Abstract
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.
Highlights
Wavelet analysis has become one of the most commonly used digital signal processing tools, with applications in data compression, image processing, time series data filtering, material detection and de-noising [1] [2]
Cardiac Output (CO) is defined as the product of Stroke Volume (SV) and Heart Rate (HR), and while HR is a relatively straightforward parameter to asses, SV is much more difficult to accurately assess, and so we have focused on obtaining an accurate non-invasive estimate of SV
We chose to set the maximum value of SSS to 32 assuming the Genetic Algorithms (GAs) could obtain results with a subset much smaller than this
Summary
Wavelet analysis has become one of the most commonly used digital signal processing tools, with applications in data compression, image processing, time series data filtering, material detection and de-noising [1] [2]. Wavelets are well suited for non-stationary time series data analysis wherein the time localization of the frequency components is important. The use of wavelet analysis has increased rapidly in the biomedical field with analysis being applied to remove base line variation and high frequency components from the electrocardiogram (ECG) and to distinguish specific features within the ECG waveform [3] [4]. Wavelet analysis is widely used to de-noise data and to separate observed components where decomposition, thresh-holding, and reconstruction are computed. We wish to avoid the time consuming operation of waveform reconstruction, since the application calls for rapid response from a resource limited device
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