Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (2): 654-662.DOI: 10.11772/j.issn.1001-9081.2023020191
Special Issue: 前沿与综合应用
• Frontier and comprehensive applications • Previous Articles
Yudan SONG1,2, Jing WANG1,2,3(), Xuehui WANG1,2, Zhaoyang MA1,2, Youfang LIN1,2,3
Received:
2023-03-01
Revised:
2023-05-04
Accepted:
2023-05-05
Online:
2024-02-22
Published:
2024-02-10
Contact:
Jing WANG
About author:
SONG Yudan, born in 1997, M. S. candidate. Her research interests include sleep physiological signal classification, deep learning.Supported by:
宋钰丹1,2, 王晶1,2,3(), 王雪徽1,2, 马朝阳1,2, 林友芳1,2,3
通讯作者:
王晶
作者简介:
宋钰丹(1997—),女,山西孝义人,硕士研究生,主要研究方向:睡眠生理信号分类、深度学习基金资助:
CLC Number:
Yudan SONG, Jing WANG, Xuehui WANG, Zhaoyang MA, Youfang LIN. Sleep physiological time series classification method based on adaptive multi-task learning[J]. Journal of Computer Applications, 2024, 44(2): 654-662.
宋钰丹, 王晶, 王雪徽, 马朝阳, 林友芳. 基于自适应多任务学习的睡眠生理时序分类方法[J]. 《计算机应用》唯一官方网站, 2024, 44(2): 654-662.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023020191
数据集 | WAKE | N1 | N2 | N3 | REM | 总样本量 | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
样本量 | 占比/% | 样本量 | 占比/% | 样本量 | 占比/% | 样本量 | 占比/% | 样本量 | 占比/% | ||
UCD | 4 659 | 25.36 | 3 400 | 18.51 | 6 984 | 38.02 | 673 | 3.66 | 3 014 | 16.41 | 18 370 |
ISRUC | 18 940 | 22.55 | 10 737 | 12.78 | 26 691 | 31.78 | 16 743 | 19.93 | 10 899 | 12.96 | 84 000 |
Tab. 1 Statistics of datasets about sleep stages
数据集 | WAKE | N1 | N2 | N3 | REM | 总样本量 | |||||
---|---|---|---|---|---|---|---|---|---|---|---|
样本量 | 占比/% | 样本量 | 占比/% | 样本量 | 占比/% | 样本量 | 占比/% | 样本量 | 占比/% | ||
UCD | 4 659 | 25.36 | 3 400 | 18.51 | 6 984 | 38.02 | 673 | 3.66 | 3 014 | 16.41 | 18 370 |
ISRUC | 18 940 | 22.55 | 10 737 | 12.78 | 26 691 | 31.78 | 16 743 | 19.93 | 10 899 | 12.96 | 84 000 |
数据集 | 呼吸暂停低通气 | 正常 | 总样本量 | ||
---|---|---|---|---|---|
样本量 | 占比/% | 样本量 | 占比/% | ||
UCD | 3 876 | 21.10 | 14 854 | 80.86 | 18 370 |
ISRUC | 6 579 | 7.83 | 77 421 | 92.17 | 84 000 |
Tab. 2 Statisticss of datasets about sleep apnea hypopnea
数据集 | 呼吸暂停低通气 | 正常 | 总样本量 | ||
---|---|---|---|---|---|
样本量 | 占比/% | 样本量 | 占比/% | ||
UCD | 3 876 | 21.10 | 14 854 | 80.86 | 18 370 |
ISRUC | 6 579 | 7.83 | 77 421 | 92.17 | 84 000 |
方法 | STL/MTL | 输入信号 | UCD数据集 | ISRUC数据集 | ||||
---|---|---|---|---|---|---|---|---|
ACC/% | MF1/% | ROC-AUC | ACC/% | MF1/% | ROC-AUC | |||
DeepSleepNet | STL | EEG | 74.36±0.69 | 73.54±0.37 | 0.932 6±0.003 0 | 72.71±1.15 | 70.32±1.33 | 0.932 1±0.003 7 |
12-layer CNN | STL | EEG | 74.46±0.55 | 73.49±0.72 | 0.929 7±0.005 7 | 79.27±0.22 | 77.38±0.26 | 0.947 5±0.000 8 |
