Espinosa Ricardo, Talero Jesica, Weinstein Alejandro
Department of Biomedical Engineering, Universidad ECCI, Biomedical Applications EMB-IEEE, Bogotá 111311, Colombia.
Center of Research and Development in Health Engineering, Valparaiso University, Valparaíso 2362905, Chile.
Entropy (Basel). 2020 Nov 14;22(11):1298. doi: 10.3390/e22111298.
Electrocardiography (ECG) and electroencephalography (EEG) signals provide clinical information relevant to determine a patient's health status. The nonlinear analysis of ECG and EEG signals allows for discovering characteristics that could not be found with traditional methods based on amplitude and frequency. Approximate entropy (ApEn) and sampling entropy (SampEn) are nonlinear data analysis algorithms that measure the data's regularity, and these are used to classify different electrophysiological signals as normal or pathological. Entropy calculation requires setting the parameters (tolerance threshold), (immersion dimension), and (time delay), with the last one being related to how the time series is downsampled. In this study, we showed the dependence of ApEn and SampEn on different values of , for ECG and EEG signals with different sampling frequencies (), extracted from a digital repository. We considered four values of (128, 256, 384, and 512 Hz for the ECG signals, and 160, 320, 480, and 640 Hz for the EEG signals) and five values of (from 1 to 5). We performed parametric and nonparametric statistical tests to confirm that the groups of normal and pathological ECG and EEG signals were significantly different ( < 0.05) for each and value. The separation between the entropy values of regular and irregular signals was variable, demonstrating the dependence of ApEn and SampEn with and . For ECG signals, the separation between the conditions was more robust when using SampEn, the lowest value of , and larger than 1. For EEG signals, the separation between the conditions was more robust when using SampEn with large values of and larger than 1. Therefore, adjusting may be convenient for signals that were acquired with different to ensure a reliable clinical classification. Furthermore, it is useful to set to values larger than 1 to reduce the computational cost.
心电图（ECG）和脑电图（EEG）信号提供了与确定患者健康状况相关的临床信息。对ECG和EEG信号进行非线性分析能够发现基于幅度和频率的传统方法所无法找到的特征。近似熵（ApEn）和样本熵（SampEn）是非线性数据分析算法，用于测量数据的规律性，这些算法被用于将不同的电生理信号分类为正常或病理性信号。熵计算需要设置参数（容忍阈值）、（嵌入维度）和（时间延迟），其中最后一个参数与时间序列的下采样方式有关。在本研究中，我们展示了从数字存储库中提取的具有不同采样频率（）的ECG和EEG信号的ApEn和SampEn对不同值的依赖性。我们考虑了四个值（ECG信号为128、256、384和512Hz，EEG信号为160、320、480和640Hz）和五个值（从1到5）。我们进行了参数和非参数统计检验，以确认对于每个和值，正常和病理性ECG及EEG信号组存在显著差异（<0.05）。规则信号和不规则信号的熵值之间的分离是可变的，这表明ApEn和SampEn对和的依赖性。对于ECG信号，当使用SampEn、最低的值以及大于1的时，不同条件之间的分离更为稳健。对于EEG信号，当使用具有较大值且大于1的SampEn时，不同条件之间的分离更为稳健。因此，对于以不同采样的信号，调整可能便于确保可靠的临床分类。此外，将设置为大于1的值以降低计算成本是有用的。
