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. 2021 Mar 22:2021:5594733.
doi: 10.1155/2021/5594733. eCollection 2021.

Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model

Junming Zhang  1   2   3   4   5 , Zhen Tang  1 , Jinfeng Gao  1   2 , Li Lin  1 , Zhiliang Liu  1 , Haitao Wu  1   2 , Fang Liu  1   3 , Ruxian Yao  1   2
Affiliations

Automatic Detection of Obstructive Sleep Apnea Events Using a Deep CNN-LSTM Model

Junming Zhang et al. Comput Intell Neurosci. .

Abstract

Obstructive sleep apnea (OSA) is a common sleep-related respiratory disorder. Around the world, more and more people are suffering from OSA. Because of the limitation of monitor equipment, many people with OSA remain undetected. Therefore, we propose a sleep-monitoring model based on single-channel electrocardiogram using a convolutional neural network (CNN), which can be used in portable OSA monitor devices. To learn different scale features, the first convolution layer comprises three types of filters. The long short-term memory (LSTM) is used to learn the long-term dependencies such as the OSA transition rules. The softmax function is connected to the final fully connected layer to obtain the final decision. To detect a complete OSA event, the raw ECG signals are segmented by a 10 s overlapping sliding window. The proposed model is trained with the segmented raw signals and is subsequently tested to evaluate its event detection performance. According to experiment analysis, the proposed model exhibits Cohen's kappa coefficient of 0.92, a sensitivity of 96.1%, a specificity of 96.2%, and an accuracy of 96.1% with respect to the Apnea-ECG dataset. The proposed model is significantly higher than the results from the baseline method. The results prove that our approach could be a useful tool for detecting OSA on the basis of a single-lead ECG.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic of the proposed CNN model for the automated detection of OSA.
Figure 2
Figure 2
Architecture of the proposed model.
Figure 3
Figure 3
Accuracy and loss of the proposed model for automated OSA detection. (a) Loss curve. (b) Accuracy curve.
Figure 4
Figure 4
Filters morphology and training time with each epoch. (a) Filter morphology. (b) Training time.
Figure 5
Figure 5
A transition epoch from an NE to an AE. Blue denotes the ECG signal, and black denotes the nasal airflow signal.
Figure 6
Figure 6
A transition epoch from an AE to an NE. Blue denotes the ECG signal, and black denotes the nasal airflow signal.
Figure 7
Figure 7
An ECG artifact epoch. Blue denotes the ECG signal, and black denotes the nasal airflow signal.
Figure 8
Figure 8
A normal ECG epoch.
Figure 9
Figure 9
The start and end positions of multiple OSA events.
See this image and copyright information in PMC

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