Method for extracting and fusing time, frequency and space domain multi-parameter electroencephalogram characters

Abstract

The invention relates to a method for extracting and fusing time, frequency and space domain multi-parameter electroencephalogram characters, which comprises the following steps: 1) collecting an electroencephalogram signal; 2) performing data pre-processing on the electroencephalogram signal; 3) extracting Kc complexity, approximate entropy and wavelet entropy from the pre-processed data; 4) on the basis of AMUSE algorithm, acquiring an electroencephalogram singular value decomposition matrix parameter; 5) performing character selection on the time, frequency and space domain character parameters for the extracted Kc complexity, approximate entropy, wavelet entropy and electroencephalogram singular value decomposition matrix parameters; 6) utilizing a SVM classifier to fuse and classify the four parameters of the time, frequency and space domains after the character selection. According to the method provided by the invention, the Kc complexity, the approximate entropy, the wavelet entropy and the electroencephalogram singular value decomposition matrix parameter can be selected for comprehensively presenting electroencephalogram character information, and then subsequent effective fusion is performed, so that effective support and help can be supplied to early diagnosis assessment for the brain functional disordered diseases, such as, Alzheimer disease, mild cognitive impairment, and the like.

Description

technical field [0001] The invention belongs to the technical field of EEG signal feature extraction and fusion, in particular to a method for time, frequency, and spatial multi-parameter EEG feature extraction and fusion. Background technique [0002] EEG can comprehensively reflect the overall effect and functional state of the electrophysiological activities of the brain nervous system. Among the many detection and evaluation methods for cognitive impairment, the evaluation method based on EEG signal features is more and more widely used in cognitive impairment due to its unique advantages of non-invasiveness, high temporal resolution, and emphasis on temporal information transmission and processing. know the field of research. As a special bioelectrical signal, EEG signal contains rich physiological and pathological information, which provides an effective means for the diagnosis of clinical brain diseases, especially early diagnosis. [0003] In the EEG signal analysi...

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Problems solved by technology

[0008] Aiming at the above problems, the present invention designs a time, frequency, and spatial multi-parameter EEG feature extraction and fusion method, which can effectively solve the problem that multi-domain feature information cannot be fully extracted and effectively selected during the process of EEG feature extraction, and the fused feature information underused problem

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