High-frequency oscillation rhythm detection method based on optimal fuzzy clustering Gaussian mixture model

Abstract

The invention provides a high-frequency oscillation rhythm detection method based on an optimal fuzzy clustering Gaussian mixture model. A method based on clustering analysis is adopted to detect thehigh-frequency oscillation rhythm, fuzzy entropy, short-time energy, a power ratio and a frequency spectrum center of mass are selected as the characteristics of an epilepsy electroencephalogram, thecharacteristics are formed into a characteristic vector as the input of a clustering algorithm, an expectation-maximization Gaussian mixture model clustering algorithm is adopted to classify the characteristic vector, and a fuzzy c-mean clustering algorithm is adopted to obtain the initialization parameter of the expectation-maximization Gaussian mixture model clustering algorithm; and a median and a quartile range are selected to analyze each category of statistics characteristics, and the high-frequency oscillation rhythm is detected. The method has the beneficial effects that the detectionspeed of the high-frequency oscillation rhythm of the epilepsy electroencephalogram is improved, and doctors are assisted in carrying out 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 fuzzy clustering optimization Gaussian mixture model. Background technique [0002] Epilepsy is a common neurological disorder affecting approximately 1% of the world's population. At present, there are more than 9 million epilepsy patients in my country, and the number is still increasing at a rate of 650,000-700,000 per year. Most people with epilepsy are treated with antiepileptic drugs. Since the discovery of phenobarbital in the treatment of epilepsy at the beginning of the last century, antiepileptic drugs such as phenytoin, ethosuximide, carbamazepine, and valproic acid have been continuously released, which can control the seizures of 70% of epileptic patients. Most patients can no longer get sick after conventional drug treatment. However, 30% of the patients are still diagnosed as intractable ...

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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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