Sleep staging method based on deep learning

A technology of sleep staging and deep learning, which is applied in the field of sleep staging based on deep learning, can solve the problems of low average recognition rate of sleep categories, and achieve the effect of avoiding low recognition rate, increasing receptive field, and retaining information

Pending Publication Date: 2020-10-16
SOUTHEAST UNIV
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Problems solved by technology

[0004] Purpose of the invention: In view of the above problems, the present invention proposes a sleep staging method based on deep learning, searches for suitable hyperparameters through Bayesian optimization, and uses a penalty weight loss function to optimize the model twice to solve the low average recognition rate of each sleep category The problem

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  • Sleep staging method based on deep learning
  • Sleep staging method based on deep learning
  • Sleep staging method based on deep learning

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Embodiment Construction

[0039] The details of the present invention will be further described below in conjunction with the accompanying drawings and examples.

[0040] The present invention proposes a sleep staging method based on deep learning, such as figure 1 As shown, it specifically includes the following steps:

[0041] Step 1: Obtain a single-channel EEG signal to construct a data set, and oversample the EEG signal in the data set, and divide the oversampled EEG signal data set into a training set and a verification set; specifically include:

[0042] Obtain the sleep staging data set Sleep-EDF on the Internet. According to the standards of the American Academy of Sleep Medicine, the labels of sleep periods are divided into 5 categories, namely awakening period, light sleep stage I, light sleep stage II, deep sleep period, Rapid eye movement period; because part of the record file is up to 20 hours, this embodiment pays attention to the sleep situation at night, so the data interception is c...

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Abstract

The invention discloses a sleep staging method based on deep learning. The method comprises the following steps: performing oversampling on a single-channel electroencephalogram signal to obtain a data set; designing a convolutional neural network for sleep staging; pre-training is carried out on the data set after oversampling, and hyper-parameters of the model are adjusted according to Bayesianoptimization; designing a penalty weight loss function to perform secondary optimization on the model; and testing the input single-channel electroencephalogram signal by using the trained model to obtain a predicted sleep period. According to the method, the neural network can learn the sleep staging information without additionally extracting features, and the problem of low average recognitionrate of each period caused by imbalance of a data set can be effectively avoided by utilizing the method. The method can be widely applied to scenes with unbalanced data sets, such as electrocardiogram arrhythmia detection and electroencephalogram epilepsy detection.

Description

technical field [0001] The invention relates to the fields of pattern recognition and signal processing, in particular to a sleep staging method based on deep learning. Background technique [0002] Automatic sleep stage classification algorithms mainly include manual feature extraction and automatic feature extraction. Manual feature extraction methods extract features such as time, frequency, and time-frequency domain features from raw signals for training. Such methods may lose most of the original information since they only extract features. The method of automatic feature extraction can directly use the original data for training. Using the characteristics of end-to-end training of some neural networks, using them as feature extractors and classifiers at the same time can solve the limitations of manual feature extraction. [0003] The method of automatic feature extraction is currently using more algorithms, but the data set itself has a serious imbalance problem. M...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04A61B5/0476A61B5/00
CPCA61B5/4812A61B5/4094G06N3/045G06F2218/12G06F18/24155G06F18/214
Inventor 胥凯林夏思宇
Owner SOUTHEAST UNIV
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