Spinous slow complex wave detection model construction method and system

Abstract

The invention discloses a recurrent neural network and priori knowledge-based spinous slow complex wave detection model construction method. The method comprises the following steps: processing samples to obtain a training set, a verification set and a test set; performing artifact discrimination on the test set through an artifact filter, and outputting brain waves which are not artifacts to forma target test set; inputting the training set into a long-short-term memory model for training, calculating the probability value of whether the input training set is a spinous slow complex wave or not, and finally outputting a corresponding data label of which the probability value is greater than T according to a set probability threshold T; performing verification through a verification set toobtain a target long-short-term memory model; performing target model detection. According to the invention, neurons of a recurrent neural network are utilized to autonomously learn non-linear features which are not easy to design and describe artificially in spinous slow complex wave classification; pseudo-error filtering is carried out before detection, so that the accuracy of the model is improved; and by setting a threshold value, detection results with different precisions and recall rates are output according to different requirements.

Description

technical field [0001] The invention relates to the technical field of medical auxiliary detection, in particular to a method and a system for constructing a spike-slow complex wave detection model based on a cyclic neural network and prior knowledge. Background technique [0002] Electroencephalogram (electroencephalogram, EEG) reflects the electrical activity of brain nerve cell groups on the surface of the cerebral cortex or scalp. EEG records the continuous, spontaneous and rhythmic potential changes of brain nerve cell groups through electrodes placed on the cortex or scalp. When clinically detecting the symptoms of a certain brain disease, the medical staff will collect EEG signals for the patient through the EEG device. The conventional EEG recording time is 20-40 minutes. Sometimes for a comprehensive diagnosis, the patient will do a 24-hour EEG signal. Signal acquisition, and relying entirely on manual analysis and judgment of long-term EEG signals, not only brings...

AI Technical Summary

Problems solved by technology

However, most of the above methods require artificial design and screening features, which have certain limitations and low efficiency.

In particular, for different application scenarios, such as severe patients and mild patients, it is impossible to freely adjust the output of the corresponding precision, resulting in the loss of some important data or the retention of unnecessary data

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