An automatic QRS detection method using a two-level 1-D convolutional neural network achieved an overall sensitivity of 99.77%, positive predictivity rate of 99.91%, and detection error rate of 0.32%.
Does a two-level 1-D CNN improve QRS complex detection accuracy in ECG signals compared to existing methods?
A novel two-level 1-D CNN approach for QRS complex detection achieves high sensitivity and positive predictivity, offering a computationally efficient alternative to manual feature extraction.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Xiang et al. (2018) studied Arrhythmia (ECG signal analysis). Two-level 1-D convolutional neural network (CNN) vs. State-of-the-art QRS complex detection approaches was evaluated on Overall sensitivity for QRS complex detection (MIT-BIH-AR database). An automatic QRS detection method using a two-level 1-D convolutional neural network achieved an overall sensitivity of 99.77%, positive predictivity rate of 99.91%, and detection error rate of 0.32%.