Biomedical subjects

Leontios J Hadjileontiadis

Publications and source records attributed to Leontios J Hadjileontiadis.

8 recordsLinked to original sources

PubMed2006

Wheeze detection based on time-frequency analysis of breath sounds.

Abnormal breath sounds like wheezes are observed in patients with obstructive pulmonary diseases. The aim of this study was to construct an automatic technique for wheeze detection and monitoring using spectral analysis. Wheezes from 13 patients with diagnosed asthma, chronic obstructive pulmonary disease and pneumonia were recorded and a time-frequency wheeze detector (TF-WD) based on TF wheeze characteristics was constructed. The TF-WD was evaluated using 337 wheezes by comparing its findings with those from clinical auscultation performed by two experts. In addition, the TF-WD was tested against artificial noise. The experimental and testing results justified the efficient performance and high noise robustness of the TF-WD.

Adult↗

PubMed2006

Nonlinear analysis of wheezes using wavelet bicoherence.

Wheezes, as being abnormal breath sounds, are observed in patients with obstructive pulmonary diseases, such as asthma. The aim of this study was to capture and analyze the nonlinear characteristics of asthmatic wheezes, reflected in the quadrature phase coupling of their harmonics, as they evolve over time within the breathing cycle. To achieve this, the continuous wavelet transform (CWT) was combined with third-order statistics/spectra. Wheezes from patients with diagnosed asthma were drawn from a lung sound database and analyzed in the time-bi-frequency domain. The analysis results justified the efficient performance of this combinatory approach to reveal and quantify the evolution of the nonlinearities of wheezes with time.

Algorithms↗

PubMed2006

Adaptive neuro-fuzzy modeling of poorly soluble drug formulations.

PURPOSE: The purpose of this study was to evaluate the efficiency of a neuro-fuzzy logic-based methodology to model poorly soluble drug formulations and predict the development of the particle size that has been proven to be an important factor for long-term stability. METHODS: An adaptive neuro-fuzzy inference system was used to model the natural structures within the data and construct a set of fuzzy rules that can subsequently used as a predictive tool. The model was implemented in Matlab 6.5 and trained using 75% of an experimental data set. Subsequently, the model was evaluated and tested using the remaining 25%, and the predicted values of the particle size were compared to the ones from the experimental data. The produced adaptive neuro-fuzzy inference system-based model consisted of four inputs, i.e., acetone, propylene glycol, POE-5 phytosterol (BPS-5), and hydroxypropylmethylcellulose 90SH-50, with four membership functions each. Moreover, 256 fuzzy rules were employed in the model structure. RESULTS: Model training resulted in a root mean square error of 4.5 x 10(-3), whereas model testing proved its highly predictive efficiency, achieving a correlation coefficient of 0.99 between the actual and the predicted values of the particle size (mean diameter). CONCLUSIONS: Neuro-fuzzy modeling has been proven to be a realistic and promising tool for predicting the particle size of drug formulations with an easy and fast way, after proper training and testing.

Acetone↗

PubMed2005

ICASP: an intensive-care acquisition and signal processing integrated framework.

This paper presents an intensive-care acquisition and signal processing integrated framework in the area of intensive care units. The framework includes nearly all monitored biosignals in the intensive care, along with metadata and processing results. It is structured on two basic applications, i.e., the acquisition and the database one, running in two different PCs that are connected through a local area network, facilitating real-time data exchange between them. The analytical rundown shows that the proposed framework is a serious effort to give a complete clinical condition of a patient and a form of a diagnostic analysis implement in the intensive care by taking in real-time processing.

Hospital Information Systems↗

PubMed2005

Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding--Part I: methodology.

An efficient method for the enhancement of lung sounds (LS) and bowel sounds (BS), based on wavelet transform (WT), and fractal dimension (FD) analysis is presented in this paper. The proposed method combines multiresolution analysis with FD-based thresholding to compose a WT-FD filter, for enhanced separation of explosive LS (ELS) and BS (EBS) from the background noise. In particular, the WT-FD filter incorporates the WT-based multiresolution decomposition to initially decompose the recorded bioacoustic signal into approximation and detail space in the WT domain. Next, the FD of the derived WT coefficients is estimated within a sliding window and used to infer where the thresholding of the WT coefficients has to happen. This is achieved through a self-adjusted procedure that iteratively "peels" the estimated FD signal and isolates its peaks produced by the WT coefficients corresponding to ELS or EBS. In this way, two new signals are constructed containing the useful and the undesired WT coefficients, respectively. By applying WT-based multiresolution reconstruction to these two signals, a first version of the desired signal and the background noise is provided, accordingly. This procedure is repeated until a stopping criterion is met, finally resulting in efficient separation of the ELS or EBS from the background noise. The proposed WT-FD filter introduces an alternative way to the enhancement of bioacoustic signals, applicable to any separation problem involving nonstationary transient signals mixed with uncorrelated stationary background noise. The results from the application of the WT-FD filter to real bioacoustic data are presented and discussed in an accompanying paper.

