Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring

Objective: Automatic detection and classification of noises can play a vital role in the development of robust unsupervised electrocardiogram (ECG) analysis systems. This paper proposes a novel unified framework for automatic detection, localization, and classification of single and combined ECG noises. Methods: The proposed framework consists of the modified ensemble empirical mode decomposition (CEEMD), the short-term temporal feature extraction, and the decision-rule-based noise detection and classification. In the proposed framework, ECG signals are first decomposed using the modified CEEMD algorithm for discriminating the ECG components from the noises and artifacts. Then, the short-term temporal features such as maximum absolute amplitude, number of zerocrossings, and local maximum peak amplitude of the autocorelation function are computed from the extracted high-frequency and low-frequency signals. Finally, a decision rule-based algorithm is presented for detecting the presence of noises and classifying the processed ECG signals into six signal groups: noise-free ECG, ECG+BW, ECG+MA, ECG+PLI, ECG+BW+PLI, and ECG+BW+MA. Results: The proposed framework is rigorously evaluated on five benchmark ECG databases and the real-time ECG signals. The proposed framework achieves an average sensitivity of 99.12%, specificity of 98.56%, and overall accuracy of 98.90% in detecting the presence of noises. Classification results show that the framework achieves an average sensitivity, positive predictivity, and classification accuracy of 98.93%, 98.39%, and 97.38%, respectively. Conclusion: The proposed framework not only achieves better noise detection and classification rates than the current state-of-the-art methods but also accurately localizes short bursts of noises with low end-point delineation errors. Significance: Extensive studies on benchmark databases demonstrate that the proposed framework is more suitable for reducing false alarm rates and selecting appropriate noise-specific denoising techniques in automated ECG analysis applications.