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.