Electroencephalogram abnormal signal detection device and method

Abstract

The invention discloses an electroencephalogram abnormal signal detection device and method. The device comprises an electroencephalogram signal preprocessing unit which is used for obtaining an original electroencephalogram signal and carrying out the denoising of the original electroencephalogram signal, and obtaining a target electroencephalogram signal, a wavelet decomposition and reconstruction unit which is used for acquiring the target electroencephalogram signal, and performing X-layer decomposition by adopting Daubechies wavelets according to the coverage frequency of the abnormal waveform and the sampling frequency of the electroencephalogram detection equipment to obtain X-layer frequency bands and characteristic components of each frequency band, a nonlinear kinetic parameter estimation unit which is used for calculating sample entropy characteristics of the electroencephalogram signals of each frequency band after wavelet decomposition, a normalization unit which is used for carrying out normalization processing on the feature components and the sample entropy features to obtain feature vectors, and a detection and classification unit which is used for detecting and classifying the feature vectors. According to the method, features after wavelet transform and features of nonlinear dynamics are combined, comprehensive consideration is carried out, and classificationdetection is carried out on final waveforms.

Description

technical field [0001] The invention relates to the technical field of medical equipment, in particular to an abnormal EEG signal detection device and detection method. Background technique [0002] Due to the characteristics of the EEG signal itself and the diversity of abnormal brain discharge components, and the EEG signal is a non-stationary, nonlinear random weak physiological electrical signal, the amplitude is generally around 100μV, and it is mixed with various power frequencies. Noise and Artifact Signals. The conventional EEG recording time is 20-40 minutes, and even 24 hours of EEG signals are collected under special circumstances. With such a large amount of data, it is completely dependent on manual analysis and judgment. The workload is very heavy and the work efficiency is low. [0003] With the development of science and technology, EEG signal detection equipment emerges in an endless stream. The current mainstream EEG signal detection equipment mainly has ...

AI Technical Summary

Problems solved by technology

The current mainstream EEG signal detection equipment mainly has the function of collecting and recording EEG signals, but usually does not have the function of analyzing EEG signals.

At the same time, due to the non-stationary and nonlinear nature of the EEG signal, it is difficult to effectively characterize its characteristics using traditional time-frequency analysis methods, and it is impossible to perform high-precision classification and detection of EEG signals.

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