Automated analysis of ECG waveforms with atypical QRS complex morphologies

• This work proposes an algorithm for the delineation of atypical ECG waveforms.
• It is identified that most ECG waveforms fall into nine broad categories.
• Curve fitting and derivative based approaches with adaptive thresholds are used.
• Spatial and temporal coherence of ECG leads are exploited to improve accuracy.
• High accuracy of 99.06% is observed.

Automated detection of the various features of an electrocardiogram (ECG) waveform has wide applications in clinical diagnosis. Although detection of typical QRS waveforms has been widely studied, detection of atypical waveforms with complex morphologies remains challenging. The importance of detecting these complex waveforms and their patterns has grown recently due to their clinical implications. In this paper, we propose a novel algorithm for detecting the various peaks of such complex ECG waveforms. It is identified that most of the well-formed ECG waveforms – both typical and complex – fall into nine broad categories according to the standard nomenclature. Motivated by this ECG waveform classification, our algorithm uses signal analysis techniques such as first and second derivatives and adaptive thresholds to classify these waveforms accordingly by detecting the various features present in them. Temporal coherence along a single lead as well as spatial coherence across the 12 leads are used to improve performance. For waveform and pattern analysis, data from 50 healthy subjects and 50 patients with myocardial infarction were randomly selected. Results with an overall sensitivity of 99.06% and overall positive predictive value of 98.89% validate the effectiveness of the approach. Further, the algorithm gives true detections even on waveforms with fluctuations in baseline and wave amplitudes, proving its robustness against such variations.