Yamane Takahiro, Fujii Masanori, Morita Mizuki
Department of Biomedical Informatics, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan.
Department of Geriatric Medicine, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Japan.
Sleep Breath. 2025 Apr 11;29(2):156. doi: 10.1007/s11325-025-03316-0.
To establish a simple and noninvasive screening test for sleep apnea (SA) that imposes less burden on potential patients. The specific objective of this study was to verify the effectiveness of past and future single-lead electrocardiogram (ECG) data from SA occurrence sites in improving the estimation accuracy of SA and sleep apnea syndrome (SAS) using machine learning.
The Apnea-ECG dataset comprising 70 ECG recordings was used to construct various machine-learning models. The time window size was adjusted based on the accuracy of SA detection, and the performance of SA detection and SAS diagnosis (apnea‒hypopnea index ≥ 5 was considered SAS) was compared.
Using ECG data from a few minutes before and after the occurrence of SAs improved the estimation accuracy of SA and SAS in all machine learning models. The optimal range of the time window and achieved accuracy for SAS varied by model; however, the sensitivity ranged from 95.7 to 100%, and the specificity ranged from 91.7 to 100%.
ECG data from a few minutes before and after SA occurrence were effective in SA detection and SAS diagnosis, confirming that SA is a continuous phenomenon and that SA affects heart function over a few minutes before and after SA occurrence. Screening tests for SAS, using data obtained from single-lead ECGs with appropriate past and future time windows, should be performed with clinical-level accuracy.
建立一种简单且无创的睡眠呼吸暂停（SA）筛查测试，以减轻潜在患者的负担。本研究的具体目标是验证来自SA发生部位的过去和未来单导联心电图（ECG）数据在使用机器学习提高SA和睡眠呼吸暂停综合征（SAS）估计准确性方面的有效性。
使用包含70份ECG记录的Apnea-ECG数据集构建各种机器学习模型。根据SA检测的准确性调整时间窗口大小，并比较SA检测和SAS诊断（呼吸暂停低通气指数≥5被视为SAS）的性能。
使用SA发生前后几分钟的ECG数据提高了所有机器学习模型中SA和SAS的估计准确性。时间窗口的最佳范围和SAS达到的准确性因模型而异；然而，敏感性范围为95.7%至100%，特异性范围为91.7%至100%。
SA发生前后几分钟的ECG数据在SA检测和SAS诊断中有效，证实SA是一种连续现象，并且SA在SA发生前后几分钟内影响心脏功能。应使用具有适当过去和未来时间窗口的单导联ECG获得的数据，以临床水平的准确性进行SAS的筛查测试。
