Medical engineering & physics
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Comparative Study
The comparison of different feed forward neural network architectures for ECG signal diagnosis.
The electrocardiograms (ECGs) record the electrical activity of the heart and are used to diagnose many heart disorders. This paper proposes a two-stage feed forward neural network for ECG signal classification. The research is aimed at the design of an intelligent ECG diagnosis tool that can recognise heart abnormalities while reducing the complexity, cost, and response time of the system. ⋯ The performance of the different modules as well as the efficiency of the whole system is presented. Among different architectures, a proposed multi-stage network named NET_BST possesses the highest recognition rate of around 93%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems.
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Controlled Clinical Trial
Analysis of EEG background activity in Alzheimer's disease patients with Lempel-Ziv complexity and central tendency measure.
In this study we have investigated the electroencephalogram (EEG) background activity in patients with Alzheimer's disease (AD) using non-linear analysis methods. We calculated the Lempel-Ziv (LZ) complexity - applying two different sequence conversion methods - and the central tendency measure (CTM) of the EEG in 11 AD patients and 11 age-matched control subjects. CTM quantifies the degree of variability, while LZ complexity reflects the arising rate of new patterns along with the EEG time series. ⋯ Our results show a decreased complexity of EEG patterns in AD patients. In addition, we obtained 90.9% sensitivity and 72.7% specificity at O1, and 72.7% sensitivity and 90.9% specificity at P3 and P4. These findings suggest that LZ complexity may contribute to increase the insight into brain dysfunction in AD in ways which are not possible with more classical and conventional statistical methods.
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In this paper, we propose a fast and automated navigation path generation algorithm to visualize inside of carotid artery using MR angiography images. The carotid artery is one of the body regions not accessible by real optical probe but can be visualized with virtual endoscopy. By applying two-phase adaptive region-growing algorithm, the carotid artery segmentation is started at the initial seed, which is located on the initially thresholded binary image. ⋯ Experiments have been conducted on both mathematical phantom and clinical data sets. This algorithm is more effective than key-framing and topological thinning method in terms of automated features and computing time. This algorithm is also applicable to generate the centerline of renal artery, coronary artery, and airway tree which has tree-like cylinder shape of organ structures in the medical imagery.
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The global compliance of a fixed-volume, incompressible compartment may play a significant role in determining the inherent vascular compliance. For the intracranial compartment, we propose that the free-displacement of the cerebral spinal fluid (CSF) directly relates to cerebral vascular compliance. To test this hypothesis, an in vivo surrogate intracranial compartment was made by enclosing a rabbit's kidney within a rigid, fluid-filled container. ⋯ The calculated C(app) for each experiment's closed-box state was favorably compared to a time-domain compliance assessment method at the mean heart rate. In addition, it was revealed that C(app) in the open-box state was greater than that in the closed-box state only when the calculations were performed at frequencies lower than the heart rate and closer to the ventilation rate. These experimental results support the concept that the vessel compliance of vascular systems enclosed within a rigid compartment is a function of the global compartment compliance.