High-frequency oscillation rhythm detection method intelligent algorithm optimized fuzzy clustering

Abstract

The invention provides a high-frequency oscillation rhythm detection method intelligent algorithm optimized fuzzy clustering to detect high-frequency oscillation rhythms on the basis of the fuzzy clustering method. The method comprises basic steps as follows: selecting a mean singular value (MSV), a wire length fl, a power ratio and a frequency spectrum centroid as features of an epilepsy electroencephalogram (EEG), and constructing the features into a feature vector to be input as a clustering algorithm; optimizing a fuzzy clustering algorithm by a simulated annealing genetic algorithm in anintelligent algorithm to obtain an optimized parameter vc; obtaining an optimized result according to the optimized parameter vc; and selecting medians and interquartile ranges to analyze statisticalfeatures of each class to detect the high-frequency oscillation rhythms. The method has the beneficial effects as follows: the detection precision of high-frequency oscillation rhythms of epilepsy EEGis improved and doctors are helped to perform epilepsy diagnosis and epileptogenic focus excision.

Description

technical field [0001] The invention relates to the field of epilepsy EEG signal processing, in particular to a high-frequency oscillation rhythm detection method based on intelligent algorithm optimization fuzzy clustering. Background technique [0002] Epilepsy is a common neurological disorder characterized by spontaneity and unpredictability. During a seizure, people often show abnormalities in movement and behavior. The prevalence of epilepsy in the world is 0.5%-1%, of which it is 0.7% in my country, and it is still increasing year by year. Most epilepsy patients can be treated with antiepileptic drugs, but there are still about 30% of patients whose seizures cannot be controlled by drugs, so they are diagnosed as intractable epilepsy patients. [0003] Refractory epilepsy refers to conventional and systematic antiepileptic drug treatment, the concentration of antiepileptic drugs in the blood remains within the effective range, but the patient's seizures cannot be co...

AI Technical Summary

Problems solved by technology

However, the low-frequency frequency components detected by this method are easily interfered by other signals, which makes the positioning result wrong and leads to the failure of the resection operation.

At the same time, this method is very time-consuming, and the positioning time is about 24-72 hours, which increases the risk of surgery

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