Classification method of electroencephalogram signal

Abstract

The invention relates to a classification method of an electroencephalogram signal. The method comprises the following steps: 1, decomposing an electroencephalogram signal into sum of intrinsic mode functions; 2, performing experience amplitude modulation-frequency modulation decomposition to each intrinsic mode function to obtain an experience frequency modulation component; 3, judging whether the obtained experience frequency modulation component contains a riding wave or not; 4, removing the riding wave if the riding wave is contained in the experience frequency modulation component; 5, calculating an experience amplitude modulation component; 6, calculating an orthogonal component of the experience frequency modulation component; 7, calculating an instantaneous phase; 8, calculating an amplitude modulation bandwidth and a frequency modulation bandwidth; and 9, classifying the electroencephalogram signals by taking the amplitude modulation bandwidth and the frequency modulation bandwidth as input of a support vector machine. The method is not restricted by Hilbert transform of signal product, avoids generation of new riding waves, has good local characteristics, and is improvement to defects of a conventional electroencephalogram signal classification method.

Description

technical field [0001] The invention relates to a method for classifying electroencephalogram signals, which can be applied to the analysis and processing of non-stationary nonlinear signals such as medical electroencephalogram signals, and belongs to the field of modern signal processing. Background technique [0002] Electroencephalogram (EEG) signal is a signal containing a large amount of information about brain activity after measuring and recording the electrical activity of hundreds of millions of neurons around the guide electrode. An important clinical tool for the diagnosis and detection of neurological diseases. [0003] The extraction of EEG signal characteristic parameters is of great significance to the diagnosis of neurological diseases. For example, the frequency spectrum based on Fourier transform is an important feature for the detection and diagnosis of epilepsy. However, the Fourier transform is a global transformation, which cannot describe the time-fr...

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Problems solved by technology

However, this method has two obvious disadvantages: 1. When the experience decomposition is performed again, new riding waves may be introduced and the number of riding waves may increase, thus requiring multiple iterations of riding wave reversal and experience decomposition; 2. This method does not have good local characteristics, because the empirical AM-FM decomposition uses the envelope of the signal to remove the signal and normalize it. When performing empirical decomposition, where the envelope value is not 1, the original normalization The empirical FM component F(t) of will be distorted

However, the envelope of the signal fitted by the cubic spline may not be 1 at many points outside the riding wave, so distortion may occur at these points

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