Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

The identification of sub-pixel components from remotely sensed data: an evaluation of an artificial neural network approach

Until recently, methodologies to extract sub-pixel information from remotely sensed data have focused on linear un-mixing models and so called fuzzy classifiers. Recent research has suggested that neural networks have the potential for providing sub- pixel information. Neural networks offer an attractive alternative as they are non- parametric, they are not restricted to any number of classes, they do not assume that the spectral signatures of pixel components mix linearly and they do not necessarily have to be trained with pure pixels. The thesis tests the validity of neural networks for extracting sub-pixel information using a combination of qualitative and quantitative analysis tools. Previously published experiments use data sets that are often limited in terms of numbers of pixels and numbers of classes. The data sets used in the thesis reflect the complexity of the landscape. Preparation for the experiments is canied out by analysing the data sets and establishing that the network is not sensitive to particular choices of parameters. Classification results using a conventional type of target with which to train the network show that the response of the network to mixed pixels is different from the response of the network to pure pixels. Different target types are then tested. Although targets which provide detailed compositional information produce higher accuracies of classification for subsidiary classes, there is a trade off between the added information and added complexity which can decrease classification accuracy. Overall, the results show that the network seems to be able to identify the classes that are present within pixels but not their proportions. Experiments with a very accurate data set show that the network behaves like a pattern matching algorithm and requires examples of mixed pixels in the training data set in order to estimate pixel compositions for unseen pixels. The network does not function like an unmixing model and cannot interpolate between pure classes