Genetic design of feature spaces for pattern classifiers
Artificial Intelligence in Medicine
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We present a signal compression scheme based on coding linear segments approximating the signal. Although the approach is useful for many types of signals, we focus in this paper on compression of electrocardiogram (EGG) signals. The ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated that exact optimization algorithms outclass these heuristic approaches by a wide margin with respect to the reconstruction error. The exact optimization algorithm extracts signal samples from the original signal by formulating the sample selection problem as a graph theory problem. Thus known optimization theory can be applied in order to yield optimal compression. This paper generalizes the exact optimization scheme by removing the interpolation restriction when applying piecewise linear approximation. This guarantees a lower reconstruction error with respect to the number of extracted signal samples. The method shows superior performance compared to traditional ECG compression methods.