transducer which fulfills 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 sounds associated with turbulent blood flow in partially occluded coronary arteries.

Application of adaptive FTF/FAEST zero tracking filters to noninvasive characterization of the sound pattern caused by coronary artery stenosis before and after angioplasty.

This article presents a new signal processing application that can be used for acoustical detection of coronary artery disease before and after angioplasty. The adaptive Autoregressive (AR) method based on the FTF/FAEST (Fast transversal filters/Fast a posteriori error sequential techniques) is used to track acoustical behavior associated with coronary occlusions. Using the amplitude trajectory of the second pole pair of this method, 9 out of 10 angioplasty patients were correctly identified using a blind protocol without prior knowledge of whether a given recording was made before and after angioplasty. These results were obtained from signals located between 200 and 300 msec after the end of the second heart sound during the diastolic period.

Carotid-cardiac interaction: heart rate variability during the unblocking of the carotid artery.

Multiresolution representations of the heart rate variability (HRV) using the wavelet transforms are proposed to characterize the autonomic nervous system regulation of cardio-vascular activity during carotid surgery. Results suggest that the power in all frequency bands was low during the surgery and increased after the declamping of the carotid artery.

Noninvasive acoustical detection of coronary artery disease using the adaptive line enhancer method.

Previous studies have indicated that heart sounds may contain information which is useful in the detection of occluded coronary arteries. Specifically, previous work based on analysing heart sounds recorded during the diastolic portion of the cardiac cycle, when blood flow through the coronary arteries is maximum, has shown that additional frequency components are present in patients with coronary artery disease. To further explore the application of advanced signal processing techniques to the noninvasive detection of coronary artery disease, a new signal-processing approach is presented using adaptive line enhancing (ALE) and spectral estimation of diastolic heart sounds taken from recordings made at the patient's bedside. This approach comprises two cascaded processes. In the first the ALE method is used to enhance the diastolic heart sounds and eliminate background noise. In the second process, either autoregressive (AR) or autoregressive moving average (ARMA) spectral methods are used to estimate the model parameters. Model parameters (the power spectral density (PSD) functions and the poles of the AR or ARMA method) were used to diagnose patients as diseased or normal. Results showed that normal and abnormal recordings were correctly identified in 39 of 43 cases using the new method. These results also confirm that high-frequency energy above 400 Hz is associated with coronary stenosis.

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

Previous studies have indicated that coronary stenoses produce sounds due to the turbulent blood flow in these vessels [1]-[10]. Measurement of these signals forms the basis of our noninvasive approach to the detection of coronary artery disease. It is during diastole that coronary blood flow is maximum and the sounds associated with turbulent blood flow through partially occluded coronary arteries would be loudest [1]-[10]. Isolated diastolic heart sounds taken from recordings made at the patient's bedside were modeled using the autoregressive (AR) and autoregressive moving average (ARMA) methods [4], [7] 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 strong evidence supporting our hypothesis that coronary stenoses produce detectable sounds during diastole [1]-[10].

Noninvasive estimation of cardiac output.

A noninvasive method of measuring cardiac output is described. The method uses adaptive aorta models in conjunction with femoral and carotid pulse contour waveform measurements to calculate aortic flow. Results are presented from measurements on dogs using internal pressure recordings made with fluid-filled catheters and compared with electromagnetic flow measurements taken in the ascending aorta. Preliminary results using external pulse measurements on patients are also presented and compared with thermal dilution measurements.

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 diastolic 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.

Modeling sound generation in stenosed coronary arteries.

Acoustic measurements obtained from sensitive microphones placed on the chest are being used in a procedure to noninvasively diagnose coronary artery disease. Utilizing specially developed signal processing techniques, the spectral content of isolated diastolic heart sounds has been estimated and usually shows an increase in high-frequency components in patients with occlusive coronary arteries. In order to establish a theory for the origin of these spectral features, a sound source model has been developed which combines an incremental network model of the left coronary artery tree with a transfer function model describing arterial chamber resonant characteristics. The network model predicts flow in both normal and stenosed coronary arteries. From this flow information, the arterial chamber transfer function model predicts the development of acoustic signals from the chamber resonant characteristics. The transfer function of a segment of coronary artery demonstrates two resonance frequencies. These resonance frequencies depend upon the length and diameter of the chamber segment, as well as upon the distal hydraulic impedance loading the segment. The lower resonance frequency can be excited by the usual flow fluctuations (low frequency) in the coronary artery. In cases of stenosis, the wideband spectral characteristics of the turbulence produced by the stenosis excites both the low and high resonance frequencies. In a small sample of patients, the spectra obtained from isolated diastolic acoustic signals recorded by a chest microphone agree well with those predicted by this theory.

