Biomedical subjects

S M Panas

Publications and source records attributed to S M Panas.

12 recordsLinked to original sources

PubMed2001

ECG data compression using wavelets and higher order statistics methods.

This paper evaluates the compression performance and characteristics of two wavelet coding compression schemes of electrocardiogram (ECG) signals suitable for real-time telemedical applications. The two proposed methods, namely the optimal zonal wavelet coding (OZWC) method and the wavelet transform higher order statistics-based coding (WHOSC) method, are used to assess the ECG compression issues. The WHOSC method employs higher order statistics (HOS) and uses multirate processing with the autoregressive HOS model technique to provide increasing robustness to the coding scheme. The OZWC algorithm used is based on the optimal wavelet-based zonal coding method developed for the class of discrete "Lipschitizian" signals. Both methodologies were evaluated using the normalized rms error (NRMSE) and the average compression ratio (CR) and bits per sample criteria, applied on abnormal clinical ECG data samples selected from the MIT-BIH database and the Creighton University Cardiac Center database. Simulation results illustrate that both methods can contribute to and enhance the medical data compression performance suitable for a hybrid mobile telemedical system that integrates these algorithmic approaches for real-time ECG data transmission scenarios with high CRs and low NRMSE ratios, especially in low bandwidth mobile systems.

Algorithms↗

PubMed2000

Enhancement of bowel sounds by wavelet-based filtering.

This paper evaluates the performance of an automatic method for structural decomposition, noise removal and enhancement of bowel sounds (BS), based on the wavelet transform. The proposed method combines multiresolution analysis with hard thresholding to compose a wavelet transform-based stationary-nonstationary (WTST-NST) filter, for enhanced separation of bowel sounds (BS) from superimposed noise. Quantitative and qualitative analysis of the experimental results, when applying the WTST-NST filter to BS recorded from controls and patients with gastrointestinal dysfunction, prove that the ability of the WTST-NST filter to remove noise and reveal the authentic structure of BS is excellent. By eliminating the need to record a noise reference signal, this method reduces hardware overhead when analysis of BS is the primary aim. The method is independent of subjective human judgement for selection of noise reference templates, is robust to different levels of signal interference, and, due to its simplicity, can easily be used in clinical medicine.

Adult↗

PubMed2000

An orthogonal least squares-based fuzzy filter for real-time analysis of lung sounds.

Pathological discontinuous adventitious sounds (DAS) are strongly related with the pulmonary dysfunction. Its clinical use for the interpretation of respiratory malfunction depends on their efficient and objective separation from vesicular sounds (VS). In this paper, an automated approach to the isolation of DAS from VS, based on their nonstationarity, is presented. The proposed scheme uses two fuzzy inference systems (FISs), operating in parallel, to perform the task of adaptive separation, resulting in the orthogonal least squares-based fuzzy filter (OLS-FF). By applying the OLS-FF to fine/coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are efficiently separated from VS. The important time domain DAS features, related to diagnostic information, are preserved and their true location and structural morphology are automatically identified. When compared to previous works, the OLS-FF performs quite similarly, but with significantly lower computational load, resulting in a faster real-time clinical screening of DAS.

Algorithms↗

PubMed1999

Microwave imaging using the finite-element method and a sensitivity analysis approach.

A method for reconstructing the constitutive parameters of two-dimensional (2-D) penetrable scatterers from scattered field measurements is presented. This method is based on the differential formulation of the forward scattering problem, which is solved by applying the finite-element method (FEM). Given a set of scattered field measurements, the objective is to minimize a cost function which consists of two terms. The first is the standard error term, which is related to the measurements and their estimates, while the second term, which is related to the Tikhonov regularization, is used to heal the ill posedness of the inverse problem. The iterative Polak-Ribière nonlinear conjugate gradient algorithm is applied to the minimization of the cost function. During each iteration of the algorithm, the direction of correction is computed by using a sensitivity analysis approach, which is carried out by an elaborate finite-element scheme. The adoption of the finite-element method results in sparse systems of equations, while the computational burden is further reduced by applying the adjoint state vector methodology. Finally, a microwave medical imaging application, which is related to the detection of proliferated bone marrow, is examined, while the robustness of the proposed technique in the presence of noise and for different regularization levels is investigated.

Bone Marrow↗

PubMed1998

A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering.

In this paper we present a new unsupervised fuzzy algorithm for vessel tracking that is applied to the detection of the ocular fundus vessels. The proposed method overcomes the problems of initialization and vessel profile modeling that are encountered in the literature and automatically tracks fundus vessels using linguistic descriptions like "vessel" and "nonvessel." The main tool for determining vessel and nonvessel regions along a vessel profile is the fuzzy C-means clustering algorithm that is fed with properly preprocessed data. Additional procedures for checking the validity of the detected vessels and handling junctions and forks are also presented. The application of the proposed algorithm to fundus images and simulated vessels resulted in very good overall performance and consistent estimation of vessel parameters.

Algorithms↗

PubMed1998

Real-time separation of discontinuous adventitious sounds from vesicular sounds using a fuzzy rule-based filter.

