Method for identification of pathological brain activity from scalp electroencephalogram

Abstract

The present invention relates to a computer-implemented method for detecting pathological brain activity patterns from a scalp electroencephaiographic signal, the method comprising the steps of obtaining (A) an electroencephaiographic signal as a function of multiple channels and time; identifying (C), for each channel, the zero- crossings of the electroencephaiographic signal over a fixed threshold; generating a zero-crossing representation of at least a segment of the obtained electroencephaiographic signal with the identified zero-crossings; obtaining (D) a reference family of real functions of time and channels from a zero-crossing statistical analysis of zero-crossing representation of pre-recorded electroencephaiographic signals; calculating (E) a matching score by comparing said zero-crossing representation of a segment of the electroencephaiographic signal with at least one reference function from the reference family of functions; and computing the matching score as a functionof time by sliding the at least one reference function from the reference family of functions over the electroencephaiographic signal.

Description

technical field [0001] The invention relates to a system and method for analyzing scalp electroencephalogram. In particular, the present invention relates to a method for identifying pathological brain activity patterns in a subject using a non-linear classification method. Background technique [0002] Scalp electroencephalography, which uses electrodes placed on the surface of the scalp to record electrical brain activity, is a fundamental tool in the diagnosis and study of neurological disorders, including epilepsy. [0003] Traditionally, clinical examination of scalp EEG has relied on visual assessment of the morphology and spatial distribution of waveform patterns recorded in multiple channels, a process that is often time-consuming and inefficient. The method of the present invention enables automatic detection of pathological brain activity patterns, such as low-amplitude scalp manifestations of interictal epileptiform discharges, as well as other neurophysiological...

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Problems solved by technology

[0021] However, as in US 6,442,421, the method disclosed by Pyrzowski et al., in particular steps d) and e), leads to a loss of information about the recorded phase relationship existing between the different channels

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