James Pardey

A review of parametric modelling techniques for EEG analysis

This review provides an introduction to the use of parametric modelling techniques for time series analysis, and in particular the application of autoregressive modelling to the analysis of physiological signals such as the human electroencephalogram. The concept of signal stationarity is considered and, in the light of this, both adaptive models, and non-adaptive models employing fixed or adaptive segmentation, are discussed. For non-adaptive autoregressive models, the Yule-Walker equations are derived and the popular Levinson-Durbin and Burg algorithms are introduced. The interpretation of an autoregressive model as a recursive digital filter and its use in spectral estimation are considered, and the important issues of model stability and model complexity are discussed.

A new approach to the analysis of the human sleep/wakefulness continuum.

SUMMARY The conventional approach to the analysis of human sleep uses a set of pre‐defined rules to allocate each 20 or 30‐s epoch to one of six main sleep stages. The application of these rules is performed either manually, by visual inspection of the electroencephalogram and related signals, or, more recently, by a software implementation of these rules on a computer. This article evaluates the limitations of rule‐based sleep staging and then presents a new method of sleep analysis that makes no such use of pre‐defined rules and stages, tracking instead the dynamic development of sleep on a continuous scale. The extraction of meaningful features from the electroencephalogram is first considered, and for this purpose a technique called autoregressive modelling was preferred to the more commonly‐used methods of band‐pass filtering or the fast Fourier transform. This is followed by a qualitative investigation into the dynamics of the electroencephalogram during sleep using a technique for data visualization known as a self‐organizing feature map. The insights gained using this map led to the subsequent development of a new, quantitative method of sleep analysis that utilizes the pattern recognition capabilities of an artificial neural network. The outputs from this network provide a second‐by‐second qualification of the sleep/wakefulness continuum with a resolution that far exceeds that of rule‐based sleep staging. This is demonstrated by the neural networks ability to pinpoint micro‐arousals and highlight periods of severely disturbed sleep caused by certain sleep disorders. Both these phenomena are of considerable clinical value, but neither are scored satisfactorily using rule‐based sleep staging.

Parallel controller synthesis for programmable logic devices

Abstract The standard approach to parallel controller design uses sequential controller design techniques. However, since these techniques cannot represent concurrent states, the problem must first be partitioned and then designed as a number of linked finite state machines. This initial partitioning is usually non-optimum and limits the amount of concurrency in the subsequent design, where the interaction between the finite state machines also makes verification difficult. This paper presents an alternative technique for parallel controller design in which a synchronous, interpreted Petri net is used to model the controllers specification as a single parallel network. No pre-partitioning is necessary, and the amount of concurrency dynamically reflects the amount of parallel activity on the data path. The formalism provided by the technique also reduces the likelihood of parallel synchronization errors. The technique is illustrated with the design of a parallel controller for a transputer link adapter and its implementation on a programmable logic device.