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS, by isolating them from VS, based on their nonstationarity, is presented in this paper. The proposed algorithm uses two adaptive network-based fuzzy inference systems to compose a generalized fuzzy rule-based stationary-nonstationary filter (GFST-NST). The training procedure of the fuzzy inference systems involves the outputs of the wavelet transform-based stationary-nonstationary filter (WTST-NST), proposed by Hadjileontiadis and Panas [1]. The basic idea of the GFST-NST was initially proposed by the authors with the introduction of the fuzzy rule-based stationary-nonstationary filter (FST-NST) [2], tested with the separation of crackles from VS. The main contribution of this paper is the modification of the structure of the FST-NST filter to a serial-type fuzzy filter that, unlike the parallel operation of the FST-NST filter, sends a predicted stationary signal (VS) into the predictor of the nonstationary (DAS). Applying the GFST-NST filter to fine-coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. The separation performance of the GFST-NST filter was evaluated through quantitative and qualitative indexes that proved its efficiency and superiority against the FST-NST filter. When compared to the WTST-NST filter, the GFST-NST filter performed similarly in accuracy and objectiveness, but in a faster way. Thus, the GFST-NST filter combines the separation accuracy of the WTST-NST filter with the real-time implementation of the FST-NST filter, so it can easily be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

Auscultation↗

PubMed1997

Adaptive reduction of heart sounds from lung sounds using fourth-order statistics.

When recording lung sounds, an incessant noise source occurs due to heart sounds. This noise source severely contaminates the breath sound signal and interferes in the analysis of lung sounds. In this paper, an adaptive heart-noise reduction method, based on fourth-order statistics (FOS) of the recorded signal, without requiring recorded "noise-only" reference signal, is presented. This algorithm uses adaptive filtering to preserve the entire spectrum. Furthermore, the proposed filter is independent of Gaussian uncorrelated noise and insensitive to the step-size parameter. It converges fast with small excess errors and, due to the narrow-band nature of heart noise (HN), it requires a very small number of taps. Results from experiments with healthy subjects indicate a local HN reduction equal to or greater than 90%.

Adaptation, Physiological↗

PubMed1997

Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter.

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds, since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS by isolating them from VS, based on their nonstationarity, is presented in this paper. The proposed algorithm combines multiresolution analysis with hard thresholding in order to compose a wavelet transform-based stationary-nonstationary filter (WTST-NST). Applying the WTST-NST filter to fine/coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. When compared to other separation tools, the WTST-NST filter performed more accurately, objectively, and with lower computational cost. Due to its simple implementation it can easily be used in clinical medicine.

Algorithms↗

PubMed1997

Adaptive reduction of heart sounds from lung sounds using a wavelet-based filter.

A new adaptive method for heart sounds reduction from lung sounds, based on wavelet transform, is presented in this paper. The use of a wavelet transform domain filtering technique as an adaptive de-noising tool, implemented in lung sounds analysis, is introduced. The multiresolution representations of the signal, produced by wavelet transform, are used for signal structure extraction. Experimental results have shown that implementation of this wavelet-based filter in lung sound analysis results in an efficient reduction of heart sounds from lung sounds, producing an almost noise-free output signal.

Adult↗

PubMed1997

Higher-order statistics: a robust vehicle for diagnostic assessment and characterisation of lung sounds.

The use of higher-order statistics for diagnostic assessment and characterisation of lung sounds is presented in this article. The parametric approach of bispectrum estimation, which is a third-order spectrum, based on a non-Gaussian white noise driven autoregressive (AR) model, reveals information about lung sounds that is not contained in the ordinary power spectrum, such as the degree of nonlinearity and deviations from normality. Characterisation of source and transmission of lung sounds is achieved using an AR model based on third-order statistics. Furthermore, harmonic analysis of lung sounds is combined with the bicoherence index in order to obtain information regarding possible quadratic phase coupling among harmonic components of musical lung sounds. Experiments have shown that higher-order statistics can offer reliable evaluation of lung sounds characteristics, since their general properties and robustness in noiseless or noisy environments (lung sounds contaminated with additive symmetrical noise, e.g., Gaussian) proved to have superior advantages in objective analysis of pulmonary dysfunction.

Humans↗

PubMedDate not supplied

Bispectral de-noising of the compound action potential for estimation of the nerve conduction velocity distribution.

The distribution of the conduction velocities (DCV) of a peripheral nerve is a powerful diagnostic tool for the assessment of neuromuscular disorders. Its efficient calculation depends on the signal-to-noise ratio (SNR) of the acquired electroneurograms (ENGs), thus, time averaging is solely used. An alternative way of improving the SNR is based on averaging in the bispectrum domain and it is proposed in this work. The compound action potential (CAP) is a linear summation of the single fiber action potentials (SFAPs) propagating along the nerve fibers and can be expressed, in the discrete time, as the circular convolution of a delay sequence (DS) and the sampled SFAP. In the proposed method, averaging of low SNR CAP measurements is done in third order spectrum domain so no time alignment is required. Averaged bispectra are introduced in modified Hirose's method, to estimate the delay sequence for a conduction distance l1. The lost linear phase is recovered by using the delay phase cepstrum. Finally, the DCV can be calculated from the estimated DS, according to the formulation of the forward problem. Comparison between time and bispectrum averaging is performed using simulated data, proving the more efficient performance of the proposed method, especially in the case of noisy ENGs.

Action Potentials↗

PubMedDate not supplied

A wavelet-based reduction of heart sound noise from lung sounds.

Heart sounds produce an incessant noise during lung sounds recordings. This noise severely contaminates the breath sounds signal and interferes in the analysis of lung sounds. In this paper, the use of a wavelet transform domain filtering technique as an adaptive de-noising tool, implemented in lung sounds analysis, is presented. The multiresolution representations of the signal, produced by wavelet transform, are used for signal structure extraction. In addition, the use of hard thresholding in the wavelet transform domain results in a separation of the nonstationary part of the input signal (heart sounds) from the stationary one (lung sounds). Thus, the location of the heart sound noise (1st and 2nd heart sound peaks) is automatically detected, without requiring any noise reference signal. Experimental results have shown that the implementation of this wavelet-based filter in lung sound analysis results in an efficient reduction of the superimposed heart sound noise, producing an almost noise-free output signal. Due to its simplicity and its fast implementation the method can easily be used in clinical medicine.

Adult↗