Classification of the electrocardiogram using selected wavelet coefficients and linear discriminants

Twenty-live wavelet coefficients were selected as inputs and cross-validation used to estimate the classifier performance. An overall accuracy of 72.3% was achieved using a database of 500 ECG records independently classified into seven classes. This compared well with published cardiologist classification rates. By introducing a no-classification state, the accuracy increased to 7.9% with 80% of ECG records classified. The method presented here is not specific to the ECG domain and may easily be applied to other classification problems.