Key result
A single-lead automated ECG segmentation method based on dynamic time warping produced a smaller mean error but a higher standard deviation compared to Laguna's two-lead method on the QT database.
Why the study?
Does a single-lead method based on dynamic time warping (DTW) improve automated ECG segmentation compared to Laguna's two-lead method?
Does a single-lead method based on dynamic time warping (DTW) improve automated ECG segmentation compared to Laguna's two-lead method?
A new single-lead DTW-based method for automated ECG segmentation yields a smaller mean error but higher standard deviation compared to an established two-lead method.
Mixed DTW performance precludes immediate clinical adoption of single-lead segmentation; leaves open optimization versus established multi-lead methods.
To detect an abnormal conduction of the heart, cardiologists annotate certain points in the electrocardiogram (ECG) manually. As this is a very strenuous task, several algorithms have been developed to segment the ECG automatically. In this paper, we describe several such methods, and we further present a new single-lead method based on dynamic time warping (DTW). The results are tested on the QT database and compared to Laguna et al.'s (1997) two-lead method. DTW produces a smaller mean error, but has a higher standard deviation than Laguna's method.
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Vullings et al. (2002) studied Abnormal conduction of the heart. Single-lead method based on dynamic time warping (DTW) vs. Laguna et al.'s (1997) two-lead method was evaluated on Mean error and standard deviation of ECG segmentation. A single-lead automated ECG segmentation method based on dynamic time warping produced a smaller mean error but a higher standard deviation compared to Laguna's two-lead method on the QT database.
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