A Novel Method for Early Seizure Detection in the EEG Based on Fragmentary Decomposition

ABSTRACT

Timely detection of seizure occurrence in epileptic patients and alerting caregivers for prompt intervention would drastically improve the quality of life of epileptic patients and reduce mortality. Despite remarkable developments in this field, there are no, to our knowledge, algorithms that could reliably and promptly detect the seizure occurrences by real-time electroencephalographic (EEG) analysis.

We propose a novel method for real-time electrographic seizure detection. The key part of the method is decomposition of the EEG signal into elementary components (fragmentary decomposition, FD), using original technique of short-term Fourier transform. FD creates accurate explicit model of the signal and provides opportunity for computer reconstructions of different sets of defined signal components. This approach provides a more elaborate way for waveform analysis which identifies specific shape of each peak in the time course of non-stationary signal. The components of the model signal are then processed by an original temporal pattern recognition algorithm, which may be tuned for recognition of any specific combination of model components.

The method was successfully tested on human intracranial and scalp EEG. In most cases the seizures were detected well before they gain the full strength – mostly before the spike phase initiates, and always before the spikes’ amplitudes reach large values. In some patients unique precursor events preceded seizure onset for several seconds. In these cases, the detection of the precursor events in fact turns into accurate seizure prediction.

The proposed method may be used for real-time fast and reliable detection of electrographic seizures in the EEG. Its ability to detect short epileptiform events and other specific patterns (which may be small in amplitude) makes the method useful in research directed to seizure prediction and in seizure detection device applications.