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

TL;DR

This paper introduces DENS-ECG, a deep learning model combining CNN and LSTM for real-time ECG heartbeat segmentation, achieving high accuracy and robustness, suitable for tele-health applications.

Contribution

It presents a novel deep learning approach that eliminates feature engineering for ECG delineation, enhancing real-time analysis capabilities.

Findings

Achieved over 97% sensitivity and 95% precision in validation.

Demonstrated robustness with over 99% sensitivity and precision on unseen data.

Proven effective for real-time heartbeat segmentation in tele-health systems.

Abstract

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.