Objectives

With the technological advancements in the field of tele-health monitoring, it is now possible to gather huge amount of electro-physiological signals such as the electrocardiogram (ECG). It is therefore necessary to develop models/algorithms that are capable of analysing these massive amount of data in real-time. This paper proposes a deep learning model for real-time segmentation of heartbeats.

Methods

The proposed 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-waves, QRS complexes, T-waves, and No waves (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 ECG records of length 15 min each and achieved an average sensitivity and precision of 97.95% and 95.68%, respectively, using a stratified 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.

中文翻译：

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DENS-ECG：用于ECG信号描绘的深度学习方法

目标

随着远程健康监控领域的技术进步，现在有可能收集大量的电生理信号，例如心电图（ECG）。因此，有必要开发能够实时分析这些海量数据的模型/算法。本文提出了一种用于心跳实时细分的深度学习模型。

方法

提出的DENS-ECG算法结合了卷积神经网络（CNN）和长短期记忆（LSTM）模型，以检测不同心跳波形（例如P波，QRS络合物，T波，并且没有波浪（NW）。使用ECG作为输入，模型学习通过训练过程提取高级特征，与其他基于经典机器学习的方法不同，该模型省去了特征工程步骤。

结果

提出的DENS-ECG模型在具有105条ECG记录的数据集上进行了训练和验证，每条记录的长度为15分钟，使用分层5倍交叉验证分别获得了97.95％和95.68％的平均灵敏度和精度。此外，该模型在一个看不见的数据集上进行了评估，以检查其在QRS检测中的鲁棒性，从而得出了99.61％的灵敏度和99.52％的精度。

结论

实验结果表明组合的CNN-LSTM模型用于ECG信号描绘的灵活性和准确性。

意义

本文提出了一种使用深度学习进行心跳细分的有效且易于使用的方法，该方法有可能在实时远程医疗监控系统中使用。