Abstract
BackgroundWe study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities. Although respiration effects on heart rate variability and ECG morphology have been well established, studies on ECG-derived respiration in daily living settings are scarce; possibly due to considerable amount of disturbances in such data. Yet, unobtrusive BF estimation during everyday activities can provide vital information for both disease management and athletic performance optimization.Method and dataFor robust ECG-derived BF estimation, we combine the respiratory information derived from R–R interval (RRI) variability and morphological scale variation of QRS complexes (MSV), acquired from ECG signals. Two different fusion techniques are applied on MSV and RRI signals: cross-power spectral density (CPSD) estimation and power spectrum multiplication (PSM). The algorithms were tested on large sets of data collected from 67 participants during office, household and sport activities, simulating daily living activities. We use spirometer reference BF to evaluate and compare our estimations made by different models.Results and conclusionPSM acquires the least average error of BF estimation, %D^{2sigma }=9.86 and %E = 9.45, compared to the reference spirometer values. PSM offers approximately 25 and 75% less error in comparison with the CPSD fusion estimation and the estimation by those two exclusive sources, respectively. Our results demonstrate the superiority of both of the fusion approaches, compared to the estimation derived from either of RRI or MSV signals exclusively.
Highlights
We study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities
The idea was based on the fact that respiratory sinus arrhythmia (RSA)—which is obtainable from heart rate variability (HRV)— correlates with the respiratory pattern
To enhance the estimation of BF during a non-stationary recording situation, we propose to combine the spectral information of BF components of RSA and morphological variation
Summary
We study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities. Respiration effects on heart rate variability and ECG morphology have been well established, studies on ECG-derived respiration in daily living settings are scarce; possibly due to considerable amount of disturbances in such data. Indirect monitoring of respiratory frequency can be conducted in different modalities, including video-based [1], electrical impedance pneumography-based [2] or wearable accelerometer-based respiration reconstruction [3]. Alikhani et al BioMed Eng OnLine (2018) 17:99 modalities explored for BF and respiratory pattern reconstruction, known as ECGderived respiration (EDR). It is feasible to derive respiratory information such as the BF indirectly by analyzing a single-channel ECG signal
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