A high-frequency oscillation rhythm detection method based on intelligent algorithm-optimized fuzzy clustering

Abstract

The invention provides a high-frequency oscillation rhythm detection method based on intelligent algorithm optimization fuzzy clustering, and detects the high-frequency oscillation rhythm based on the fuzzy clustering method. The basic steps are as follows: select the average singular value MSV, line length f l , power ratio R and spectral centroid f c is the feature of the epileptic EEG signal, and its constituent feature vector is used as the input of the clustering algorithm; the simulated annealing genetic algorithm in the intelligent algorithm is used to optimize the fuzzy clustering algorithm, and the optimized parameter v c ; According to the optimization parameter v c , to obtain the optimized results; select the median and interquartile range to analyze the statistical characteristics of each category, and detect the high-frequency oscillation rhythm. The beneficial effect of the present invention is to improve the detection accuracy of the high-frequency oscillation rhythm of epileptic EEG signals, and help doctors to diagnose epilepsy and excise epileptogenic foci.

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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