Lin Cheng-Yu, Wang Yi-Wen, Setiawan Febryan, Trang Nguyen Thi Hoang, Lin Che-Wei
Department of Otolaryngology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.
Department of Environmental and Occupational Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.
J Clin Med. 2021 Dec 30;11(1):192. doi: 10.3390/jcm11010192.
Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used to detect sleep apnea (SA) for decades. The present study proposes an SA-detection algorithm using a machine-learning framework and bag-of-features (BoF) derived from an ECG spectrogram.
This study was verified using overnight ECG recordings from 83 subjects with an average apnea-hypopnea index (AHI) 29.63 (/h) derived from the Physionet Apnea-ECG and National Cheng Kung University Hospital Sleep Center database. The study used signal preprocessing to filter noise and artifacts, ECG time-frequency transformation using continuous wavelet transform (CWT), BoF feature generation, machine-learning classification using support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN) classification, and cross-validation. The time length of the spectrogram was set as 10 and 60 s to examine the required minimum spectrogram window time length to achieve satisfactory accuracy. Specific frequency bands of 0.1-50, 8-50, 0.8-10, and 0-0.8 Hz were also extracted to generate the BoF to determine the band frequency best suited for SA detection.
The five-fold cross-validation accuracy using the BoF derived from the ECG spectrogram with 10 and 60 s time windows were 90.5% and 91.4% for the 0.1-50 Hz and 8-50 Hz frequency bands, respectively.
An SA-detection algorithm utilizing BoF and a machine-learning framework was successfully developed in this study with satisfactory classification accuracy and high temporal resolution.
几十年来，心率变异性（HRV）和心电图（ECG）衍生呼吸（EDR）一直被用于检测睡眠呼吸暂停（SA）。本研究提出了一种使用机器学习框架和从ECG频谱图导出的特征袋（BoF）的SA检测算法。
本研究使用来自83名受试者的夜间ECG记录进行验证，这些受试者的平均呼吸暂停低通气指数（AHI）为29.63（/小时），数据来自Physionet Apnea-ECG和国立成功大学医院睡眠中心数据库。该研究使用信号预处理来过滤噪声和伪迹，使用连续小波变换（CWT）进行ECG时频变换，生成BoF特征，使用支持向量机（SVM）、集成学习（EL）、k近邻（KNN）分类进行机器学习分类，并进行交叉验证。将频谱图的时间长度设置为10和60秒，以检查实现满意准确性所需的最小频谱图窗口时间长度。还提取了0.1-50、8-50、0.8-10和0-0.8Hz的特定频段以生成BoF，以确定最适合SA检测的频段频率。
对于0.1-50Hz和8-50Hz频段，使用来自10秒和60秒时间窗口的ECG频谱图导出的BoF进行五重交叉验证的准确率分别为90.5%和91.4%。
本研究成功开发了一种利用BoF和机器学习框架的SA检测算法，具有令人满意的分类准确率和高时间分辨率。
