Optimization of ECG Classification by Means of Feature Selection
Optimization of ECG Classification by Means of Feature Selection
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DOI：
10.1109/tbme.2011.2113395
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发表时间：
2011-08-01
影响因子：
4.6
通讯作者：
Poll, Ruediger
中科院分区：
文献类型：
--
作者：
Mar, Tanis;Zaunseder, Sebastian;Poll, Ruediger
本研究通过一种方法来解决心电分类问题，这种方法能够在提高分类性能的同时减少计算资源，使其特别适合应用于改善步行环境。为此，应用了基于线性判别式的新的准则函数索引的顺序正向浮动搜索(SFFS)算法。这一标准被专门设计为心电图心律失常分类的质量指标。在此基础上，使用SFFS算法对综合特征集进行分析，并使用多层感知器(MLP)对返回的最合适子集进行评估，以评估模型的稳健性。为了获得对真实世界性能的有意义的估计，并便于与类似研究进行比较，本研究遵循医疗器械促进会的EC57：1998标准和先前几项研究中使用的相同的患者间划分方案。结果表明，在保证满足动态监测要求的前提下，应用所提出的方法可以超过同类研究在相同约束条件下的性能。
This study tackles the ECG classification problem by means of a methodology, which is able to enhance classification performance while simultaneously reducing the computational resources, making it specially adequate for its application in the improvement of ambulatory settings. For this purpose, the sequential forward floating search (SFFS) algorithm is applied with a new criterion function index based on linear discriminants. This criterion has been devised specifically to be a quality indicator in ECG arrhythmia classification. Based on this measure, a comprehensive feature set is analyzed with the SFFS algorithm, and the most suitable subset returned is additionally evaluated with a multilayer perceptron (MLP) to assess the robustness of the model. Aiming at obtaining meaningful estimates of the real-world performance and facilitating comparison with similar studies, the present contribution follows the Association for the Advancement of Medical Instrumentation standard EC57: 1998 and the same interpatient division scheme used in several previous studies. Results show that by applying the proposed methods, the performance obtained in similar studies under the same constraints can be exceeded, while keeping the requirements suitable for ambulatory monitoring.