Year: 2011
Type: article
Abstract: 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 bewteen accuracy and computational simplicity.Further improving the accuracy, however, turns out to be hardly feasible considering the chosen feature space.
Source: OPUS (Augsburg University)
Institution: Technische Universität Dresden
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2120015911