Method and device for detection of sleep apnea fragment based on unsupervised feature learning

Abstract

The invention discloses a method and device for detection of a sleep apnea fragment based on unsupervised feature learning. The method includes the steps: collecting sleeping electrocardiosignals (ECG), performing analog-digital conversion on the ECG signals so as to obtain electrocardio digital signals, performing segmentation on the electrocardio digital signals according to minutes so as to obtain ECG segments, extracting a RR interphase sequence for correction according to the ECG segments, performing cubic spline interpolation and fast Fourier transform so as to obtain a frequency domainsequence, making a train set, constructing a stack-type sparse self-coding model, adopting an unlabeled data set to perform pre-training of a sparse self-encoder, performing unsupervised learning andfeature extraction on the frequency domain sequence, performing fine tuning on the stack-type sparse self-coding model by using a labeled train set, constructing a Softmax-hidden markov and time-dependent-cost-sensitive classification model, using the sparse self-coding model to obtain features and corresponding labels in the labeled train set, and training the Softmax-hidden markov and time-dependent-cost-sensitive classification model by using the features and corresponding labels in the labeled train set so as to obtain a sleep apnea classification model.

Description

technical field [0001] The invention relates to the technical field of medical detection, in particular to a sleep apnea segment detection method and equipment based on unsupervised feature learning. Background technique [0002] Sleep apnea (sleep apnea) is a common disorder in which the airflow of breathing is reduced or stopped during sleep, mainly due to the relaxation of the tongue or other soft tissues in the back of the throat during sleep, blocking the airway, or due to inhibition of the respiratory center, The central nervous system is caused by unstable control of respiratory feedback. The incidence of sleep apnea in the world is about 2% to 4%. At present, about 936 million people in the world suffer from sleep apnea (OSA), and about 60 million people in my country suffer from this type of disease. When sleep apnea occurs, due to The lack of oxygen in the body will cause repeated micro-awakenings in the brain during sleep, which seriously affects the patient's sle...

AI Technical Summary

Problems solved by technology

[0006] In view of the above problems, the object of the present invention is to provide a sleep apnea segment detection method and equipment based on unsupervised feature learning, which successfully introduces the unsupervised learning method into the task of sleep apnea feature extraction, and solves the problem of sleep apnea in previous studies. Problems that are highly dependent on training data labels and prior knowledge

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