Emission Modelling for Supervised ECG Segmentation using Finite Differences

The segmentation of ECG signals into P waves, QRS complexes, T waves and baselines is an important practical problem for physicians diagnosing cardiac diseases. The duration of the signal and the number of beats to segment are often too large for a manual annotation, so that automatic segmentation is a challenging and useful tool. State-of-the-art algorithms use hidden Markov models with wavelet transform encoding and represent the ECG in multidimensional spaces using Gaussian mixtures models. The main problem of this approach is its computational cost due to the number of free parameters, the choice of the wavelet transform parameters and the high failure rate of the EM algorithm. In this work, we propose an alternative emission encoding for hidden Markov models using both the ECG signal and its derivative in order to better model the dynamics of the signal in a lower dimensional space. We show that this method achieves similar performances with much less model parameters and is less subject to failures.