College of Information Science and Electronic Engineering, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
Institute of VLSI Design, Zhejiang University, Zheda Road 38, Hangzhou, 310027, China.
Biomed Eng Online. 2018 Jan 29;17(1):13. doi: 10.1186/s12938-018-0441-4.
The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances.
In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted.
Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values.
An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.
QRS 复合波是心电图（ECG）信号中最显著的特征，因此其检测对于 ECG 信号分析至关重要。现有的检测方法在很大程度上依赖于手工制作的人工特征和参数，这可能会引入显著的计算复杂性，尤其是在变换域中。此外，固定的特征和参数不适合在不同情况下检测各种 QRS 复合波。
在这项研究中，基于一维卷积神经网络（CNN），提出了一种用于 QRS 复合波检测的精确方法。该 CNN 由目标级和部分级 CNN 组成，用于自动提取不同粒度的 ECG 形态特征。所有提取的形态特征都由多层感知器（MLP）用于 QRS 复合波检测。此外，采用了仅在时域中包含差分运算的简单 ECG 信号预处理技术。
基于麻省理工学院生物医学工程系心律失常数据库（MIT-BIH-AR），所提出的检测方法的整体灵敏度 Sen = 99.77%，阳性预测率 PPR = 99.91%，检测误差率 DER = 0.32%。此外，还根据不同的信噪比（SNR）值进行了性能变化分析。
提出了一种使用两级一维 CNN 和简单信号预处理技术的自动 QRS 复合波检测方法。与现有的 QRS 复合波检测方法相比，实验结果表明，所提出的方法具有相当的准确性。
