Epileptic focus positioning data processing method and system and storage medium

Abstract

The invention discloses an epileptic focus positioning data processing method and system and a storage medium. The method includes the steps: acquiring electroencephalogram data and magnetic resonance imaging data of a patient; constructing a head model of the patient according to the magnetic resonance imaging data of the patient; screening the electroencephalogram data of the patient by a sparse Bayesian algorithm; performing tracing and positioning on the head model of the patient according to the screened electroencephalogram data and displaying an epileptic focus on the head model of the patient. The head model of the patient is constructed according to the magnetic resonance imaging data, the electroencephalogram data are screened by the sparse Bayesian algorithm, finally, tracing and positioning are performed on the head model of the patient according to the screened electroencephalogram data, and the epileptic focus is displayed, so that dependency of a focus analysis process on related clinical doctors or professional electroencephalographers is reduced, diagnosis time is shortened, and diagnosis expenses are reduced. The method, the system and the storage medium can be widely applied to the technical field of disease data processing.

Description

technical field [0001] The invention relates to the technical field of disease data processing, in particular to a processing method, system and storage medium for epileptic focus location data. Background technique [0002] Glossary: [0003] Sparse Bayesian learning is a machine learning algorithm that has been applied in the field of sparse signal restoration and compressed sensing. In the field of compressed sensing, it can use the principle of compressed sensing to restore ideal images. [0004] Epilepsy is a chronic disease in which the sudden abnormal discharge of brain neurons leads to transient brain dysfunction. Precise localization of epilepsy foci plays an important role in lesion resection. The current clinical location of epileptic lesions is mainly based on a large number of imaging methods, including video computer, magnetic resonance imaging, positron reflection tomography, neuropsychological assessment, etc. EEG specialists analyze the data, which takes ...

AI Technical Summary

Problems solved by technology

The current clinical location of epileptic lesions mainly uses a large number of imaging methods, including video computer, magnetic resonance imaging, positron reflection tomography, neuropsychological assessment, etc. These lesions location methods require relevant clinicians or professional EEG specialists analyze the data, which takes a long time, complicated methods and high cost

In addition to the above lesion localization methods, it also includes the minimum norm method and standard low-resolution electromagnetic tomography. Among them, although the minimum norm method can also present weak signals in the visualization of brain activation, it is easy to detect epilepsy. The judgment of the location of the lesion interferes; the standard low-resolution electromagnetic tomography scan is too "sensitive" to epileptic signals, and the visualized brain activation shows that a whole area is in a very active state, making it difficult to judge the epileptic focus specific location of

[0005] To sum up, the existing information processing methods for the localization of epileptic lesions lead to long diagnosis time and high cost for patients, cumbersome operation process for doctors, and the influence of weak signals on the lesion localization process cannot be avoided.

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