Wireless dynamic anesthesia depth detection method based on lattice complexity algorithm

Abstract

The invention discloses a wireless dynamic anesthesia depth detection method based on a lattice complexity algorithm. The method is based on a following anesthesia depth monitoring device; the anesthesia depth monitoring device comprises a collection module, a wireless transmission module, a data processing module, a central processing module and a display module, wherein the collection module is used for collecting an EEG (Electroencephalogram) signal, amplifying the simulated EEG signal and converting the EEG signal into digital EEG signal data; the wireless transmission module is used for uploading the digital EEG signal data output by the collection module to the data processing module for being preprocessed through WiFi (Wideless Fidelity); the preprocessed data can be input into the central processing module for being analyzed and processed, the lattice complexity, the marginal frequency and the burst suppression ratio can be respectively calculated, and an anesthesia depth index can be obtained and displayed by utilizing a decision tree algorithm in a fitting way. According to the wireless dynamic anesthesia depth detection method disclosed by the invention, since the wireless transmission module and the lattice complexity algorithm are used, inconvenience brought by guide lines of a traditional detection device is removed, the algorithm is simple, and the instantaneity is strong.

Description

technical field [0001] The invention relates to an anesthesia depth detection device, in particular to an anesthesia depth detection device for an intensive care unit which includes a lattice complexity algorithm and wireless transmission. Background technique [0002] Anesthesia refers to the disappearance of the whole body or local sensation and the state of memory amnesia produced by means of drugs and other methods. It can ensure the smooth progress of the operation. Too deep or too shallow anesthesia will cause harm to the patient. Therefore, monitoring the depth of anesthesia is particularly important. Anesthesia achieves the purpose of loss of consciousness and pain relief by causing reversible inhibition and excitation of the central nervous system. The EEG can directly reflect the activity of the central nervous system. Therefore, EEG technology has become one of the best means to determine the depth of anesthesia. Many time-domain and frequency-domain parameters...

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Problems solved by technology

[0008] At the same time, considering that the equipment is used in the int

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