Ensemble Empirical Mode Decomposition With Principal Component Analysis: A Novel Approach for Extracting Respiratory Rate and Heart Rate From Photoplethysmographic Signal

• Published in: IEEE journal of biomedical and health informatics Vol. 22; no. 3; pp. 766 - 774
• Main Authors: Motin, Mohammod Abdul, Karmakar, Chandan Kumar, Palaniswami, Marimuthu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; Aged; Algorithms; Band-pass filters; Blood volume; Child; Child, Preschool; Data banks; EKG; Electrocardiography; Empirical analysis; Empirical mode decomposition; Ensemble empirical mode decomposition (EEMD); ensemble empirical mode decomposition with principal component analysis (EEMD-PCA); Female; Heart diseases; Heart rate; Heart Rate - physiology; Humans; Infant; Male; Middle Aged; MIMICs; Monitoring; photoplethysmographic (PPG) signal; Photoplethysmography - methods; Physiological effects; Principal Component Analysis; principal component analysis (PCA); Principal components analysis; Quartiles; Respiration; Respiratory rate; Respiratory Rate - physiology; respiratory rate estimation; Signal Processing, Computer-Assisted; Young Adult
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2017.2679108

The photoplethysmographic (PPG) signal measures the local variations of blood volume in tissues, reflecting the peripheral pulse modulated by cardiac activity, respiration, and other physiological effects. Therefore, PPG can be used to extract the vital cardiorespiratory signals like heart rate (HR), and respiratory rate (RR) and this will reduce the number of sensors connected to the patient's body for recording these vital signs. In this paper, we propose an algorithm based on ensemble empirical mode decomposition with principal component analysis (EEMD-PCA) as a novel approach to estimate HR and RR simultaneously from PPG signal. To examine the performance of the proposed algorithm, we used 310 (from 35 subjects) and 632 (from 42 subjects) epochs of simultaneously recorded electrocardiogram, PPG, and respiratory signal extracted from MIMIC (Physionet ATM data bank) and Capnobase database, respectively. Results of EEMD-PCA-based extraction of HR and RR from PPG signal showed that the median RMS error (1st and 3rd quartiles) obtained in MIMIC data set for RR was 0.89 (0, 1.78) breaths/min, for HR was 0.57 (0.30, 0.71) beats/min and in Capnobase data set it was 2.77 (0.50, 5.9) breaths/min and 0.69 (0.54, 1.10) beats/min for RR and HR, respectively. These results illustrated that the proposed EEMD-PCA approach is more accurate in estimating HR and RR than other existing methods. Efficient and reliable extraction of HR and RR from the pulse oximeter's PPG signal will help patients for monitoring HR and RR with low cost and less discomfort.