TinySleepNet | STL | EEG | 75.96±0.28 | 74.98±0.26 | 0.936 9±0.003 8 | 79.09±0.30 | 77.57±0.22 | 0.953 0±0.001 2 |
AttnSleep | STL | EEG | 75.07±1.43 | 74.10±1.42 | 0.936 7±0.003 2 | 77.52±0.46 | 76.48±0.91 | 0.945 7±0.004 0 |
本文方法 | MTL | EEG+ECG | 77.17±0.46 | 76.20±0.34 | 0.945 2±0.002 1 | 79.39±0.32 | 77.34±0.42 | 0.952 9±0.000 6 |
Tab. 3 Sleep stage classification performance comparison between proposed method and baseline methods on UCD and ISRUC datasets
方法 | STL/MTL | 输入信号 | UCD数据集 | ISRUC数据集 | ||||
---|---|---|---|---|---|---|---|---|
ACC/% | MF1/% | ROC-AUC | ACC/% | MF1/% | ROC-AUC | |||
DeepSleepNet | STL | EEG | 74.36±0.69 | 73.54±0.37 | 0.932 6±0.003 0 | 72.71±1.15 | 70.32±1.33 | 0.932 1±0.003 7 |
12-layer CNN | STL | EEG | 74.46±0.55 | 73.49±0.72 | 0.929 7±0.005 7 | 79.27±0.22 | 77.38±0.26 | 0.947 5±0.000 8 |
TinySleepNet | STL | EEG | 75.96±0.28 | 74.98±0.26 | 0.936 9±0.003 8 | 79.09±0.30 | 77.57±0.22 | 0.953 0±0.001 2 |
AttnSleep | STL | EEG | 75.07±1.43 | 74.10±1.42 | 0.936 7±0.003 2 | 77.52±0.46 | 76.48±0.91 | 0.945 7±0.004 0 |
本文方法 | MTL | EEG+ECG | 77.17±0.46 | 76.20±0.34 | 0.945 2±0.002 1 | 79.39±0.32 | 77.34±0.42 | 0.952 9±0.000 6 |
方法 | STL/ MTL | 输入 信号 | UCD数据集 | ISRUC数据集 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
ACC/% | MF2/% | ROC-AUC | Recall/% | ACC/% | MF2/% | ROC-AUC | Recall/% | |||
6-layer CNN | STL | ECG | 84.25±1.14 | 54.01±3.79 | 0.722 3±0.018 0 | 53.17±4.73 | 88.492±2.43 | 29.23±7.93 | 0.618 2±0.035 8 | 29.70±10.59 |
1D-ResNet | STL | ECG | 88.25±0.69 | 48.83±5.93 | 0.712 4±0.027 9 | 44.26±6.19 | 92.386±0.30 | 16.17±6.48 | 0.566 7±0.027 7 | 13.59±5.65 |
本文方法 | MTL | EEG+ ECG | 83.07±0.81 | 65.09±0.68 | 0.776 0±0.003 6 | 68.92±1.45 | 89.72±0.68 | 39.13±1.28 | 0.667 6±0.007 0 | 39.11±1.61 |
Tab. 4 SAHS detection performance comparison between proposed method and baseline methods on UCD and ISRUC datasets
方法 | STL/ MTL | 输入 信号 | UCD数据集 | ISRUC数据集 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
ACC/% | MF2/% | ROC-AUC | Recall/% | ACC/% | MF2/% | ROC-AUC | Recall/% | |||
6-layer CNN | STL | ECG | 84.25±1.14 | 54.01±3.79 | 0.722 3±0.018 0 | 53.17±4.73 | 88.492±2.43 | 29.23±7.93 | 0.618 2±0.035 8 | 29.70±10.59 |
1D-ResNet | STL | ECG | 88.25±0.69 | 48.83±5.93 | 0.712 4±0.027 9 | 44.26±6.19 | 92.386±0.30 | 16.17±6.48 | 0.566 7±0.027 7 | 13.59±5.65 |
本文方法 | MTL | EEG+ ECG | 83.07±0.81 | 65.09±0.68 | 0.776 0±0.003 6 | 68.92±1.45 | 89.72±0.68 | 39.13±1.28 | 0.667 6±0.007 0 | 39.11±1.61 |
方法 | STL/MTL | 输入信号 | 睡眠分期 | 睡眠呼吸暂停低通气检测 | |||||
