Machine learning algorithm for sleep staging by applying prefrontal lobe single-channel electroencephalogram signals
An EEG signal, sleep staging technology, applied in applications, sensors, medical science, etc., can solve the problems of affecting sleep, inconvenience, cumbersome wearing methods, etc., to achieve the effect of convenient collection and wearing, and noise removal.
Pending Publication Date: 2021-11-02
浙江柔灵科技有限公司
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Problems solved by technology
These two methods will cause a lot of inconvenience for ordinary people's sleep monitoring, and the wearing method is also relatively cumbersome, and even affect sleep
Method used
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Embodiment
[0041] Embodiment: first attach the flexible patch electrode to the Fp1-Fp2 position of the frontal lobe of the monitor, then use the PSG device to collect data signals, and then according to the collected signals according to the corresponding marked labels, divide them into: awake, rapid eye The four categories of active period, light sleep and deep sleep, the collected signal is filtered and removed artifacts, 30s segmentation processing and feature extraction after a series of processing, the extracted features and corresponding labels are sent to the XGBoost model for training ; In the XGBoost model, the objective function is set as the softmax function, and the sleep staging diagram is drawn according to the prediction results given by the model, such as image 3 shown;
[0042] The monitoring time period is 00:50-06:30. In the figure, Wake is the awake stage, REM is the rapid eye movement stage, Light is the light sleep stage, and Deep is the deep sleep stage.
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The invention discloses a machine learning algorithm for sleep staging by applying prefrontal lobe single-channel electroencephalogram signals. The machine learning algorithm comprises training model establishment and final output prediction, the training model establishment comprises the four stages of Fp1-Fp2 electroencephalogram data acquisition, filtering and artifact removal, 30s segmentation processing and feature extraction, and finally the training model is established; and Fp1-Fp2 electroencephalogram data collection, filtering and artifact removal, 30s segmentation processing and feature extraction are also needed for final output prediction, finally, the extracted features are sent into the established training model to obtain final output prediction, and the collection of the Fp1-Fp2 electroencephalogram data is to place a flexible patch electrode at the Fp1-Fp2 position of the forehead lobe of the human brain to collect electroencephalogram signals. The machine learning algorithm has the advantages that the Fp1-Fp2 forehead lobe single-channel electroencephalogram adopted by the device is located at the forehead of the human brain, a flexible patch electrode is used as an acquisition electrode, acquisition and wearing are very convenient, and night sleep of a wearer cannot be affected by nearly non-inductive wearing.
Description
technical field [0001] The invention relates to a machine learning algorithm for sleep staging, in particular to a machine learning algorithm for sleep staging by applying a prefrontal lobe single-channel electroencephalogram signal, and belongs to the technical field of sleep staging. Background technique [0002] Sleep monitoring, like heart rate, blood oxygen, and blood pressure and other daily physiological indicators, is one of the very important daily monitoring items. Sleep monitoring is not only needed by people with sleep disorders, but also by many ordinary people and some patients with chronic diseases. Sleep staging based on EEG will be much more accurate than methods based on ECG or pulse rate, body movement, etc. Currently, sleep monitoring methods based on EEG signals are mainly based on multiple channels, such as EEG, EOG, and EMG, or a single channel based on the position of the central axis of the head, such as Fpz-Oz, Pz-Cz and other single-channel EEG si...
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Login to View More IPC IPC(8): A61B5/369A61B5/374A61B5/00A61B5/291A61B5/257
CPCA61B5/369A61B5/374A61B5/7235A61B5/4812A61B5/7203A61B5/725A61B5/7267A61B5/291A61B5/257
Inventor 曹琪琪刘冰
Owner 浙江柔灵科技有限公司