CNN (convolutional neural network) based automatic sleep apnea detection method

A sleep apnea and convolutional neural network technology, applied in diagnostic recording/measurement, medical science, sensors, etc., can solve problems such as inability to represent important information, poor robustness and versatility, and achieve good robustness and prevent Sudden death accident, the effect of real-time analysis

Inactive Publication Date: 2020-06-09
HUANGHUAI UNIV
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Problems solved by technology

[0006] However, these studies are all based on feature engineering methods. Designing these features requires expert do

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  • CNN (convolutional neural network) based automatic sleep apnea detection method
  • CNN (convolutional neural network) based automatic sleep apnea detection method
  • CNN (convolutional neural network) based automatic sleep apnea detection method

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Embodiment Construction

[0033] The implementation process of the present invention will be described with specific examples below with reference to the accompanying drawings, and engineers and technicians or scientific researchers can understand the advantages of the present invention according to the contents described in this specification. The present invention can not only be used for apnea identification, but also can be used for other signal analysis. The implementation examples are provided for the skilled person to understand the present invention more thoroughly. Its main process is as follows figure 1 As shown, it specifically includes the following steps:

[0034] 1. Select data

[0035] Data in the Apnea-ECG database provided by the Physionet website were selected. The data consists of 70 records, 35 training learning sets (a01 to a20, b01 to b05, c01 to c10) and 35 testing sets (x01 to x35). Each recording consisted of a lead ECG signal, apnea annotation labels (analyzed by the physi...

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Abstract

The invention discloses a CNN (convolutional neural network) based automatic sleep apnea detection method. The method specifically comprises steps as follows: S1, an apnea analysis signal is selected,and single-channel ECG signals are selected from a sleep dataset; S2, the ECG data are divided into a training set, a validation set and a test set; S3, a CNN is trained by use of the training set, model parameters are selected by use of the validation set, and model performance is tested by use of the test set; and S4, sleep apnea is detected by use of a final model. By use of the scheme, real-time detection of fully automatic sleep apnea can be realized, and effective support is provided for popularization of detection of the sleep apnea and reduction of sudden death caused by the sleep apnea.

Description

technical field [0001] The invention belongs to the field of physiological signal processing and pattern recognition, in particular to an automatic detection method for sleep apnea based on a convolutional neural network. Background technique [0002] Among many sleep diseases, sleep apnea syndrome (SAS) poses the greatest threat to human health. Sleep apnea can directly lead to problems such as long-term hypoxia, increased chest negative pressure, and sleep structure disorders, and thus cause functional damage to multiple systems, including cardiovascular and cerebrovascular systems, respiratory systems, and central nervous systems. High-risk factors for diseases such as diabetes, cerebrovascular accident, and myocardial infarction. According to the health report issued by the health department, the incidence rate of obstructive sleep apnea syndrome is as high as 4%, and the prevalence rate of the elderly over 65 years old is 20% to 40%. As our country enters the aging st...

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Application Information

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IPC IPC(8): A61B5/00
CPCA61B5/4818A61B5/7267
Inventor 张俊明汤震高金锋张瑜赖晗蔺莉姚汝贤
Owner HUANGHUAI UNIV
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