Abstract: The morphological identification of QRS complex is a key in the detection of abnormal ECG, which acts as the basis for disease diagnosis. The existing QRS morphological recognition methods either identify only a few morphologies, or are sensitive to parameter settings, and the performance is not ideal. Based on this, a sliding window voting strategy based on hidden Markov model (SWVHMM) is proposed to automatically identify QRS morphologies. Firstly, each QRS complex is divided into four phases, and a sliding window is set for each phase to extract samples. Secondly, the waveform of each phase is regarded as a state, and the cluster center of the window samples acts as the observation to construct a state-constrained Hidden Marko model. Finally, we vote on the result of the combination of different phase windows to identify the target morphology pattern with the largest possibility. On the real data set labelled by professional doctors, compared with existing methods, our method improves F1 measure by 5.97% ,5.49% and 2.27%, respectively. The results show that SWVHMM can identify a variety of morphology patterns with improved accuracy.

Key words: QRS complex, ECG abnormality, hidden Markov, wave band, clustering