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The aim of this project is to improve the detection of coronary occlusions using an approach based on the recording and analysis of isolated diastolic heart sounds associated with turbulent blood flow in occluded coronary arteries. The nonlinear dynamic analysis method based on approximate entropy has been proposed for the analysis of diastolic heart sounds. A commercially available electronic stethoscope was used to record the diastolic heart sounds from patients diagnosed with or without coronary artery disease (CAD) based on their coronary angiography examination. The nonlinear dynamical analysis [approximate entropy) measures of the diastolic heart sounds recorded from 30 patients with coronary occlusions and ten normal subjects were estimated. Results suggest the presence of the high nonlinear (approximate entropy) values of diastolic heart sounds associated with CAD (p < 0.05). This approach led to a sensitivity of 77 %, a specificity of 80%, and an overall accuracy of 78%. As a summary, 23 out of 30 abnormal patients and eight out of ten normal patients were correctly detected. Index Terms--Approximate entropy, coronary artery disease (CAD), nonlinear dynamics analysis.

Single fiber action potentials (SFAPs) from peripheral nerves, such as recorded with cuff electrodes, can be modelled as the convolution of a source current and a weight function that describes the recording electrodes and the surrounding medium. It is shown that for cuff electrodes, the weight function is linearly scaled with the action potential (AP) velocity and that it is, therefore, possible to implement a model of the recorded SFAPs based on a wavelet multiresolution technique (filterbank), where the wavelet scale is proportional to the AP velocity. The model resulted in single fiber action potentials matching the results from other models with a goodness of fit exceeding 0.99. This formulation of the SFAP may serve as a basis for model-based wavelet analysis and for advanced cuff design. Index Terms--Cuff electrodes, filter bank, nerve signal, single fiber action potentials, wavelet.

In this paper, we investigate the influence of hypercapnia on the early and late phases of the phrenic neurogram using the matching pursuit (MP) method in the decebrated piglets. The phrenic neurogram was recorded from 8 piglets (4-7 days old) during control (40% [O.sub.2] with 5% end-tidal C[O.sub.2]), the mild hypercapnia (40% [O.sub.2] with 7% end-tidal C[O.sub.2]), and the severe hypercapnia (40% [O.sub.2] with 15% end-tidal C[O.sub.2]). The time-frequency representations, atoms, of the phrenic neurogram are calculated from the 5 consecutive phrenic neurogram burst for each piglet for each condition using the MP method after vagotomy and chemodenervation. Our results show that the energy percentage of atoms representing the nonperiodic neural activities (NPNAs) significantly increased when the C[O.sub.2] concentration was shifted from 7% to 15% in the early phase (the first half) of the phrenic neurogram. In addition, the energy percentage of atoms representing the periodic neural activities (PNAs) decreased in the late phase (the second half) when the C[O.sub.2] concentration was shifted from 7% to 15% (p < 0.01). As a summary, our result suggest that hypercapnia results in significant changes in the phrenic neurogram, an output of the respiratory neural networks in the medulla, both in time and frequency domians during early maturation. Index Terms--Hypercapnia, matching pursuit, phrenic neurogram, respiratory pattern generator, time-frequency analysis.

Nonlinear dynamical analysis was performed on the phrenic neurogram before and after vagotomy in order to study the influence of the vagus nerve on the complexity of the phrenic neurogram in piglets in three age groups: 3-7 days (n = 7); 11-19 days (n = 6); and 29-34 days (n = 8). The phrenic neurogram, generated by the respiratory neural networks in the medulla, projects on the diaphragm muscles and initiate the respiratory movement. On the other hand, the vagus nerves carry the information from mechanoreceptors, located at the lower airway and lungs, to the medulla. The data was recorded during normal breathing (eupnea) before and after vagotomy while piglets were ventilated with 40% [O.sub.2] in [N.sub.2] and analyzed using the approximate entropy (ApEn) method. The mean values of the approximate entropy before and after vagotomy during the first 7 days of the postnatal age were 1.32 [+ or -] 0.1 (standard deviation) and 1.34 [+ or -] 0.07, respectively. These values before and after vagotomy during the 11-19 days age group were 1.15 [+ or -] 0.09 and 1.12 [+ or -] 0.05, respectively. For the 29-34 days age group, they were 1.14 [+ or -] 0.05 before vagotomy and 1.19 [+ or -] 0.08 after vagotomy. These differences in the ApEn (complexity) values of the phrenic neurogram before and after vagotomy are not statistically different at each age group. However, the mean mean approximate entropy (complexity) values between the 3-7 days age group and the other two groups were significantly different both before and after vagotomy (p < 0.05) using an analysis off variance test. These results suggest that the vagus nerve may not be mature during early maturation in piglets. Index Terms--Approximate entropy, complexity, eupnea, maturation, phrenic nerve, vagotomy.

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.

In this paper, we investigate the effects of maturation on the early and late phases of the phrenic neurogram. We have used the matching pursuit (MP) method to examine the effects of maturation on breathing patterns in both time and frequency domains. The MP was chosen since the wavelet transform method may not represent signals whose Fourier transforms have a narrow high-frequency support. The phrenic neurogram was recorded from 25 piglets (3-35 days) during eupnea (normal breathing) at three postnatal age groups: young (3-7 days (n = 9)), (middle) 10-21 days (n = 6), and old (29-35 days (n = 10)). The energy percentage of atoms representing the nonperiodic neural activities (NPNAs) significantly decreased from young age to middle age groups (p The energy percentage of (PNAs) increased in the late phase of the phrenic neurogram as maturation proceeded although these changes were only significant between young and old age groups (p These results suggest that the significant decrease of the NPNAs in the early phase and the increase in the late phase of the phrenic neurogram could be a sign of maturation in piglets. Index Terms--Matching pursuit (MP), maturation, nonperiodic neural activity (NPNA), periodic neural activity (PNA), phrenic neurogram, short-time Fourier transform (STFT), wavelet transform (WT).

