# Finding morphology points of electrocardiographic signal waves using wavelet analysis

arXiv: https://arxiv.org/abs/1808.04804 · HTML: https://mlsanity.com/paper/1808.04804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Deep learning based ECG segmentation for delineation of diverse arrhythmias](https://mlsanity.com/paper/2304.06237/) (2023)

## Abstract (arXiv)

A new algorithm has been developed for delineation of significant points of various electrocardiographic signal (ECG) waves, taking into account information from all available leads and providing similar or higher accuracy in comparison with other modern technologies. The test results for the QT database show a sensitivity above 97% when detecting ECG wave peaks and 96% for their onsets and offsets, as well as better positive predictive value compared to the previously known algorithms. In contrast to the previously published algorithms, the proposed approach also allows one to determine the morphology of waves. The segmentation mean errors of all significant points are below the tolerances defined by the Committee of General Standards for Electrocardiography (CSE).