IEEE J Biomed Health Inform. 2018 Nov;22(6):1895-1905. doi: 10.1109/JBHI.2017.2784415. Epub 2017 Dec 18.
Obstructive sleep apnea (OSA) is a popular sleep disorder. Traditional OSA diagnosis methods are cumbersome and expensive, which bring inconvenience for patient diagnosis and heavy workload for physician. Automatically identifying OSA patients from electrocardiogram (ECG) records is important for clinical diagnosis and treatment. In this paper, a new method based on the frequency and network domains is proposed to automatically recognize OSA patients with nocturnal ECG records. First, each RR-interval (beat to beat heart rate) series was divided into segments. By calculating the power spectral density (PSD) of heart rate variability segment with Lomb-Scargle method, the dynamic time warping (DTW) distance was used to evaluate the similarity (dissimilarity) of the lower frequency in the PSD series, then the DTW distance matrix was transformed to a binary matrix, and then network metrics were calculated to discriminate OSA patients with healthy subjects. The new method was tested with data of 389 subjects collected from two public databases that consist of normal subjects without OSA (apnea-hypopnea index, AHI 5) and OSA patients (AHI 5). Results show that a single network metric (local clustering coefficient) can recognize OSA patients with 90.1% accuracy, 88.29% sensitivity, and 90.5% specificity, and confirm the potential of using the ECG records for OSA patients recognition.
阻塞性睡眠呼吸暂停（OSA）是一种常见的睡眠障碍。传统的 OSA 诊断方法繁琐且昂贵，给患者诊断带来不便，也给医生带来沉重的工作负担。自动从心电图（ECG）记录中识别 OSA 患者对于临床诊断和治疗非常重要。本文提出了一种基于频率和网络领域的新方法，用于自动识别夜间 ECG 记录中的 OSA 患者。首先，将每个 RR 间隔（心跳间隔）序列分为片段。通过计算 Lomb-Scargle 方法的心率变异性片段的功率谱密度（PSD），使用动态时间规整（DTW）距离来评估 PSD 序列中低频的相似性（不相似性），然后将 DTW 距离矩阵转换为二进制矩阵，然后计算网络度量以区分 OSA 患者和健康受试者。该新方法使用从两个公共数据库中收集的 389 名受试者的数据进行了测试，这些数据库由没有 OSA（呼吸暂停-低通气指数，AHI 5）的正常受试者和 OSA 患者（AHI 5）组成。结果表明，单个网络度量（局部聚类系数）可以以 90.1%的准确率、88.29%的灵敏度和 90.5%的特异性识别 OSA 患者，证实了使用 ECG 记录识别 OSA 患者的潜力。
