Developing an Evolutionary Algorithm to Search for an Optimal Multi-Mother Wavelet Packets Combination

• 作者: J. David Schaffer Kenneth J. McLeod Ohad Bar Siman Tov
• 来源期刊: 生物医学工程(英文)
• 学科: 医学
• 关键词: WAVELET TRANSFORM Data Mining Genetic Algorithm
• 年，卷（期）: 2015,（7）
• 页码范围: 458-470
• 页数: 13页
• 分类号: R73

摘要

The wavelet transform is a popular analysis tool for non-stationary data, but in many cases, the choice of the mother wavelet and basis set remains uncertain, particularly when dealing with physiological data. Furthermore, the possibility exists for combining information from numerous mother wavelets so as to exploit different features from the data. However, the combinatorics become daunting given the large number of basis sets that can be utilized. Recent work in evolutionary computation has produced a subset selection genetic algorithm specifically aimed at the discovery of small, high-performance, subsets from among a large pool of candidates. Our aim was to apply this algorithm to the task of locating subsets of packets from multiple mother wavelet decompositions to estimate cardiac output from chest wall motions while avoiding the computational cost of full signal reconstruction. We present experiments which show how a continuous assessment metric can be extracted from the wavelets coefficients, but the dual-objective nature of the algorithm (high accuracy with small feature sets) imposes a need to restrict the sensitivity of the continuous accuracy metric in order to achieve the small subset size desired. A possibly subtle tradeoff seems to be needed to meet the dual objectives.