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.
A new electrocardiogram (ECG) compression method based on the combination of the empirical mode decomposition (EMD) and the wavelet transform is presented. ECG signal can be decomposed into a set of intrinsic mode functions by the EMD. The proposed method recomposes the intrinsic mode functions into two groups, and compresses each group separately to fully utilise their data characteristics. The first group can be completely described by its extrema with negligible reconstruction error, while the second group is further decomposed by wavelet transform. With appropriate threshold selecting and the using of the run length coding and the Huffman coding, the proposed method exhibits competitive performances compared with other compressors for ECG compression.
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