ECG-Driven Extraction of Respiration Rate Using Ensemble Empirical Mode Decomposition and Canonical Correlation Analysis

  • Vineet Kumar
  • Gurpreet Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)


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.


Canonical correlation analysis (CCA) Ensemble empirical mode decomposition (EEMD) Intrinsic mode function (IMF) Canonical component (CC) 


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Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringLovely Professional UniversityPhagwaraIndia

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