Data driven approach to ECG signal quality assessment using multistep SVM classification

In response to the PhysioNet/CinC Challenge 2011: Improving the quality of ECGs collected using mobile phones we have developed an algorithm based on a decision support system. It combines couple of simple rules - in order to discard recordings of obviously low quality (i.e. high-amplitude noise, detached electrodes) with more sophisticated support vector machine (SVM) classification that deals with more difficult cases where simple rules are inefficient. It turns out that complicatedly computed features provide only small information gain and therefore we used for SVM classifier only time-lagged covariance matrix elements, which provide useful information about signal structure in time. Our results are 0.836.