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

Previous studies done by our group suggest that partially occluded coronary arteries may generate sounds due to turbulent blood flow. To support these previous 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. Indeed, 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.

A novel mechanical cardiac assist device for reversing left ventricular failure.

Diastolic augmentation of aortic pressure is an efficacious means of improving coronary perfusion in heart failure. A novel mechanical cardiac assist device (MCAD), that has advantages over a conventional intraaortic balloon pump and left ventricular assist devices, has been developed. It consists of a high efficiency rotary solenoid, coupled to a pair of actuator plates that clamp on a shunt aortic graft section, and operates in a diastolic counterpulsation mode. The system has been evaluated in six anesthetized, thoracotomized dogs with myocardial ischemia. The MCAD was activated 30-40 min after coronary artery occlusion and synchronized with the R-wave. As illustrated by a representative sample of the data obtained from one of several trials, the preliminary experimental results demonstrated that the MCAD worked effectively to fulfill the primary functions of a counterpulsation assist device, i.e., augmentation of coronary perfusion and reduction in the vascular impedance to ventricular ejection.

Detection of coronary occlusions using autoregressive modeling of diastolic heart sounds.

Previous studies have indicated that diastolic heart sounds may contain information useful in the detection of occluded coronary arteries. In this study, recordings of diastolic heart sound segments were modeled by autoregressive (AR) methods including the adaptive recursive least-square lattice (RLSL) and the gradient lattice predictor (GAL). Application of the Akaike criterion demonstrated that between 5 and 15 AR coefficients are required to completely describe a diastolic segment. The reflection coefficients, prediction coefficients, zeros of the polynomial of the inverse filter, and the 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.

Design and control of the atrio-aortic left ventricular assist device based on O2 consumption.

The left ventricular assist device (LVAD) is used in parallel with the left ventricle to temporarily assist patients with diminished cardiac function for the purpose of minimizing heart workload and to maintain systemic arterial perfusion. The proper adjustment and timing of the pneumatic LVAD is important such that this goal is achieved. Previous investigations into the left ventricular assist device are inconclusive regarding the optimal utilization of this device. This paper documents a protocol for optimal timing of the LVAD. Timing is studied using a closed-loop analog model of the heart, vascular system, and the LVAD. The model is tested for basic representation of the physiological system. The LVAD is incorporated into the model to discover its interaction with cardiac preload and afterload. Heart workload is computed via the pressure-volume-area method. The normal and impaired heart are modeled, in each case the pump control variables are adjusted. A protocol for adjustment of the LVAD is proposed based on reduced heart oxygen consumption. It was found that the pump should begin ejection immediately after the close of the aortic valve and that the pump filling pressure should be set to a value which produces maximum filling of the pump. Although aortic pressure and flow could be improved at pump rates above the heart rate, oxygen utilization of the heart could only be minimized for synchronous pumping. The adjustment of the pump ejection pressure is a tradeoff between d/dt (LVO2) and stroke volume and mean arterial pressure. The LVAD should be designed to minimize inflow and outflow resistance and to maximize pump compliance. The process of weaning the patient from the LVAD is considered. The overall results provide quantitative guidance for the use of the AA-LVAD.

A closed-loop control scheme for intraaortic balloon pumping.

The beneficial hemodynamic effects of IABP are critically dependent on balloon timing relative to the diastolic phase of the cardiac cycle. A microprocessor-based controller has been developed to implement real-time automation of IABP using P-R intervals to regulate balloon deflation and systolic time intervals to trigger balloon inflation in a semi-automatic fashion. Experiments were performed on anesthetized open-chest dogs. Simultaneous measurement of aortic pressure and flow, coronary flow, and left ventricular pressure were recorded. Muscle segment lengths in normal and ischemic border zones were also measured from implanted pairs of endocardial ultrasonic dimension gages. P-waves were obtained from atrial cardiograms, and heart sounds were detected using a special filtering circuit. Both signals were input together with ECG to automate IABP timing. Systolic time intervals were calculated in real-time. IABP efficacy was assessed from changes in aortic flow, coronary flow, tension time index, end diastolic pressure, and the endocardial viability ratio. Comparisons were made between automated and manual timing set by a certified technician. Results indicate that automated timing yielded equivalent hemodynamic enhancement with greater ease of adjustment. A closed-loop control scheme is proposed which allows complete automatic device operation and the capability to rapidly achieve the optimum of any directly measurable hemodynamic variable.

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.