Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal
[摘要] Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.
[发布日期] [发布机构]
[效力级别] [学科分类] 肠胃与肝脏病学
[关键词] electrocardiography;medical signal processing;signal denoising;signal representation;signal reconstruction;muscle;noise-aware dictionary-learning-based sparse representation;noise detection;noise removal;ECG signal analysis;automatic electrocardiogram signal enhancement;sparse signal decomposition;sparse signal reconstruction;moving average filter;first-order difference;temporal features;turning points;maximum absolute amplitude;zero crossings;autocorrelation features;ECG denoising;baseline wanders;power-line interference;muscle artefacts [时效性]