Simulation of elevated T-waves of an ECG inside a static magnetic field (MRI).

In MRI, the flow of blood in the patient is subjected to a strong static magnetic field (B(0)). The movement of charge carriers in a magnetic field causes a magnetofluid dynamic (MFD) effect that induces a voltage across the artery. This induced voltage distorts the ECG signal of the patient and appears as an elevation of the T-wave of the ECG signal. Flow of blood through the aortic arch is perpendicular to the magnetic field and coincides with the occurrence of the T-wave of the ECG. Based on these facts, it is proposed that the elevation in the T-wave occurs because of the voltage induced across the aortic arch. In this paper, the elevation is computed mathematically using the equations of MFD. A method is developed to measure this induced voltage based on discretization of the aortic arch and measuring the blood flow profile in the aorta. The results are compared to the ECG signals measured in humans in the bore of 1.5 T imaging magnet. The computed ECG signals at the 12 leads are very similar to the measured values.

Gap-based estimation: choosing the smoothing parameters for probabilistic and general regression neural networks.

Probabilistic neural networks (PNN) and general regression neural networks (GRNN) represent knowledge by simple but interpretable models that approximate the optimal classifier or predictor in the sense of expected value of the accuracy. These models require the specification of an important smoothing parameter, which is usually chosen by cross-validation or clustering. In this article, we demonstrate the problems with the cross-validation and clustering approaches to specify the smoothing parameter, discuss the relationship between this parameter and some of the data statistics, and attempt to develop a fast approach to determine the optimal value of this parameter. Finally, through experimentation, we show that our approach, referred to as a gap-based estimation approach, is superior in speed to the compared approaches, including support vector machine, and yields good and stable accuracy.

Experiments with Safe muARTMAP : effect of the network parameters on the network performance.

Fuzzy ARTMAP (FAM) is currently considered to be one of the premier neural network architectures in solving classification problems. One of the limitations of Fuzzy ARTMAP that has been extensively reported in the literature is the category proliferation problem. That is, Fuzzy ARTMAP has the tendency of increasing its network size, as it is confronted with more and more data, especially if the data are of the noisy and/or overlapping nature. To remedy this problem a number of researchers have designed modifications to the training phase of Fuzzy ARTMAP that had the beneficial effect of reducing this category proliferation. One of these modified Fuzzy ARTMAP architectures was the one proposed by Gomez-Sanchez, and his colleagues, referred to as Safe muARTMAP. In this paper we present reasonable analytical arguments that demonstrate of how we should choose the range of some of the Safe muARTMAP network parameters. Through a combination of these analytical arguments and experimentation we were able to identify good default parameter values for some of the Safe muARTMAP network parameters. This feat would allow one to save computations when a good performing Safe muARTMAP network is needed to be identified for a new classification problem. Furthermore, we performed an exhaustive experimentation to find the best Safe muARTMAP network for a variety of problems (simulated and real problems), and we compared it with other best performing ART networks, including other ART networks that claim to resolve the category proliferation problem in Fuzzy ARTMAP. These experimental results allow one to make appropriate statements regarding the pair-wise comparison of a number of ART networks (including Safe muARTMAP).