Biomedizinische Technik. Biomedical engineering
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Atrial fibrillation is the most common cardiac arrhythmia, affecting more than two million people in the US. Several therapies for patients with atrial fibrillation are available, but methods to help physicians select the optimal therapy for an individual patient are still required. Knowledge of whether a patient with a normal ECG will exhibit atrial fibrillation in the future, as well as whether atrial fibrillation will terminate spontaneously, would be very useful in clinical routine. ⋯ This frequency has been shown to decrease significantly prior to the termination of atrial fibrillation. Nevertheless, the effect is much less distinct in the large data set used for this study compared to previous studies. The initiation of atrial fibrillation, however, could be correctly predicted in approximately 75% of the data analyzed.
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Independent component analysis (ICA) is an emerging technique for multidimensional signal processing. In recent years, these techniques have been proposed for solving a large number of biomedical applications. This work reviews current knowledge on ICA in electrocardiographic (ECG) analysis. The benefits that ICA can bring to clinical practice are illustrated with four relevant clinical applications: foetal ECG extraction from maternal ECG recordings, analysis of atrial fibrillation, ECG denoising and removal of pacemaker artefacts.
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Monitoring the depth of anaesthesia has become an important research topic in the field of biosignal processing. Auditory evoked potentials (AEPs) have been shown to be a promising tool for this purpose. Signals recorded in the noisy environment of an operating theatre are often contaminated by artefacts. ⋯ Determination of a suitable artefact detection configuration based on EEG data from a clinical study is described. Artefact detection algorithms and an AEP extraction procedure encompassing the artefact detection results are presented. Different configurations of artefact detection algorithms are evaluated using an AEP verification procedure and support vector machines to determine a suitable configuration for the assessment of depth of anaesthesia using AEPs.
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Electroencephalogram (EEG) signals and auditory evoked potentials (AEPs) have been suggested as a measure of depth of anaesthesia, because they reflect activity of the main target organ of anaesthesia, the brain. The online signal processing module NeuMonD is part of a PC-based development platform for monitoring "depth" of anaesthesia using EEG and AEP data. NeuMonD allows collection of signals from different clinical monitors, and calculation and simultaneous visualisation of several potentially useful parameters indicating "depth" of anaesthesia using different signal processing methods. The main advantage of NeuMonD is the possibility of early evaluation of the performance of parameters or indicators by the anaesthetist in the clinical environment which may accelerate the process of developing new, multiparametric indicators of anaesthetic "depth".
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Changes in normal lung sounds are an important sign of pathophysiological processes in the bronchial system and lung tissue. For the diagnosis of bronchial asthma, coughing and wheezing are important symptoms that indicate the existence of obstruction. In particular, nocturnal long-term acoustic monitoring and assessment make sense for qualitative and quantitative detection and documentation. ⋯ In 68 of the patients we could detect cough events and in 87 we detected wheezing. The recording method was tolerated by all participating adults and children. Our mobile system allows non-invasive and cooperation-independent nocturnal monitoring of acoustic symptoms in the domestic environment, especially at night, when most ailments occur.