论文标题
ECG分割的图形受限的更改点检测方法
A Graph-constrained Changepoint Detection Approach for ECG Segmentation
论文作者
论文摘要
心电图（ECG）信号是评估心血管疾病中最常用的非侵入性工具。 ECG信号的分割以定位其组成性波，尤其是R-Peaks，是ECG处理和分析的关键步骤。多年来，已经提出了具有不同特征的几种分割和QRS复杂检测算法。但是，它们的性能在很大程度上取决于应用预处理步骤，这使得它们在室外护理设置和远程监视系统的实时数据分析中不可靠，而收集到的数据非常嘈杂。此外，关于ECG信号的多种形态类别及其高计算成本，目前的算法仍然存在一些问题。在本文中，我们介绍了一种基于图形的最佳更改点检测（GCCD）方法，用于可靠地检测R峰位置，而无需采用任何预处理步骤。提出的模型保证可以计算全球最佳更改点检测解决方案。它本质上也是通用的，可以应用于其他时间序列的生物医学信号。基于MIT-BIH心律不齐（MIT-BIH-AR）数据库，所提出的方法达到了总体敏感性SEN = 99.76，阳性预测性PPR = 99.68，并且检测错误率der = 0.55，这些= 0.55，与其他最新的方法相当。
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 real-time 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 R-peak 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.
