Varon Carolina, Testelmans Dries, Buyse Bertien, Suykens Johan A K, Van Huffel Sabine
Department of Electrical Engineering ESAT, SCD-SISTA, and IBBT Future Health Department, Leuven, Belgium.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3151-4. doi: 10.1109/EMBC.2012.6346633.
Artefacts can pose a big problem in the analysis of electrocardiogram (ECG) signals. Even though methods exist to reduce the influence of these contaminants, they are not always robust. In this work a new algorithm based on easy-to-implement tools such as autocorrelation functions, graph theory and percentile analysis is proposed. This new methodology successfully detects corrupted segments in the signal, and it can be applied to real-life problems such as for example to sleep apnea classification.
伪迹在心电图（ECG）信号分析中可能会造成很大问题。尽管存在一些方法来降低这些干扰因素的影响，但它们并不总是稳健的。在这项工作中，提出了一种基于易于实现的工具（如自相关函数、图论和百分位数分析）的新算法。这种新方法成功地检测出信号中损坏的部分，并且可以应用于实际问题，例如睡眠呼吸暂停分类。
