Journal of medical engineering & technology
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The purpose of this review is to survey the types of intermittent pneumatic compression systems that are currently used, and their medical applications. ⋯ The full potential of intermittent pneumatic compression has probably not yet been realized, and requires better quality research. System design must follow physiological evidence, and while complexity in that design may allow greater therapeutic flexibility, it may incur greater financial cost, difficulty in use, and in the prevention of DVT in particular may be unnecessary.
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Intermittent pneumatic compression (IPC) systems are used for prophylaxis of venous thromboembolism. Both legs are wrapped with inflatable sleeves connected to a pneumatic controller to allow compression of the legs causing expulsion of venous blood. Venous refill between inflation periods causes leg expansion, which can be tracked by measuring pressure changes in the sleeve. The aim of our study, which utilized the SCD RESPONSE compression system in conjunction with an independent pressure transducer, was to investigate whether factors such as temperature changes within the sleeves during inflation and deflation affect the measured venous refill time (VRT). ⋯ In all cases, temperature changes during the refill phase were too small to result in significant pressure changes that would affect VRT. The pressure increases observed with the static models after deflation appeared to be due to viscoelastic relaxation. Viscoelastic responses were present in human subjects, but the effect on VRT was negligible. This indicates that the increased VRT observed in humans is due to blood return. Body position affected VRTs, indicating the system's ability to detect changes in filling times and venous blood volume.
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This paper presents the application of a support vector machine (SVM) for the detection of QRS complexes in the electrocardiogram (ECG). The ECG signal is filtered using digital filtering techniques to remove noise and baseline wander. The support vector machine is used as a classifier to delineate QRS and non-QRS regions. ⋯ This improves to 99.75% for the simultaneously recorded 12-lead ECG signal. The percentage of false negative detection is 0.7% and the percentage of false positive detection is 12.4% in the single-lead QRS detection and it reduces to 0.26% and 1.61% respectively for QRS detection in simultaneously recorded 12-lead ECG signals. The performance of the algorithms depends strongly on the selection and the variety of the ECGs included in the training set, data representation and the mathematical basis of the classifier.
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In this work, the classification of brain tumours in magnetic resonance images is studied by using optimal texture features. These features are used to classify three sets of brain images - normal brain, benign tumour and malignant tumour. A wavelet-based texture feature set is derived from the region of interest. ⋯ A classification performance of 98% is achieved in a genetic algorithm with only four of the available 29 features. Principal component analysis and classical sequential methods require a larger feature set to attain the similar classification accuracy of 98%. The optimal texture features such as range of angular second moment, range of sum variance, range of information measure of correlation II and energy selected by the genetic algorithm provide best classification performance with lower computational effort.
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Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. ⋯ We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.