The effects of morphine on the relationship between fetal EEG, breathing and blood pressure signals using fast wavelet transform.

In this study, we introduce the fast wavelet transform (WT) as a method for investigating the effects of morphine on the electroencephalogram (EEG), respiratory activity and blood pressure in fetal lambs. Morphine was infused intravenously at 25 mg/h. The EEG, respiratory activity and blood pressure signals were analyzed using WT. We performed wavelet decomposition for five sets of parameters D2j where -1 < j < -5. The five series WTs represent the detail signal bandwidths: 1, 16-32 Hz; 2, 8-16 Hz; 3, 4-8 Hz; 4, 2-4 Hz; 5, 1-2 Hz. Before injection of the high-dose morphine, power in the EEG was high in all six frequency bandwidths. The respiratory and blood pressure signals showed common frequency components with respect to time and were coincident with the low-voltage fast activity (LVFA) EEG signal. Respiratory activity was observed during only some of the LVFA periods, and was completely absent during high-voltage slow activity (HVSA) EEG. The respiratory signal showed dominant power in the fourth wavelet band, and less power in the third and fifth bands. The blood pressure signal was also characterized by dominant power in the fourth wavelet band. This power was significantly increased during periods of respiratory activity. There was a strong relationship between fetal EEG, blood pressure and breathing movements. However, the injection of high-dose morphine resulted in a disruption of the normal cyclic pattern between the two EEG states and a significant increase in power in the first wavelet band. In addition, the high-dose drug resulted in a significant increase in the power of respiratory signal in the fourth and fifth wavelet bands, while power was reduced in the third wavelet band. Breathing activity was also continuous after the drug. The high-dose morphine also caused a temporary power shift from the third wavelet band to the fourth wavelet band for the 30-min period after injection of drug. Finally, high-dose morphine completely destroyed the correlation between EEG, breathing and blood pressure signals.

Investigating the effects of opioid drugs on electrocortical activity using wavelet transform.

Fetal electrocortical activity (ECoG) is characterized by two distinct patterns: HVSA (high voltage, slow activity) and LVFA (low voltage, fast activity). Using the wavelet transform (WT), we recently reported that the frequency characteristics of these two ECoG patterns undergo significant maturational changes prior to birth (Akay et al. 1994a). We now report that fetal ECoG can also be significantly affected by pharmacological agents. In this paper, we compared the effects of two opioid drugs (morphine and [D-Pen2, D-Pen5]-enkephalin, DPDPE) on fetal ECoG, using the chronically instrumented fetal lamb model. Morphine was infused intravenously (i.v.) at 2.5 mg/h, while DPDPE was infused into the lateral cerebroventricle (i.c.v.) at 30 micrograms/h. The ECoG was analyzed using WT. We performed multi-resolution decomposition for four sets of parameters D2j where -1 < j < -4. The four series WTs represent the detail signal bandwidths: (1) 16-32 Hz, (2) 8-16 Hz, (3) 4-8 Hz, (4) 2-4 Hz. The data were subjected to statistical analysis using the Kolmogorov-Smirnov (KS) test. Both morphine and DPDPE resulted in a significant increase in power in the first wavelet band, while power was reduced in the second, third and fourth wavelet bands. In addition, both drugs resulted in a disruption of the normal cyclic pattern between the two ECoG patterns. There was a difference in the time course of action between morphine and DPDPE. This is the first occasion in which continuous ECoG has been subjected to rigorous statistical analysis. The results suggest that the WT-KS method is most suitable for quantitating changes in the ECoG induced by pharmacological agents.

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.

Time-frequency analysis of the electrocortical activity during maturation using wavelet transform.

In this study, we introduce the wavelet transform (WT) as a method for characterizing the maturational changes in electrocortical activity in 24 fetal lambs ranging from 110-144 days gestation (term 145 days). The WT, based on multiresolution signal decomposition, is free of assumptions regarding the characteristics of the signal. The approximation of the electrocortical activity at resolutions varying from 2j+1 to 2j can be extracted by decomposing the signal on a wavelet orthonormal basis of L2(R). We performed multiresolution decomposition for four sets of parameters D2j, where -1 < j < -4. The four series WT represent the detail signal band-widths: (1) 16-32 Hz, (2) 8-16 Hz, (3) 4-8 Hz, (4) 2-4 Hz. The data were divided into three groups according to gestational age: 110-122 days (early), 123-135 days (middle), and 136-144 days (late). In the early group, the power was highest in the fourth signal bandwidth, with relatively low power in the other bands. Increase in gestational age was characterized by increased power in all four bandwidths. Comparison of the cumulative distribution function of the power in the four wavelet bands confirmed the presence of two statistically different patterns in all three age groups. These two patterns correspond to the visually identified patterns of HVSA (high-voltage slow activity) and LVFA (low-voltage fast activity). The earliest development change occurred in HVSA, with progressive increase in power in the 2-8 Hz band. Later changes occurred in LVFA, with a significant increase in power in the 16-32 Hz band.(ABSTRACT TRUNCATED AT 250 WORDS)

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.

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.

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.

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].