Key result
Single-lead APCA-PDDTW ECG segmentation shows comparable mean error to Laguna's two-lead method despite higher standard deviation.
Why the study?
Manual annotation of ECGs is time consuming, and existing automatic ECG segmentation algorithms require evaluation and improvement.
A novel single-lead ECG segmentation method using APCA and PDDTW achieves comparable mean error to established two-lead methods, though with slightly higher variance.
Electrocardiogram (ECG) segmentation is necessary to help reduce the time consuming task of manually annotating ECG's. Several algorithms have been developed to segment the ECG automatically. We first review several of such methods, and then present a new single lead segmentation method based on Adaptive piecewise constant approximation (APCA) and Piecewise derivative dynamic time warping (PDDTW). The results are tested on the QT database. We compared our results to Laguna's two lead method. Our proposed approach has a comparable mean error, but yields a slightly higher standard deviation than Laguna's method.
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Zifan et al. (2007) studied ECG segmentation. Single lead segmentation method based on Adaptive piecewise constant approximation (APCA) and Piecewise derivative dynamic time warping (PDDTW) vs. Laguna's two lead method was evaluated on Mean error and standard deviation. A single lead ECG segmentation method based on APCA and PDDTW demonstrated comparable mean error but slightly higher standard deviation compared to Laguna's two lead method.
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