Sharif Babak, Jafari Amir Homayoun
Medical Physics & Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Research Center for Biomedical Technologies and Robotics (RCBTR), Tehran, Iran.
Australas Phys Eng Sci Med. 2018 Mar;41(1):13-20. doi: 10.1007/s13246-017-0599-2. Epub 2017 Nov 16.
Biosignals are considered as important sources of data for diagnosing and detecting abnormalities, and modeling dynamics in the body. These signals are usually analyzed using features taken from time and frequency domain. In theory' these dynamics can also be analyzed utilizing Poincaré plane that intersects system's trajectory. However' selecting an appropriate Poincaré plane is a crucial part of extracting best Poincaré samples. There is no unique way to choose a Poincaré plane' because it is highly dependent to the system dynamics. In this study, a new algorithm is introduced that automatically selects an optimum Poincaré plane able to transfer maximum information from EEG time series to a set of Poincaré samples. In this algorithm' EEG time series are first embedded; then a parametric Poincaré plane is designed and finally the parameters of the plane are optimized using genetic algorithm. The presented algorithm is tested on EEG signals and the optimum Poincaré plane is obtained with more than 99% data information transferred. Results are compared with some typical method of creating Poinare samples and showed that the transferred information using with this method is higher. The generated samples can be used for feature extraction and further analysis.
生物信号被视为用于诊断和检测异常以及对身体动态进行建模的重要数据来源。这些信号通常使用从时域和频域提取的特征进行分析。理论上，这些动态也可以利用与系统轨迹相交的庞加莱平面进行分析。然而，选择合适的庞加莱平面是提取最佳庞加莱样本的关键部分。由于高度依赖于系统动态，所以没有唯一的方法来选择庞加莱平面。在本研究中，引入了一种新算法，该算法能自动选择一个最优的庞加莱平面，以便将最大信息从脑电图（EEG）时间序列传递到一组庞加莱样本。在该算法中，首先对EEG时间序列进行嵌入；然后设计一个参数化的庞加莱平面，最后使用遗传算法对该平面的参数进行优化。所提出的算法在EEG信号上进行了测试，并获得了最优的庞加莱平面，其传递的数据信息超过99%。将结果与一些创建庞加莱样本的典型方法进行了比较，结果表明使用该方法传递的信息更高。生成的样本可用于特征提取和进一步分析。
