Sleep staging method based on EEG time domain multi-dimensional features and M-WSVM, and wearable device

A sleep staging and multi-dimensional technology, applied in medical science, diagnosis, diagnostic recording/measurement, etc., can solve problems such as being susceptible to interference, and achieve the effect of small number of features, easy extraction, and good practical performance

Active Publication Date: 2019-03-26
WUHAN UNIV
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Problems solved by technology

[0005] Due to the weak nature of the EEG signal and its susceptibility to interference, it poses a great challenge to the effective acquisition of the signal. At the same time, the application of advanced artificial intelligence technology and methods on lightweight mobile devices also has certain deficiencies.

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  • Sleep staging method based on EEG time domain multi-dimensional features and M-WSVM, and wearable device
  • Sleep staging method based on EEG time domain multi-dimensional features and M-WSVM, and wearable device
  • Sleep staging method based on EEG time domain multi-dimensional features and M-WSVM, and wearable device

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Embodiment Construction

[0070] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0071] In this embodiment, the time-domain multi-dimensional feature extraction of the EEG signal first needs to perform amplitude-time mapping on the signal to obtain relevant feature information. The specific embodiment is to map the collected continuous EEG signal to the amplitude axis and the time axis, such as figure 1 As shown, each numerical point in the signal sample is mapped to the amplitude axis, and the unique numerical point, the number of numerical points, and the average interval of the numerical points appearing in the signal sample are calcula...

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Abstract

The invention discloses a sleep staging method based on EEG time domain multi-dimensional features and M-WSVM, and a wearable device. The method comprises the following steps: acquiring EEG continuoustime signals, and extracting time domain multi-dimensional features of the EEG continuous time signals by amplitude-time mapping; selecting the extracted signal features to obtain optimal signal features; and analyzing and processing the sleep stage at different classification levels by using an M-WSVM algorithm, and monitoring the sleep staging in real time. The device comprises a signal collecting module, a signal processing module and a signal transmission module, and can communicate with a user end of an intelligent device in real time, model learning of the EEG training data is carried out on a PC end, and a learning algorithm model is transplanted on the intelligent device to monitor the sleep staging in real time. The method for extracting and classifying the features of the EEG signals is used to simplify the complexity of the sleep staging, and a physiological signal measurement circuit is used to develop the wearable sleep staging device in order to obtain thee real-time andhigh-precision automatic sleep staging effect.

Description

technical field [0001] The invention relates to the field of automatic sleep staging of intelligent algorithms, in particular to a sleep staging method and a wearable device based on EEG time-domain multi-dimensional features and M-WSVM. Background technique [0002] With the proposal of the national general health strategy and the advancement of artificial intelligence technology, the intelligent health industry will make great progress. Based on EEG sleep staging research, it is of great significance for the diagnosis, treatment and prevention of sleep diseases. At present, in the study of sleep staging of EEG signals, the application of various signal transformation technologies and machine learning methods has achieved certain results, especially the study of sleep staging of single-channel EEG signals, which is easier to achieve sleep on wearable devices. For monitoring and analysis, literatures [1-9] have studied sleep staging using single-channel EEG signals. However...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/00A61B5/0476
CPCA61B5/4806A61B5/6802A61B5/72A61B5/369
Inventor 袁志勇安攀峰林远轩
Owner WUHAN UNIV
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