房颤鉴别中熵方法的比较。
A comparison of entropy approaches for AF discrimination.
机构信息
The State Key Laboratory of Bioelectronics, Jiangsu Key Lab of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, People's Republic of China. Department of Biomedical Informatics, Emory University, Atlanta, GA, United States of America.
出版信息
Physiol Meas. 2018 Jul 6;39(7):074002. doi: 10.1088/1361-6579/aacc48.
OBJECTIVE
This study focuses on the comparison of single entropy measures for ventricular response analysis-based AF detection.
APPROACH
To enhance the performance of entropy-based AF detectors, we developed a normalized fuzzy entropy, [Formula: see text], a novel metric that (1) uses a fuzzy function to determine vector similarity, (2) replaces probability estimation with density estimation for entropy approximation, (3) utilizes a flexible distance threshold parameter, and (4) adjusts for heart rate by subtracting the natural log value of the mean RR interval. An AF detector based on [Formula: see text] was trained using the MIT-BIH atrial fibrillation (AF) database, and tested on the MIT-BIH normal sinus rhythm (NSR) and MIT-BIH arrhythmia databases. The [Formula: see text]-based AF detector was compared to AF detectors based on three other entropy measures: sample entropy ([Formula: see text]), fuzzy measure entropy ([Formula: see text]) and coefficient of sample entropy ([Formula: see text]), over three standard window sizes.
MAIN RESULTS
To classify AF and non-AF rhythms, [Formula: see text] achieved the highest area under receiver operating characteristic curve (AUC) values of 92.72%, 95.27% and 96.76% for 12-, 30- and 60-beat window lengths respectively. This was higher than the performance of the next best technique, [Formula: see text], over all windows sizes, which provided respective AUCs of 91.12%, 91.86% and 90.55%. [Formula: see text] and [Formula: see text] resulted in lower AUCs (below 90%) over all window sizes. [Formula: see text] also provided superior performance for all other tested statistics, including the Youden index, sensitivity, specificity, accuracy, positive predictivity and negative predictivity. In conclusion, we show that [Formula: see text] can be used to accurately identify AF from RR interval time series. Furthermore, longer window lengths (up to one minute) increase the performance of all entropy-based AF detectors under evaluation except the [Formula: see text]-based method.
SIGNIFICANCE
Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.
目的
本研究旨在比较基于心室反应分析的房颤检测的单一熵测度。
方法
为了提高基于熵的房颤检测器的性能，我们开发了一种归一化模糊熵 [Formula: see text]，这是一种新的度量标准，它（1）使用模糊函数来确定向量相似性，（2）用密度估计代替概率估计来近似熵，（3）使用灵活的距离阈值参数，（4）通过减去平均 RR 间隔的自然对数来调整心率。基于 [Formula: see text] 的房颤检测器使用麻省理工学院-贝思以色列医院（MIT-BIH）房颤数据库进行训练，并在麻省理工学院-贝思以色列医院正常窦性节律（NSR）和麻省理工学院-贝思以色列医院心律失常数据库上进行测试。与基于三个其他熵测度的房颤检测器（样本熵 [Formula: see text]、模糊测度熵 [Formula: see text]和样本熵系数 [Formula: see text]）相比，[Formula: see text]在三个标准窗口大小上均实现了更高的接收器工作特征曲线（ROC）下面积（AUC）值，分别为 92.72%、95.27%和 96.76%。对于 12、30 和 60 个心跳的窗口长度，这高于下一个最佳技术 [Formula: see text]在所有窗口大小上的性能，分别提供了 91.12%、91.86%和 90.55%的 AUC。在所有窗口大小上，[Formula: see text]和 [Formula: see text]产生的 AUC 均低于 90%。对于所有其他测试统计数据，[Formula: see text]也提供了卓越的性能，包括约登指数、敏感性、特异性、准确性、阳性预测值和阴性预测值。总之，我们表明 [Formula: see text]可用于从 RR 间隔时间序列中准确识别房颤。此外，更长的窗口长度（长达一分钟）除了基于 [Formula: see text]的方法外，还可以提高所有评估的基于熵的房颤检测器的性能。
意义
我们的结果表明，新开发的归一化模糊熵是一种准确检测房颤的方法。
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