Alikhani, I., Noponen, K., Hautala, A., Ammann, R., Seppänen, T. (2018) Spectral fusion-based breathing frequency estimation; experiment on activities of daily living. BioMedical Engineering OnLine, 17, 99. https://doi.org/10.1186/s12938-018-0533-1
Spectral fusion-based breathing frequency estimation : experiment on activities of daily living
|Author:||Alikhani, Iman1; Noponen, Kai1; Hautala, Arto1;|
1Physiological Signal Analysis Team, Center for Machine Vision and Signal Analysis, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland
2Swiss Federal Institute of Sport, Hauptstrasse 247, 2532 Magglingen, Switzerland
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018080933574
|Publish Date:|| 2018-09-21
Background: We 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 data: For 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 conclusion: PSM acquires the least average error of BF estimation, %D²ᵟ=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.
Biomedical engineering online
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
217 Medical engineering
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