IEEE Trans Biomed Eng. 2018 Apr;65(4):754-762. doi: 10.1109/TBME.2017.2718179. Epub 2017 Jun 21.
In this paper, we propose a fast novel nonlinear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal is multiplied, heightening events of interest. This algorithm is thoroughly assessed on benchmark datasets in three different biomedical applications, namely ECG QRS-complex detection, EEG K-complex detection, and imaging photoplethysmography (iPPG) peak detection. Rel-En successfully identified the events in these settings. Compared to the state-of-the-art, better or comparable results were obtained on QRS-complex and K-complex detection. For iPPG peak detection, the proposed method was used as a preprocessing step to a fixed threshold algorithm that lead to a significant improvement in overall results. While easily defined and computed, Rel-En robustly extracted short-term events of interest. The proposed algorithm can be implemented by two filters and its parameters can be selected easily and intuitively. Furthermore, Rel-En algorithm can be used in other biomedical signal processing applications where a need of short-term event extraction is present.
在本文中，我们提出了一种快速新颖的非线性滤波方法，名为相对能量（Rel-En），用于从生物医学信号中稳健地提取短期事件。我们开发了一种算法，该算法可以提取信号中的短期和长期能量，并提供一个系数向量，通过该向量对信号进行乘法运算，从而增强感兴趣的事件。该算法在三个不同的生物医学应用中的基准数据集上进行了全面评估，即心电图 QRS 复合波检测、脑电图 K 复合波检测和成像光体积描记（iPPG）峰值检测。Rel-En 在这些设置中成功地识别了事件。与最先进的方法相比，在 QRS 复合波和 K 复合波检测方面取得了更好或相当的结果。对于 iPPG 峰值检测，所提出的方法被用作固定阈值算法的预处理步骤，这导致整体结果有了显著的提高。虽然 Rel-En 易于定义和计算，但它稳健地提取了感兴趣的短期事件。该算法可以通过两个滤波器实现，其参数可以方便直观地选择。此外，Rel-En 算法可用于其他需要短期事件提取的生物医学信号处理应用中。
