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

2018 ◽  
Vol 22 (3) ◽  
pp. 766-774 ◽  
Author(s):  
Mohammod Abdul Motin ◽  
Chandan Kumar Karmakar ◽  
Marimuthu Palaniswami
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Respiratory Rate ◽  
Empirical Mode Decomposition ◽  
Principal Component ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Novel Approach ◽  
Mode Decomposition

scholarly journals Estimation of Heart Rate and Respiratory Rate from PPG Signal Using Complementary Ensemble Empirical Mode Decomposition with both Independent Component Analysis and Non-Negative Matrix Factorization

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3238
Author(s):  
Ruisheng Lei ◽  
Bingo Wing-Kuen Ling ◽  
Peihua Feng ◽  
Jinrong Chen
Keyword(s):  
Heart Rate ◽  
Independent Component Analysis ◽  
Respiratory Rate ◽  
Empirical Mode Decomposition ◽  
Matrix Factorization ◽  
Component Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Independent Component ◽  
Mode Decomposition ◽  
Non Negative Matrix Factorization

This paper proposes a framework combining the complementary ensemble empirical mode decomposition with both the independent component analysis and the non-negative matrix factorization for estimating both the heart rate and the respiratory rate from the photoplethysmography (PPG) signal. After performing the complementary ensemble empirical mode decomposition on the PPG signal, a finite number of intrinsic mode functions are obtained. Then, these intrinsic mode functions are divided into two groups to perform the further analysis via both the independent component analysis and the non-negative matrix factorization. The surrogate cardiac signal related to the heart activity and another surrogate respiratory signal related to the respiratory activity are reconstructed to estimate the heart rate and the respiratory rate, respectively. Finally, different records of signals acquired from the Medical Information Mart for Intensive Care database downloaded from the Physionet Automated Teller Machine (ATM) data bank are employed for demonstrating the outperformance of our proposed method. The results show that our proposed method outperforms both the digital filtering approach and the conventional empirical mode decomposition based methods in terms of reconstructing both the surrogate cardiac signal and the respiratory signal from the PPG signal as well as both achieving the higher accuracy and the higher reliability for estimating both the heart rate and the respiratory rate.

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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1184
Author(s):  
Iau-Quen Chung ◽  
Jen-Te Yu ◽  
Wei-Chi Hu
Keyword(s):  
Heart Rate ◽  
Data Fusion ◽  
Respiratory Rate ◽  
Spectral Data ◽  
Empirical Mode Decomposition ◽  
Cost Effective ◽  
Absolute Error ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition

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.

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