---|---|---|---|---|---|---|---|---|---|
ACC/% | MF1/% | ROC-AUC | ACC/% | MF2/% | ROC-AUC | Recall/% | |||
A | STL | ECG | — | — | — | 85.81±0.50 | 60.63±1.67 | 0.762 0±0.010 8 | 60.53±2.13 |
B | STL | EEG+ECG | 75.94±0.60 | 74.55±0.54 | 0.942 0±0.002 0 | 85.07±0.87 | 63.75±1.47 | 0.773 8±0.009 8 | 65.17±1.42 |
C | MTL | EEG+ECG | 77.17±0.46 | 76.20±0.34 | 0.945 2±0.002 1 | 83.07±0.81 | 65.09±0.68 | 0.776 0±0.003 6 | 68.92±1.45 |
Tab. 5 Ablation study results conducted on UCD dataset
方法 | STL/MTL | 输入信号 | 睡眠分期 | 睡眠呼吸暂停低通气检测 | |||||
---|---|---|---|---|---|---|---|---|---|
ACC/% | MF1/% | ROC-AUC | ACC/% | MF2/% | ROC-AUC | Recall/% | |||
A | STL | ECG | — | — | — | 85.81±0.50 | 60.63±1.67 | 0.762 0±0.010 8 | 60.53±2.13 |
B | STL | EEG+ECG | 75.94±0.60 | 74.55±0.54 | 0.942 0±0.002 0 | 85.07±0.87 | 63.75±1.47 | 0.773 8±0.009 8 | 65.17±1.42 |
C | MTL | EEG+ECG | 77.17±0.46 | 76.20±0.34 | 0.945 2±0.002 1 | 83.07±0.81 | 65.09±0.68 | 0.776 0±0.003 6 | 68.92±1.45 |
睡眠时期 | 条件概率 | 注意力权重 |
---|---|---|
WAKE | 0.035 3 | 0.383 9 |
N1 | 0.131 0 | 0.536 8 |
N2 | 0.123 1 | 0.641 4 |
N3 | 0.094 7 | 0.537 2 |
REM | 0.146 2 | 0.640 9 |
Tab. 6 Probability of sleep apnea hypopnea of each stage and average weight in last five rounds on training set in UCD dataset
睡眠时期 | 条件概率 | 注意力权重 |
---|---|---|
WAKE | 0.035 3 | 0.383 9 |
N1 | 0.131 0 | 0.536 8 |
N2 | 0.123 1 | 0.641 4 |
N3 | 0.094 7 | 0.537 2 |
REM | 0.146 2 | 0.640 9 |
方法 | STL/MTL | FLOPs/106 | 参数量/103 | 占用GPU显存/MB | 训练时间/min | |
---|---|---|---|---|---|---|
睡眠分期 STL基准方法 | DeepSleepNet | STL | 245.40 | 38 780.00 | 2 303 | 95 |
12-layer CNN | STL | 1 160.00 | 2 280.00 | 3 869 | 116 | |
TinySleepNet | STL | 144.41 | 1450.00 | 1 135 | 81 | |
AttnSleep | STL | 392.47 | 1 260.00 | 2 823 | 89 | |
SAHS检测 STL基准方法 | 6⁃layer CNN | STL | 328.94 | 64.87 | 3 113 | 146 |
1D ResNet | STL | 392.47 | 1 260.00 | 2 637 | 152 | |
MTL方法 | 本文方法 | MTL | 504.90 | 968.02 | 2 589 | 201 |
Tab. 7 Time complexity comparison between proposed MTL method and baseline STL methods on UCD dataset
方法 | STL/MTL | FLOPs/106 | 参数量/103 | 占用GPU显存/MB | 训练时间/min | |
---|---|---|---|---|---|---|
睡眠分期 STL基准方法 | DeepSleepNet | STL | 245.40 | 38 780.00 | 2 303 | 95 |
12-layer CNN | STL | 1 160.00 | 2 280.00 | 3 869 | 116 | |
TinySleepNet | STL | 144.41 | 1450.00 | 1 135 | 81 | |
AttnSleep | STL | 392.47 | 1 260.00 | 2 823 | 89 | |
SAHS检测 STL基准方法 | 6⁃layer CNN | STL | 328.94 | 64.87 | 3 113 | 146 |
1D ResNet | STL | 392.47 | 1 260.00 | 2 637 | 152 | |
MTL方法 | 本文方法 | MTL | 504.90 | 968.02 | 2 589 | 201 |
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