# DENS-ECG: A Deep Learning Approach for ECG Signal Delineation

arXiv: https://arxiv.org/abs/2005.08689 · HTML: https://mlsanity.com/paper/2005.08689/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 14 of 14)

- [AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings](https://mlsanity.com/paper/2411.17711/) (2024)
- [ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning](https://mlsanity.com/paper/2304.04646/) (2023)
- [Tackling the muon identification in water Cherenkov detectors problem for the future Southern Wide-field Gamma-ray Observatory by means of Machine Learning](https://mlsanity.com/paper/2101.11924/) (2021)
- [Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report](https://mlsanity.com/paper/2304.06286/) (2023)
- [Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks](https://mlsanity.com/paper/2404.15367/) (2024)
- [A Comprehensive Benchmark for Electrocardiogram Time-Series](https://mlsanity.com/paper/2507.14206/) (2025)
- [Training neural networks with synthetic electrocardiograms](https://mlsanity.com/paper/2111.06175/) (2021)
- [Self-Trained Model for ECG Complex Delineation](https://mlsanity.com/paper/2406.02711/) (2024)
- [An Efficient and Flexible Deep Learning Method for Signal Delineation via Keypoints Estimation](https://mlsanity.com/paper/2407.20258/) (2024)
- [A Network Science Approach to Granular Time Series Segmentation](https://mlsanity.com/paper/2505.17640/) (2025)
- [Physics-Based Explainable AI for ECG Segmentation: A Lightweight Model](https://mlsanity.com/paper/2508.15872/) (2025)
- [Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals](https://mlsanity.com/paper/2605.18483/) (2026)
- [Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations](https://mlsanity.com/paper/2608.09053/) (2026)
- [Decoder Design Matters for ECG Delineation](https://mlsanity.com/paper/2609.16489/) (2026)

## Abstract (arXiv)

Objectives: With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amounts of electro-physiological signals such as electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amounts of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats. Methods: The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model to detect onset, peak, and offset of different heartbeat waveforms such as the P-wave, QRS complex, T-wave, and No wave (NW). Using ECG as the inputs, the model learns to extract high level features through the training process, which, unlike other classical machine learning based methods, eliminates the feature engineering step. Results: The proposed DENS-ECG model was trained and validated on a dataset with 105 ECGs of length 15 minutes each and achieved an average sensitivity and precision of 97.95% and 95.68%, respectively, using a 5-fold cross validation. Additionally, the model was evaluated on an unseen dataset to examine its robustness in QRS detection, which resulted in a sensitivity of 99.61% and precision of 99.52%. Conclusion: The empirical results show the flexibility and accuracy of the combined CNN-LSTM model for ECG signal delineation. Significance: This paper proposes an efficient and easy to use approach using deep learning for heartbeat segmentation, which could potentially be used in real-time tele-health monitoring systems.