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Biomedical subjects

M Akay

Publications and source records attributed to M Akay.

At least 55 records · Page 3Linked to original sources
PubMed1993

Mechanoelectrical feedback in cardiac myocytes from stretch-activated ion channels.

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.

Action Potentials↗
PubMed1992

Noninvasive diagnosis of coronary artery disease using a neural network algorithm.

This study examines the utility of neural networks for detecting coronary artery disease noninvasively by using the clinical examination variables and extracting useful information from the diastolic heart sounds associated with coronary occlusions. It has been widely reported that coronary stenoses produce sounds due to the turbulent blood flow in these vessels. These complex and highly attenuated signals taken from recordings made in both soundproof and noisy rooms were detected and analyzed to provide feature set based on the poles and power spectral density function (PSD) of the Autoregressive (AR) method after Adaptive Line Enhancement (ALE) method. In addition, some physical examination variables such as sex, age, body weight, smoking condition, diastolic pressure, systolic pressure and derivation from them were included in the feature vector. This feature vector was used as the input pattern to the neural network. The analysis was studied on one hundred recordings (63 abnormal, 37 normals). The network correctly identified 84% of the subjects with coronary artery disease and 89% of the normal subjects.

Algorithms↗
PubMed1992

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.

Coronary Disease↗
PubMed1992

Heart rate variability as an index of autonomic imbalance in patients with recent myocardial infarction.

Autonomic nervous activities are estimated in three groups of patients: Group A consists of patients who had experienced myocardial infarction (MI) within 2-6 weeks before the tests; Group B consists of patients who had MI more than one year previously; Group C consists of matched controls, free of cardiac disease. Autonomic activity is estimated using postural effects on heart rate variability (HRV): a sympathetic activity index is defined as HRV power within a low frequency band (0.04-0.12 Hz) in the tilt position and a parasympathetic activity index is defined as HRV power in a high frequency band (0.18-0.28 Hz) in the supine position. Results show that, relative to controls, Group A patients have reduced parasympathetic activity index (5 + 3 against 13 + 8, normalised units; p < 0.05) and an increased ratio of sympathetic to parasympathetic activity (17 + 17 against 4 + 2; p < 0.05). Group B is not significantly different from Group A or C. The period of 2-6 weeks post-MI thus appears to be characterised by depressed parasympathetic nervous activity which can be measured using HRV analysis.

Aged↗
PubMed1992

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].

Angioplasty, Balloon, Coronary↗
PubMed1991

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.

Aged↗
PubMed1990

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.

Algorithms↗
PubMed1990

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.

Algorithms↗
PubMedDate not supplied

Localized spatial discrimination of epicardial conduction paths after linear transformation of variant information.

We present a method for the localized statistical discrimination of class populations based on the Karhunen-Loève and Fukunaga-Koontz transforms. These transforms provide features that model the variance of a sample distribution. The spatial series of a 196 channel epicardial electrogram recording from an arrythmogenic postinfarct canine were analyzed. For each type of rhythm studied, Karhunen-Loève and Fukunaga-Koontz expansions were computed from five training sets of spatial data, corresponding to five locations across the surface of the heart. Nonparametric statistical tests were then used for discriminant analysis to compare properties representative of the distribution from each proposed class. In a comparison of properties from sinus rhythm to those of two ventricular tachycardias, several spatial regions exhibited statistically significantly different propagation characteristics. These areas were observed by visual inspection of electrogram activation maps to be characterized by conductive gradients, which differed in magnitude and direction from one rhythm to another. The regions in which the propagation characteristics are of greatest difference in each tachycardia were centered upon sites of conduction block, manifested by reentrant circuit rhythms. Therefore, the importance of the technique for the localization of specific electrophysiologic events is demonstrated. This study extends previous work of our group on biosignal pattern recognition to encompass localized spatial data.

Analysis of Variance↗
PubMedDate not supplied

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.

Animals↗
PubMedDate not supplied

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.

Coronary Disease↗
PubMedDate not supplied

Wavelets in biomedical engineering.

Wavelets analysis methods have been widely used in the signal processing of biomedical signals. These methods represent the temporal characteristics of a signal by its spectral components in the frequency domain. In this way, important features of the signal can be extracted in order to understand or model the physiological system. This paper reviews the widely used orthogonal wavelet transform method in the biomedical applications.

Algorithms↗
PubMedDate not supplied

Investigating the relationship between fetus EEG, respiratory, and blood pressure signals during maturation using wavelet transform.

In this study, we introduce the fast wavelet transform as a method for characterizing maturational changes in electrocortical activity, respiratory activity, and blood pressure in fetal lambs in early (110-122 days), mid (123-135 days), and late (136-144 days) third trimester (term 145 days). Each recording was 2 hr in duration. Wavelet decomposition was performed for six sets of parameters D2j where 1 < or = j < or = 6. The six series wavelet transforms represent the following signal frequency bands: 1. 16-32 Hz; 2. 8-16 Hz; 3. 4-8 Hz; 4. 2-4 Hz; 5. 1-2 Hz; 6. 0.5-1 Hz. In the early group, power in the electrocephalogram (EEG) was highest in the fourth wavelet band, with relatively low power in the other bands. Increase in gestational age was characterized by increased power in all four wavelet bands. Power in the first wavelet band was significantly increased during low-voltage fast activity (LVFA) in the late group. The respiratory and blood pressure signals showed common frequency components with respect to time and were coincident with the LVFA EEG signal. Respiratory activity was only observed during 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 band. 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. These results suggest a strong relationship between fetal EEG, blood pressure, and breathing movements.

Animals↗
PubMedDate not supplied

A comparison of analytical methods for the study of fractional Brownian motion.

Fractional Brownian motion (FBM) provides a useful model for many physical phenomena demonstrating long-term dependencies and l/f-type spectral behavior. In this model, only one parameter is necessary to describe the complexity of the data, H, the Hurst exponent. FBM is a nonstationary random function not well suited to traditional power spectral analysis however. In this paper we discuss alternative methods for the analysis of FBM, in the context of real-time biomedical signal processing. Regression-based methods utilizing the power spectral density (PSD), the discrete wavelet transform (DWT), and dispersive analysis (DA) are compared for estimation accuracy and precision on synthesized FBM datasets. The performance of a maximum likelihood estimator for H, theoretically the best possible estimator, are presented for reference. Of the regression-based methods, it is found that the estimates provided by the DWT method have better accuracy and precision for H > 0.5, but become biased for low values of H. The DA method is most accurate for H < 0.5 for a 256-point data window size. The PSD method was biased for both H < 0.5 and H > 0.5.

Bias↗