Algorithms↗

PubMed2005

Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding--Part II: application results.

The application of the wavelet transform-fractal dimension-based (WT-FD) filter of Part I of this paper to real bioacoustic data, which include explosive lung sounds (ELS) and explosive bowel sounds (EBS) recorded from patients with pulmonary or gastrointestinal dysfunction, respectively, is presented in this paper. The objective of the latter is the evaluation of the performance of the WT-FD filter on different types of bioacoustic signals, varying not only in their structural morphology but also in the degree of their noise contamination. As it is thoroughly described in Part I of this paper, the WT-FD filter uses the fractal dimension to form an efficient way of thresholding the WT coefficients at different resolution scales, keeping, thus, only those that can contribute to the accurate reconstruction of the ELS and EBS signals. Quantitative and qualitative analysis of the experimental results show an efficient performance of the WT-FD filter to circumvent the noise presence (100% detectability rate, 100% sensitivity, 100% specificity) by faithfully extracting the authentic structure of ELS and EBS from the background noise. The WT-FD filter does not require any noise reference signal or noise reference templates. The results from a noise stress test (mean cross-correlation index of the original and the estimated signal converging to 100%; mean normalized maximum amplitude error converging to 0.7%) prove its robustness to various noise levels (0-20 dB), enabling its potential use in similar noise cases met in everyday clinical medicine. Furthermore, the efficient performance of the WT-FD filter facilitates the physician to better interpret the auscultation findings. Due to its simplicity and low computational cost, the WT-FD filter can possibly be implemented in a real-time context to serve as a tool for the continuous ELS and EBS screening.

Adult↗

PubMed2005

Using higher-order crossings to distinguish liver regeneration indices in hepatectomized diabetic and non-diabetic rats.

BACKGROUND AND AIMS: Diabetes mellitus is implicated in several liver diseases; hence, its potential affection to liver regenerative capacity is an open research question. So far, only sporadic studies have addressed this issue, mainly using basic statistical techniques. The current study evaluated the ability of a novel technique, namely higher-order crossings (HOC), based on liver DNA biosynthesis and thymidine kinase (TK) enzymatic activity data, to discriminate liver regeneration processes between hepatectomized diabetic and non-diabetic rats. METHODS: We used 251 adult male rats, divided in two groups; diabetic by Alloxan injection and non-diabetic control, subjected to 70% partial hepatectomy and killed at different time intervals post-partial hepatectomy (PH) (0-240 h). The rate of tritiated thymidine (3HTdR) incorporation into hepatic DNA and the enzymatic activity of liver TK were estimated and, after proper interpolation, were analyzed using HOC sequences. Changes of the latter were captured and used as a means for linear discrimination between the two groups. RESULTS: Ninth-order HOC estimated for post-PH (24, 28, 40, 44, 72 and 84 h) exhibited linear discrimination for the rate of 3HTdR incorporation, whereas second-order HOC estimated for (44-72 h) post-PH exhibited linear discrimination for the TK enzymatic activity data. Fuzzy logic-based c-means cluster analysis of HOC provided distinct areas of group categorization (100% accuracy) for diagnostic distinctions (P < 0.001). The data grouping pointed out by the HOC-based analysis revealed an onset delay in the liver regeneration process when Alloxan diabetes was present (P < 0.05). CONCLUSIONS: Our results suggest that HOC have the potential to linearly discriminate between experimentally induced diabetic and non-diabetic liver regeneration post-PH processes, based on two liver regeneration indices, capturing the delay seen in the liver regeneration process due to Alloxan diabetes, fostering their use as an efficient classification tool. In this way, HOC could be used as an advanced, easily implemented and user-friendly method to thoroughly analyze liver regeneration processes.

Animals↗

PubMed2003

Bowel sounds analysis: a novel noninvasive method for diagnosis of small-volume ascites.

Ascites is more difficult to detect when only a small quantity is present. The aim of this pilot study was to determine the optimal bowel sound characteristics in order to distinguish no ascites from small-volume ascites by advanced processing of bowel sound wave patterns. This analysis results in the definition of the normal range of bowel sound patterns, thus providing a novel, simple, and noninvasive way of determining on abnormal pattern, which may reflect presence of small volume ascites. Cirrhotic patients with radiologically proven small-volume ascites and a control group were subjected to bowel sound recordings. The latter were analyzed using a denoising wavelet transform-based filter and a higher-order crossings-based technique in a blinded fashion for linearly distinguishing the two classes. Scatter plots of third-order zero crossings reflect distinct changes seen in the denoised bowel sound pattern between patients and controls due to altered transmission path, providing a distinct separation of all cirrhotic patients with small ascites from controls (P < 0.0001). We conclude that the proposed bowel sounds analysis appears to provide new information regarding the changes of the bowel sound patterns due to the presence of small-volume ascites, potentially contributing towards a safe, effective, noninvasive, and easily implemented alternative method for the diagnosis of small volume ascites at the bedside.

Adult↗