Previous studies have indicated heart sounds may contain information useful in the detection of occluded coronary arteries (1)-(12). During diastole, coronary blood flow is maximum, and the sounds associated with turbulent blood flow through partially occluded coronary arteries should be detectable (1)-(12). 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 (Eigenvector) methods. To further enhance the diastolic heart sounds and reduce background noise, an Adaptive filter was used as a preprocessor (12). The power ratios of the FFT method and the poles of the AR, ARMA, and Eigenvector 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 (2)-(12).

In this paper, the discrete wavelet transform (DWT) was applied to analyze the fluctuations in RR interval and systolic arterial pressure (SAP) recorded from eight [alpha]-chloralose anesthetized pigs. Our aim was to characterize the autonomic modulation before and after cardiac autonomic blockade and during baroreflex function tests. The instantaneous power of decomposed low-frequency (LF) and high-frequency (HF) components was used for a time-variant spectral analysis. Our results suggested that transient events and changes in autonomic modulation were detected with high temporal resolution. A nonlinear relationship between RR interval and SAP during pharmacologically induced changes in blood pressure was found, when the superimposed effect of respiratory sinus arrhythmia was removed. In addition, the baroslopes were nearly linear when both the LF and HF components were removed using DWT decomposition. Index Terms--Baroreflex, heart rate variability, HRV, spectral analysis, wavelet transform.

The signals produced by turbulent blood flow through stenosed coronary blood vessels were measured to develop a noninvasive approachin detecting coronary artery disease. Diastolic heart sound recordings filteredfrom background noise were analyzed using autoregressive and autoregressive moving average methods before and after angioplasty. Results show that this acoustic approach is a moderately useful noninvasive method to detect coronary artery disease.

Fractal methods have been found to be useful in characterizing biomedical signals. The use of fractal estimation requires the estimation of parameter H, which is directly related to the fractal dimension D. Here, we propose a new approach which is a combination of the wavelet transform and fractal estimators to characterize the human fetal breathing signals before and after the intake of two glasses of wine by a mother. This study was performed on 26 fetuses. The variances of the wavelet coefficients were estimated at each scale. The slope of the representation on a logarithmic plot from the scales 5 to 1 was found to be increased after alcohol intake. Our results suggested that fetal breathing rates have a rough structure before the alcohol intake and a smooth structure after alcohol intake. Index Terms - Alcohol, fractal, wavelets.

Stretch-activated ion channels (SAC's) in cardiac myocytes from neonatal rats were studied in cell-attached patches. Stretch of membrane patches by suction in the recording pipette caused the triggering of action potentials that were recorded as action currents (AC's). The significance of a temporal correlation between SAC open probability and AC's was tested using the Kolmogorov-Smirnov and Poisson distributions. It was shown that the 50-ms epoch immediately preceding the action current had unique kinetics and represented a peak in SAC open probability (p < 0.001). Thus it appears that current from a small number of SAC's injects sufficient charge (0.2 pC during 50 ms) to trigger action potentials in myocytes. These data strengthen the hypothesis that passive mechanical stretch of myocardium can be arrhythmogenic.

In this paper, we introduce the nonstationary signal analysis methods to analyze the myoelectric (ME) signals during dynamic contractions by estimating the time-dependent spectral moments. The time-frequency analysis methods including the short-time Fourier transform, the Wigner-Ville distribution, the Choi-Williams distribution, and the continuous wavelet transform were compared for estimation accuracy and precision on synthesized and real ME signals. It is found that the estimates provided by the continuous wavelet transform have better accuracy and precision than those obtained with the other time-frequency analysis methods on simulated data sets. In addition, ME signals from four subjects during three different tests (maximum static voluntary contraction, ramp contraction, and repeated isokinetic contractions) were also examined. Index Terms--Choi-Williams distribution (CWD), continuous wavelet transform (CWT), myoelectric (ME) signal, pseudo Wigner-Ville distribution (PWVD), running windowed exponential distribution (RWED), short-time Fourier transform (STFT), spectral change indicator, time-frequency analysis, Wigner-Ville distribution (WVD).

In this paper, we introduce wavelet packets as an alternative method for spectral analysis of surface myoelectric (ME) signals. Both computer synthesized and real ME signals are used to investigate the performance. Our simulation results show that wavelet packet estimate has slightly less mean square error (MSE) than Fourier method, and both methods perform similarly on the real data. Moreover, wavelet packets give us some advantages over the traditional methods such as multiresolution of frequency, as well as its potential use for effecting time-frequency decomposition of the nonstationary signals such as the ME signals during dynamic contractions. We also introduce wavelet shrinkage method for improving spectral estimates by significantly reducing the MSE's for both Fourier and wavelet packet methods. Index Terms - Autoregressive moving average (ARMA), Fourier transform, myoelectric signal (ME), power spectral density, spectral estimation, wavelet transform (WT), wavelet packets (WP), WP spectrum, wavelet shrinkage, wavelet spectrum.