# Deep Learning for ECG Segmentation

arXiv: https://arxiv.org/abs/2001.04689 · HTML: https://mlsanity.com/paper/2001.04689/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 13 of 13)

- [A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks](https://mlsanity.com/paper/2306.07303/) (2023)
- [Learning General Representation of 12-Lead Electrocardiogram with a Joint-Embedding Predictive Architecture](https://mlsanity.com/paper/2410.08559/) (2024)
- [anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding](https://mlsanity.com/paper/2506.00942/) (2025)
- [Deep learning based ECG segmentation for delineation of diverse arrhythmias](https://mlsanity.com/paper/2304.06237/) (2023)
- [A Deep Bayesian Neural Network for Cardiac Arrhythmia Classification with Rejection from ECG Recordings](https://mlsanity.com/paper/2203.00512/) (2022)
- [Investigating Deep Learning Benchmarks for Electrocardiography Signal Processing](https://mlsanity.com/paper/2204.04420/) (2022)
- [Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation](https://mlsanity.com/paper/2111.12996/) (2021)
- [Deep Learning based acoustic measurement approach for robotic applications on orthopedics](https://mlsanity.com/paper/2403.05879/) (2024)
- [Anatomical Region Recognition and Real-time Bone Tracking Methods by Dynamically Decoding A-Mode Ultrasound Signals](https://mlsanity.com/paper/2405.19542/) (2024)
- [Self-Trained Model for ECG Complex Delineation](https://mlsanity.com/paper/2406.02711/) (2024)
- [IKrNet: A Neural Network for Detecting Specific Drug-Induced Patterns in Electrocardiograms Amidst Physiological Variability](https://mlsanity.com/paper/2505.07533/) (2025)
- [SCG With Your Phone: Diagnosis of Rhythmic Spectrum Disorders in Field Conditions](https://mlsanity.com/paper/2601.13926/) (2026)
- [Automated ECG Interval Measurement and Wave Delineation Using Fast Fourier Convolution ResNet](https://mlsanity.com/paper/2608.00058/) (2026)

## Abstract (arXiv)

We propose an algorithm for electrocardiogram (ECG) segmentation using a UNet-like full-convolutional neural network. The algorithm receives an arbitrary sampling rate ECG signal as an input, and gives a list of onsets and offsets of P and T waves and QRS complexes as output. Our method of segmentation differs from others in speed, a small number of parameters and a good generalization: it is adaptive to different sampling rates and it is generalized to various types of ECG monitors. The proposed approach is superior to other state-of-the-art segmentation methods in terms of quality. In particular, F1-measures for detection of onsets and offsets of P and T waves and for QRS-complexes are at least 97.8%, 99.5%, and 99.9%, respectively.