Analyses of electroencephalographic signals and subsequent diagnoses can only be done effectively on long term recordings that preserve the signals' morphologies. Currently, electroencephalographic signals are obtained at Nyquist rate or higher, thus introducing redundancies. Existing compression methods remove these redundancies, thereby achieving compression. We propose an alternative compression scheme based on a sampling theory developed for signals with a finite rate of innovation (FRI) which compresses electroencephalographic signals during acquisition. We model the signals as FRI signals and then sample them at their rate of innovation. The signals are thus effectively represented by a small set of Fourier coefficients corresponding to the signals' rate of innovation. Using the FRI theory, original signals can be reconstructed using this set of coefficients. Seventy-two hours of electroencephalographic recording are tested and results based on metrices used in compression literature and morphological similarities of electroencephalographic signals are presented. The proposed method achieves results comparable to that of wavelet compression methods, achieving low reconstruction errors while preserving the morphologiies of the signals. More importantly, it introduces a new framework to acquire electroencephalographic signals at their rate of innovation, thus entailing a less costly low-rate sampling device that does not waste precious computational resources.
In this paper, we investigate the Stockwell Transform, a linear time-frequency spectral localisation technique, on non-stationary, multicomponent neonatal seizure EEG signals. The seizure signals of interest are namely slow wave and sharp spike seizures. The performance of Stockwell Transform is compared to that of existing quadratic time-frequency representation, namely the Choi-Williams Distribution and the B Distribution, on both simulated and real EEG seizure signals. The results show that the Stockwell Transform yields distinctive, interference free time-frequency patterns corresponding to the neonatal EEG seizure signals. By capturing both high-frequency spike components and predominantly low frequency components of neonatal seizures concurrently and accurately, the Stockwell Transform is able to distinguish these two types of neonatal seizure signals with unique signatures. These signatures can then be effectively used for seizure modelling, detection and prediction.
Analyses of neonatal EEG seizures and subsequent diagnoses can only be done effectively on long-term recordings on the condition that the morphology of the EEG signals are retained. Therefore, a reliable, accurate and efficient compression and reconstruction technique is necessary to store and retrieve the data. In this paper, we propose a new compression technique for neonatal EEG seizure signals via sampling theory developed for signals with a finite rate of innovation. Firstly, the EEG seizure signals are modeled as periodic nonuniform linear splines. Next, through the sampling and reconstruction scheme developed for signals with finite rate of innovation, we show that neonatal EEG seizure signals can be highly compressed while preserving their morphologies.
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