CinC challenge — Assessing the usability of ECG by ensemble decision trees

For various biomedical applications, an automated quality assessment is an essential but also complex task. Ensembles of decision trees (EDTs) have proven to be a suitable choice for such classification tasks. Within this contribution we invoke EDTs to assess the usability of ECGs. Our classification relies on the usage of simple spectral features which were derived directly from individual ECG channels. EDTs are generated by bootstrap aggregating while invoking the concept of random forrests. Though their simplicity, the trained ensemble classifiers turned out to be a very robust choice yielding an accuracy of 90.4 %. Therewith, the proposed method offers a good tradeoff between accuracy and computational simplicity. Further improving the accuracy, however, turns out to be hardly feasible considering the chosen feature space.