Scalp electroencephalogram (EEG) retrospective epileptic seizure point detection method and system

Abstract

The invention belongs to the technical field of scalp electroencephalogram (EEG), and presents a scalp EEG retrospective epileptic seizure point detection method and system. The method is as follows: retrospective analysis is carried out on EEGs with various artifact EEGs being removed through a non-linear dynamics sample entropy threshold value detection method to determine the epileptic seizure point. The scalp EEG retrospective epileptic seizure point detection system includes an EEG receiving module, an epileptic seizure point determining module, and an information output module, wherein the EEG receiving module is used for receiving original EEG collected clinically; the epileptic seizure point determining module is used for analyzing and determining the retrospective epileptic seizure point through the EEG received by the EEG receiving module; the information output module is used for outputting the retrospective epileptic seizure point determined by the epileptic seizure point determining module. According to the scalp EEG retrospective epileptic seizure point detection method and system, the EEG data can be demixed in 10s, so that the epileptic seizure point can be quickly determined and the effect is obvious.

Description

technical field [0001] The invention relates to the technical field of scalp electroencephalogram signals, in particular to a method and system for retrospectively detecting epileptic seizure points of scalp electroencephalogram signals. Background technique [0002] Epilepsy is a disease of nervous system disorder. According to literature reports, the incidence of the disease in the population is about 0.5% to 2%. It is characterized by disorder and brings great inconvenience to the lives of patients. Electroencephalogram (EEG) examination is a common technique for epilepsy diagnosis and focus location in clinical practice at present, and the analysis of epilepsy by EEG is easily accepted by doctors and patients. The time resolution of the EEG signal is high, and it can accurately reflect the time-varying characteristics of the brain at the millisecond level. The 24-hour long-range EEG signal is an important basis for the diagnosis of epilepsy. However, in the face of suc...

AI Technical Summary

Problems solved by technology

The 24-hour long-range EEG signal is an important basis for the diagnosis of epilepsy. However, in the face of such a large amount of data, the next step of analysis is still based on manual visual inspection to search for seizure points. The workload is huge and the rate of misjudgment is high.

[0003] However, since the human body is a complex network system, the EEG will inevitably be disturbed by some irrelevant electrophysiological signals such as oculoelectricity, myoelectricity and electrocardiogram. Therefore, clinically collected EEG signals often contain a large number of artifacts, and because the brain neural network is an interconnected network, there will be crosstalk between the EEG signals of different channels, so that the EEG signals of each channel will eventually appear as an aliasing signal

The phenomenon of crosstalk brings inconvenience to the analysis of EEG, and may even lead to wrong results

Some existing epilepsy localization detection methods will reduce the accuracy due to the influence of signal quality

[0004] For the research methods of information in EEG signals, time-domain frequency-domain analysis and probability statistics analysis, but time-domain methods such as the detection of spikes and sharp waves, and probability statistics methods such as neural networks and principal component analysis cannot cover the epileptic brain. most features of electricity

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