A Graph-constrained Changepoint Detection Approach for ECG Segmentation.
A Graph-constrained Changepoint Detection Approach for ECG Segmentation.
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DOI：
10.1109/embc44109.2020.9175333
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发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Afghah F
中科院分区：
文献类型：
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作者：
Fotoohinasab A;Hocking T;Afghah F
在心血管疾病的评估中，心电信号是最常用的非侵入性工具。分割心电信号以定位其构成波，特别是R峰，是心电信号处理和分析的关键步骤。多年来，已经提出了几种具有不同特点的分割和QRS波群检测算法，但它们的性能高度依赖于应用预处理步骤，这使得它们在门诊护理设置和远程监护系统的实时数据分析中不可靠，这些系统收集的数据噪声很高。此外，目前的算法仍然存在一些问题，即心电信号的形态分类繁多，计算量大。本文提出了一种新的基于图的最优变点检测(GCCD)方法，该方法无需任何预处理步骤即可可靠地检测R峰位置。该模型保证了计算出全局最优的变点检测解。它在本质上也是通用的，并且可以应用于其他时间序列生物医学信号。基于MIT-BIH心律失常(MIT-BIH-AR)数据库，该方法的总体灵敏度SEN=99.76，正预测性PPR=99.68，检测错误率DER=0.55，可与其他先进方法相媲美。
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.