Computers in biology and medicine
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Paroxysmal atrial fibrillation (PAF) is one of the most common heart arrhythmias. It is very difficult to detect unless an explicit Atrial Fibrillation episode occurs during the exploration. ⋯ The ability of this parameter set to characterize PAF patients is studied and discussed. Based on these parameters a modular automatic classification algorithm for PAF diagnosis is developed and evaluated.
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Electronic auscultation is an efficient technique to evaluate the condition of respiratory system using lung sounds. As lung sound signals are non-stationary, the conventional method of frequency analysis is not highly successful in diagnostic classification. ⋯ Lung sound signals were decomposed into the frequency subbands using wavelet transform and a set of statistical features was extracted from the subbands to represent the distribution of wavelet coefficients. An ANN based system, trained using the resilient back propagation algorithm, was implemented to classify the lung sounds to one of the six categories: normal, wheeze, crackle, squawk, stridor, or rhonchus.
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Computer simulators play an important role in medical education. We have extended our simulator EchoComJ with an intelligent training system (ITS) to support trainees adjusting echocardiographic standard views. ⋯ The ITS analyzes the image planes according to their position, orientation and the visualization of anatomical landmarks using fuzzy rules. An adaptive feedback is provided that colors the specific anatomic landmarks within the contours of the virtual model based on the quality of the image plane.
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We tested the adequacy of a videoconferencing system using a single integrated systems digital network (ISDN) line (128 kilobits per second) for the remote diagnosis of children with suspected congenital heart disease (CHD). Real-time echocardiogram interpretation was compared to subsequent videotape review in 401 studies with concordance in 383 (95.5%) studies. ⋯ In 300 studies, a normal diagnosis obviated further evaluation. A single ISDN line is adequate for transmission of pediatric echocardiograms and it allows for remote management of patients with CHD.
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A number of natural time series including electroencephalogram (EEG) show highly non-stationary characteristics in their behavior. We analyzed the EEG in sleep apnea that typically exhibits non-stationarity and long-range correlations by calculating its scaling exponents. Scaling exponents of the EEG dynamics are obtained by analyzing its fluctuation with detrended fluctuation analysis (DFA), which is suitable for non-stationary time series. We found the mean scaling exponents of EEG is discriminated according to Non-REM, REM (Rapid Eye Movement) and waken stage, and gradually increased from stage 1 to stage 2, 3 and 4.