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Sensors
Volume 21
Issue 4
10.3390/s21041184
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Article

Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion

by
Iau-Quen Chung
1,
Jen-Te Yu
2 and
Wei-Chi Hu
1,*
1
The Department of Biomedical Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
2
The Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Ki H. Chon
Sensors 2021, 21(4), 1184; https://doi.org/10.3390/s21041184
Received: 26 December 2020 / Revised: 4 February 2021 / Accepted: 5 February 2021 / Published: 8 February 2021
(This article belongs to the Section Wearables)
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Abstract

Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods. View Full-Text
Keywords: electrocardiogram (ECG); photoplethysmogram (PPG); ensemble empirical mode decomposition (EEMD); intrinsic mode function (IMF); principal component analysis (PCA); ensemble empirical mode decomposition with principal component analysis (EEMD-PCA); ensemble empirical mode decomposition with spectral data fusion (EEMD-SDF); heart rate (HR); respiratory rate (RR); ECG derived respiration (EDR) electrocardiogram (ECG); photoplethysmogram (PPG); ensemble empirical mode decomposition (EEMD); intrinsic mode function (IMF); principal component analysis (PCA); ensemble empirical mode decomposition with principal component analysis (EEMD-PCA); ensemble empirical mode decomposition with spectral data fusion (EEMD-SDF); heart rate (HR); respiratory rate (RR); ECG derived respiration (EDR)
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MDPI and ACS Style

Chung, I.-Q.; Yu, J.-T.; Hu, W.-C. Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion. Sensors 2021, 21, 1184. https://doi.org/10.3390/s21041184

AMA Style

Chung I-Q, Yu J-T, Hu W-C. Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion. Sensors. 2021; 21(4):1184. https://doi.org/10.3390/s21041184

Chicago/Turabian Style

Chung, Iau-Quen, Jen-Te Yu, and Wei-Chi Hu. 2021. "Estimating Heart Rate and Respiratory Rate from a Single Lead Electrocardiogram Using Ensemble Empirical Mode Decomposition and Spectral Data Fusion" Sensors 21, no. 4: 1184. https://doi.org/10.3390/s21041184

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