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Method for detecting obstructive apnea and night frontal lobe epilepsy by using sleep structure

An apnea and structure detection technology, which is applied in the detection of obstructive apnea and nocturnal frontal lobe epilepsy, can solve the problems that the diagnosis results are prone to deviation, cannot directly reflect the patient's sleep stage and sleep structure, etc., so as to improve the generalization ability, The effect of improving robustness

Pending Publication Date: 2022-04-01
CHENGDU UNIV OF INFORMATION TECH
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Problems solved by technology

Although these signals are convenient to collect, they do not directly reflect the sleep stage and sleep structure of the patient, so the diagnostic results are prone to bias

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  • Method for detecting obstructive apnea and night frontal lobe epilepsy by using sleep structure
  • Method for detecting obstructive apnea and night frontal lobe epilepsy by using sleep structure
  • Method for detecting obstructive apnea and night frontal lobe epilepsy by using sleep structure

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Embodiment Construction

[0041] In sleep disorders, there are differences between the sleep structure and the normal periodic structure, and for the diseases of different sleep disorders due to their different pathogenesis, the sleep structure may also be different. Therefore, the present invention attempts to obtain the sleep structure through the automatic sleep model. Detect OSA and NFLE two sleep disorders. First, through the automatic sleep staging model and transfer learning, the single-channel EEG sleep staging datasets of OSA patients and NFLE patients were migrated to the single-channel EEG datasets of the main model healthy people to obtain their sleep structures. Secondly, use ExtraTree, RF, and Xgboost three classifiers to complete OSA disease detection, NFLE disease detection, and OSA and NFLE disease detection. And use the idea of ​​ensemble learning to further improve the performance of sleep staging. Our method can not only realize the automation from EEG to sleep disease detection, b...

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Abstract

The invention discloses a method for detecting obstructive apnea and night frontal lobe epilepsy by using a sleep structure. The method comprises the following steps: S1, data acquisition: respectively acquiring sleep data of a healthy person, an OSA patient and an NFLE patient; s2, data preprocessing; s3, establishing an automatic sleep staging network model, and performing staging processing on the sleep data of the healthy people by using the automatic sleep staging network model; s4, carrying out transfer learning to obtain sleep staging structures of the OSA patient and the NFLE patient; and S5, establishing a sleep disease detection model. According to the method for detecting the OSA and NFLE diseases by using the sleep structure, the OSA and NFLE sleep diseases can be effectively detected from the sleep structure level, and the model has the practical application potential.

Description

technical field [0001] The invention relates to a method for detecting obstructive apnea and nocturnal frontal lobe epilepsy by using sleep structure. Background technique [0002] Sleep is essential to human health. According to the rules of the American Association of Sleep Medicine (AASM), sleep stages can be labeled as 5 stages: wakefulness, rapid eye movement (REM), non-REM sleep stages 1 (N1), 2 (N2) and 3 (N3), while the entire The sleep stage structure chart at night is an important indicator of sleep quality and health status. In the normal sleep state, the sleep stage cycles from N1 to REM, and then starts from N1 again, and the cycle repeats. A complete sleep structure cycle takes an average of 90 to 110 minutes, with each stage lasting 5 to 15 minutes. When patients have sleep disorders, the sleep structure also presents different degrees of abnormality or disorder. Therefore, the sleep structure chart is a preliminary step for sleep doctors to diagnose sleep ...

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Application Information

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IPC IPC(8): G16H50/20G06K9/00G06K9/62G06N3/04G06N20/00A61B5/00
Inventor 郜东瑞冯李霄张丽彭波曹文朋严明靖汪曼青张永清
Owner CHENGDU UNIV OF INFORMATION TECH
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