基于CNN和LSTM的睡眠呼吸暂停检测算法

doi:10.16180/j.cnki.issn1007-7820.2021.02.004

Abstract

Abstract:

Sleep apnea is a common sleep disorder, which is associated with multiple diseases. This study proposes deep learning methods to detect sleep apnea events. The two stages of ECG signal preprocessing are used as inputs to the CNN and LSTM models, respectively. The CNN model takes the original ECG signal as input and automatically extracts features through convolution to identify sleep apnea. The LSTM model uses ECG's indirect signal as input and automatically extractes features from RR intervals and respiratory signals. Experiments show that the LSTM model has a high accuracy of 87.4%, which is close to the performance of the traditional methods. The proposed method combines the advantages of artificial feature extraction and deep learning, and is more applicable than the traditional classification methods.

Key words: CNN, LSTM, sleep apnea, electrocardiograph signal, preprocessing, feature extraction

CLC Number:

TP183

GE Jing,LIU Zilong. The Algorithm Based on CNN and LSTM for Sleep Apnea Syndrome Detection[J].Electronic Science and Technology, 2021, 34(2): 21-26.

Figures/Tables 7

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References 19

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编号	层类型	滤波器尺寸	输出维度
1	batchnorm1	-	6 000×1
2	conv_1	64@50×1	5 951×64
3	maxpool_1	2×2	2 975×64
4	dropout1	0.2	2 975×64
5	batchnorm2	-	2 975×64
6	conv_2	64@50×1	2 926×64
7	conv_3	32@30×1	2 897×32
8	maxpool_2	2×2	1 448×32
9	dropout2	0.2	1 448×32
10	conv_4	16@10×1	1 439×32
11	maxpool_3	2×2	719×16
12	flatten	-	11 504
13	dense_1	-	256
14	dense_2	-	1

编号	层类型	记忆细胞	输出维度
1	batchnorm1	-	240×2
2	1^st LSTM	240	240×128
3	2^nd LSTM	240	256
4	batchnorm2	-	64
5	1^st dense_1	-	64
6	2^nd dense_2	-	64
7	merge	-	320
8	dense_3	-	32
9	dense_4	-	1

方法	Accuracy/%	Recall/%	Precision/%
CNN	75.3	78.1	80.7
LSTM	87.4	84.2	85.5

文献来源	方法	Accuracy /%	Recall /%	Precision /%
Wu等^[9]	SVM	91.29	89.80	91.75
Martín等 ^[18]	DFA	84.76	81.45	86.82
Son等^[17]	HMM+SVM	86.20	82.60	88.40
Li等^[19]	DNN+HMM	84.70	88.90	82.10

The Algorithm Based on CNN and LSTM for Sleep Apnea Syndrome Detection

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