Accurate automatic identification of fiducial points within an ECG is required for the automatic interpretation of this signal. Several methods exist in the literature for automatic ECG segmentation. These algorithms are based on different methodologies and often evaluated with different datasets and protocols, which makes their performance challenging to compare. For this study, nine segmentation algorithms were selected from the literature and evaluated with the same protocol in order to study their performance. One hundred signals from the PhysioNet's QT database were used for this evaluation. Results showed that one of the algorithms based in the discrete wavelet transform achieved sensitivity of 100% when detecting the onset and offset of the QRS complex. An algorithm using the Multi-scale Morphological Derivate achieved sensitivities of 99.81%, 98.17% and 99.56% when detecting the peak, onset and offset respectively of the P-wave. When segmenting the T-wave, an algorithm based on the Phasor transform had a good performance with sensitivities of 97.78%, 97.81% and 95.43% when detecting the peak, onset and offset, respectively. Additionally, probabilistic methods such as Hidden Markov Models had good results due to the fact that they can learn from real signals and adapt to specific conditions. However, these techniques are often computationally more complex and require training. This study could help in selecting optimal algorithms for ECG segmentation when implementing a system for automatic ECG interpretation. (C) 2017 Elsevier Ltd. All rights reserved.
