Key result
EEMD-SDF algorithm reduces respiratory rate estimation error from single-lead ECGs by ~69% versus EEMD-PCA.
Why the study?
Wearable ambulatory systems using ECG signals for simultaneous heart rate and respiratory rate estimation require improved accuracy and cost-effectiveness.
Does an algorithm using ensemble empirical mode decomposition and spectral data fusion improve heart rate and respiratory rate estimation from a single lead ECG compared to existing methods?
Does an algorithm using ensemble empirical mode decomposition and spectral data fusion improve heart rate and respiratory rate estimation from a single lead ECG compared to existing methods?
Absolute Event Rate: 2.2% vs 7.03%
A novel algorithm using ensemble empirical mode decomposition and spectral data fusion accurately estimates both heart rate and respiratory rate from a single-lead ECG.
May enable accurate respiratory rate monitoring from single-lead ECG; extends EEMD-PCA methods but leaves open clinical validation.
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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Chung et al. (2021) studied Healthy (n=57). Ensemble Empirical Mode Decomposition and Spectral Data Fusion (EEMD-SDF) vs. Ensemble Empirical Mode Decomposition with Principal Component Analysis (EEMD-PCA) was evaluated on Mean absolute error (MAE) for respiratory rate estimation (breaths per minute). The EEMD-SDF algorithm estimated respiratory rate from a single-lead ECG with a mean absolute error of 2.20 breaths per minute, outperforming the EEMD-PCA method which had an error of 7.03 breaths per minute.
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