Abstract
Respiratory signal and electrocardiogram are correlated to each other. In this paper, respiration rate has been extracted from ECG. We purpose a novel combination of Canonical Correlation Analysis (CCA) and Ensemble Empirical Mode Decomposition (EEMD) in order to remove the artifacts, and we have estimated the respiratory rate from the denoised ECG by creating the envelope of the denoised signal. The canonical correlation corresponding to the artifacts was removed on the basis of correlation coefficient of denoised signal and ground truth signal. The MIT-Polysomonographic and Apnea-ECG databases of physionet bank were used to acquire the ECG signals. Real-time Baseline wander noise from MIT-NSTDB was added to each record, and the respiratory rate determined was compared with the corresponding respiratory signals. The average signal-to-noise ratio improvement in case of denoising using EEMD-CCA is 20.8989 db. The average BPM error in respiration rate derived from ECG denoised from EEMD is ±2.5 BPM.
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Kumar, V., Singh, G. (2016). ECG-Driven Extraction of Respiration Rate Using Ensemble Empirical Mode Decomposition and Canonical Correlation Analysis. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 381. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2526-3_30
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DOI: https://doi.org/10.1007/978-81-322-2526-3_30
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