Institute of Medical Technology and Equipment, Roosevelt St 118, 41-800 Zabrze, Poland.
Artif Intell Med. 2012 Feb;54(2):125-34. doi: 10.1016/j.artmed.2011.09.007. Epub 2011 Oct 13.
We propose and develop a concept of a granular representation of a collection of signals (patterns) where a prototype (representative) of such numeric signals is formed as a certain information granule (say, a set, fuzzy set, rough set, and alike) instead of a single numeric entity. As being more abstract, the granular format of the representative of the family of signals is more in rapport with the nature of the representation task itself. It is instrumental in quantifying the diversity of data and capture their inherent distribution characteristics.
In the realization of the granular representation of the signals, we introduce a certain level of granularity (supplied in advance), which in the construction of the granular representative is regarded as an essential important modeling asset. A two-phase design is developed whose ultimate goal is to optimally allocate (distribute) the predefined level of granularity to the individual elements of the universe of discourse over which the signals are described. Given the nature of the required optimization, the ensuing optimization problem is solved by engaging a machinery of population-based optimization, namely Particle Swarm Optimization (PSO). Furthermore a number of information granularity distribution protocols are proposed. The numerical experiments completed for synthetic data and ECG MIT-BIH database signals are used to demonstrate the performance of the overall optimization algorithm and quantify the effectiveness of the allocation of information granularity realized by the PSO. An area under curve (AUC) criterion is proposed as a measure to express the quality of the overall optimization framework.
For both synthetic as well as ECG signals, it is shown that the method endowed with the PSO identifies the best prototype and spans the lower and upper bounds of its granular counterpart. In addition to the numeric quantification of the best (optimized) granular prototype, the method helps visualizing its bounds. The relative difference in mapping performance between the best and the weakest granular prototypes is in the range of 18% (for normal ECG complexes) and over 26% in case of complexes of premature ventricular contraction.
A complete algorithm of the construction of granular prototypes is presented. Treating the granular prototype as a template of a given class of electrocardiogram (ECG) signals, a matching process is facilitated and used as a basis for the design of signal classification algorithms. Various realizations of granular prototypes can be completed with the use of fuzzy sets or rough sets.
我们提出并开发了一种信号（模式）集合的粒度表示概念，其中此类数值信号的原型（代表）被形成为某种信息粒度（例如，集合、模糊集、粗糙集等）而不是单个数值实体。作为更抽象的表示，信号族代表的粒度格式与表示任务本身的性质更加一致。它有助于量化数据的多样性并捕获其内在分布特征。
在实现信号的粒度表示时，我们引入了一定程度的粒度（预先提供），在构建粒度表示时，该粒度被视为必不可少的建模资产。开发了两阶段设计，其最终目标是将预定义的粒度水平最佳分配（分布）到描述信号的论域的各个元素上。鉴于所需优化的性质，通过使用基于种群的优化机制，即粒子群优化（PSO）来解决随后的优化问题。此外，还提出了一些信息粒度分布协议。针对合成数据和 ECG MIT-BIH 数据库信号完成的数值实验用于演示整体优化算法的性能，并量化由 PSO 实现的信息粒度分配的有效性。提出了曲线下面积（AUC）标准作为表达整体优化框架质量的度量。
对于合成数据和 ECG 信号，都表明该方法具有 PSO 的功能，可以识别最佳原型，并跨越其粒度对应物的下限和上限。除了对最佳（优化）粒度原型进行数值量化外，该方法还有助于可视化其边界。最佳和最弱粒度原型之间的映射性能差异在 18%（正常 ECG 复合波）范围内，在室性早搏复合波的情况下超过 26%。
提出了构建粒度原型的完整算法。将粒度原型视为给定类别的心电图（ECG）信号的模板，便于进行匹配过程，并用作信号分类算法的设计基础。可以使用模糊集或粗糙集来完成各种粒度原型的实现。
