Fotoohinasab Atiyeh, Hocking Toby, Afghah Fatemeh
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:332-336. doi: 10.1109/EMBC44109.2020.9175333.
Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases. Segmentation of the ECG signal to locate its constitutive waves, in particular the R-peaks, is a key step in ECG processing and analysis. Over the years, several segmentation and QRS complex detection algorithms have been proposed with different features; however, their performance highly depends on applying preprocessing steps which makes them unreliable in realtime data analysis of ambulatory care settings and remote monitoring systems, where the collected data is highly noisy. Moreover, some issues still remain with the current algorithms in regard to the diverse morphological categories for the ECG signal and their high computation cost. In this paper, we introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of Rpeak positions without employing any preprocessing step. The proposed model guarantees to compute the globally optimal changepoint detection solution. It is also generic in nature and can be applied to other time-series biomedical signals. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55 which are comparable to other state-of-the-art approaches. .
心电图（ECG）信号是评估心血管疾病时最常用的非侵入性工具。对ECG信号进行分割以定位其组成波，特别是R波峰，是ECG处理和分析中的关键步骤。多年来，已经提出了几种具有不同特征的分割和QRS复合波检测算法；然而，它们的性能高度依赖于应用预处理步骤，这使得它们在动态护理环境和远程监测系统的实时数据分析中不可靠，因为在这些系统中收集的数据噪声很大。此外，当前算法在ECG信号的不同形态类别及其高计算成本方面仍然存在一些问题。在本文中，我们介绍了一种新颖的基于图的最优变点检测（GCCD）方法，用于可靠地检测R波峰位置，而无需采用任何预处理步骤。所提出的模型保证能计算出全局最优变点检测解决方案。它本质上也是通用的，可应用于其他时间序列生物医学信号。基于麻省理工学院 - 贝斯以色列女执事医疗中心心律失常（MIT - BIH - AR）数据库，所提出的方法实现了总体灵敏度Sen = 99.76、阳性预测值PPR = 99.68和检测错误率DER = 0.55，与其他现有技术方法相当。
