Data Fusion for Improved Respiration Rate Estimation
<p/> <p>We present an application of a modified Kalman-Filter (KF) framework for data fusion to the estimation of respiratory rate from multiple physiological sources which is robust to background noise. A novel index of the underlying signal quality of respiratory signals is presented a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2010-01-01
|
| Series: | EURASIP Journal on Advances in Signal Processing |
| Online Access: | http://asp.eurasipjournals.com/content/2010/926305 |
| _version_ | 1811220249255608320 |
|---|---|
| author | Malhotra Atul Nemati Shamim Clifford GariD |
| author_facet | Malhotra Atul Nemati Shamim Clifford GariD |
| author_sort | Malhotra Atul |
| collection | DOAJ |
| description | <p/> <p>We present an application of a modified Kalman-Filter (KF) framework for data fusion to the estimation of respiratory rate from multiple physiological sources which is robust to background noise. A novel index of the underlying signal quality of respiratory signals is presented and then used to modify the noise covariance matrix of the KF which discounts the effect of noisy data. The signal quality index, together with the KF innovation sequence, is also used to weight multiple independent estimates of the respiratory rate from independent KFs. The approach is evaluated both on a realistic artificial ECG model (with real additive noise) and on real data taken from 30 subjects with overnight polysomnograms, containing ECG, respiration, and peripheral tonometry waveforms from which respiration rates were estimated. Results indicate that our automated voting system can out-perform any individual respiration rate estimation technique at all levels of noise and respiration rates exhibited in our data. We also demonstrate that even the addition of a noisier extra signal leads to an improved estimate using our framework. Moreover, our simulations demonstrate that different ECG respiration extraction techniques have different error profiles with respect to the respiration rate, and therefore a respiration rate-related modification of any fusion algorithm may be appropriate.</p> |
| first_indexed | 2024-04-12T07:38:29Z |
| format | Article |
| id | doaj.art-ee0ab3ae855e42839d5836ec05397adc |
| institution | Directory Open Access Journal |
| issn | 1687-6172 1687-6180 |
| language | English |
| last_indexed | 2024-04-12T07:38:29Z |
| publishDate | 2010-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-ee0ab3ae855e42839d5836ec05397adc2022-12-22T03:41:53ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-0120101926305Data Fusion for Improved Respiration Rate EstimationMalhotra AtulNemati ShamimClifford GariD<p/> <p>We present an application of a modified Kalman-Filter (KF) framework for data fusion to the estimation of respiratory rate from multiple physiological sources which is robust to background noise. A novel index of the underlying signal quality of respiratory signals is presented and then used to modify the noise covariance matrix of the KF which discounts the effect of noisy data. The signal quality index, together with the KF innovation sequence, is also used to weight multiple independent estimates of the respiratory rate from independent KFs. The approach is evaluated both on a realistic artificial ECG model (with real additive noise) and on real data taken from 30 subjects with overnight polysomnograms, containing ECG, respiration, and peripheral tonometry waveforms from which respiration rates were estimated. Results indicate that our automated voting system can out-perform any individual respiration rate estimation technique at all levels of noise and respiration rates exhibited in our data. We also demonstrate that even the addition of a noisier extra signal leads to an improved estimate using our framework. Moreover, our simulations demonstrate that different ECG respiration extraction techniques have different error profiles with respect to the respiration rate, and therefore a respiration rate-related modification of any fusion algorithm may be appropriate.</p>http://asp.eurasipjournals.com/content/2010/926305 |
| spellingShingle | Malhotra Atul Nemati Shamim Clifford GariD Data Fusion for Improved Respiration Rate Estimation EURASIP Journal on Advances in Signal Processing |
| title | Data Fusion for Improved Respiration Rate Estimation |
| title_full | Data Fusion for Improved Respiration Rate Estimation |
| title_fullStr | Data Fusion for Improved Respiration Rate Estimation |
| title_full_unstemmed | Data Fusion for Improved Respiration Rate Estimation |
| title_short | Data Fusion for Improved Respiration Rate Estimation |
| title_sort | data fusion for improved respiration rate estimation |
| url | http://asp.eurasipjournals.com/content/2010/926305 |
| work_keys_str_mv | AT malhotraatul datafusionforimprovedrespirationrateestimation AT nematishamim datafusionforimprovedrespirationrateestimation AT cliffordgarid datafusionforimprovedrespirationrateestimation |