Original Article
Value of night pulse oximetry monitoring in obstructive sleep apnea hypopnea syndrome prediction and classification
Zhang Jing, Zhao Dan, Zhou Zhongxing, Wang Yan, Chen Baoyuan
Published 2021-02-12
Cite as Chin J Tuberc Respir Dis, 2021, 44(2): 101-107. DOI: 10.3760/cma.j.cn112147-20200724-00843
Abstract
ObjectiveTo explore the value of night pulse oximetry monitoring in the prediction and classification of obstructive sleep apnea hypopnea syndrome (OSAHS).
MethodsFrom January 2018 to December 2019, 580 snoring patients admitted to the Sleep Center of Tianjin Medical University General Hospital were analyzed retrospectively. There were 418 males and 162 females, aging 13-85(49±14) years. All subjects underwent polysomnography, and the apnea hypopnea index (AHI)was 0-101.4(43.06±27.47) times/hour. There were 52 cases in the non-OSAHS group (AHI<5 times/h), 69 cases in the mild OSAHS group (5 times/h<AHI≤15 times/h), 98 cases in the moderate OSAHS group (15 times/h<AHI≤30 times/h), and 361 cases in the severe OSAHS group (30 times/h<AHI).Correlation analysis was performed between indicators extracted from SpO2 signal and AHI, and 11 blood oxygen indicators related to AHI were selected (3% oxygen reduction recovery index, the area of SpO2 under the 90% curve, average lowest SpO2, lowest SpO2, the average SpO2, the percentage of time SpO2 under 95%, 90%, 85%, 80%, 75%, 70%). Finally, gender, age and body mass index (BMI) were added. We ysed multiple linear regression (MLR) method to achieve AHI prediction, and back propagation neural network (BPNN) multi-classification method to achieve OSAHS severity classification. Statistical analysis was performed based on SPSS 25.0. The measurement data were analyzed using Pearson correlation test.
ResultsThe MLR method achieved high prediction performance, with a prediction correlation coefficient r=0.901 (P<0.05) and a goodness of fitr2 = 0.848 (P<0.05).The specificity and negative prediction rate of BPNN method classification results were both around 90%, and the sensitivity and positive prediction rates were also high. Among them, the sensitivity of the non-OSAHS group (AHI<5 times/h) was 88.46%±4.50%, and the sensitivity of the severe OSAHS group (AHI>30 times/h) was 94.74%±0.76%.
ConclusionBased on the signals recorded by the SpO2 monitor, the methods of using MLR model for AHI prediction and using BPNN model for multi-classification may have higher value for the prediction and classification of OSAHS.
Key words:
Sleep apnea, obstructive; Oximetry; Apnea-hypopnea index; Multiple linear regression; Backpropagation neural network
Contributor Information
Zhang Jing
Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
Zhao Dan
Department of Respiratory and Critical Care Medicine, Tianjin Medical University Central Hospital, Tianjin 300052, China
Zhou Zhongxing
Biomedical Engineering, School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, China
Wang Yan
Department of Respiratory and Critical Care Medicine, Tianjin Medical University Central Hospital, Tianjin 300052, China
Chen Baoyuan
Department of Respiratory and Critical Care Medicine, Tianjin Medical University Central Hospital, Tianjin 300052, China
