Probabilistic estimation of respiratory rate using gaussian processes
The presence of respiratory information within the electrocardiogram (ECG) signal is a well-documented phenomenon. We present a Gaussian process framework for the estimation of respiratory rate from the different sources of modulation in a single-lead ECG. We propose a periodic covariance function t...
| Main Authors: | , , , , |
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| Format: | Conference item |
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Institute of Electrical and Electronics Engineers
2013
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| _version_ | 1797096096220053504 |
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| author | Pimentel, M Clifton, D Clifton, L Tarassenko, L IEEE |
| author_facet | Pimentel, M Clifton, D Clifton, L Tarassenko, L IEEE |
| author_sort | Pimentel, M |
| collection | OXFORD |
| description | The presence of respiratory information within the electrocardiogram (ECG) signal is a well-documented phenomenon. We present a Gaussian process framework for the estimation of respiratory rate from the different sources of modulation in a single-lead ECG. We propose a periodic covariance function to model the frequency- and amplitude-modulation time series derived from the ECG, where the hyperparameters of the process are used to derive the respiratory rate. The approach is evaluated using data taken from 40 healthy subjects each with 2 hours of monitoring, containing ECG and respiration waveforms. Results indicate that the accuracy of our proposed method is comparable with that of existing methods, but with the advantages of a principled probabilistic approach, including the direct quantification of the uncertainty in the estimation. © 2013 IEEE. |
| first_indexed | 2024-03-07T04:37:10Z |
| format | Conference item |
| id | oxford-uuid:d0598b36-92c4-4487-9eb9-f9f9439fbb04 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:37:10Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | oxford-uuid:d0598b36-92c4-4487-9eb9-f9f9439fbb042022-03-27T07:49:21ZProbabilistic estimation of respiratory rate using gaussian processesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:d0598b36-92c4-4487-9eb9-f9f9439fbb04Symplectic Elements at OxfordInstitute of Electrical and Electronics Engineers2013Pimentel, MClifton, DClifton, LTarassenko, LIEEEThe presence of respiratory information within the electrocardiogram (ECG) signal is a well-documented phenomenon. We present a Gaussian process framework for the estimation of respiratory rate from the different sources of modulation in a single-lead ECG. We propose a periodic covariance function to model the frequency- and amplitude-modulation time series derived from the ECG, where the hyperparameters of the process are used to derive the respiratory rate. The approach is evaluated using data taken from 40 healthy subjects each with 2 hours of monitoring, containing ECG and respiration waveforms. Results indicate that the accuracy of our proposed method is comparable with that of existing methods, but with the advantages of a principled probabilistic approach, including the direct quantification of the uncertainty in the estimation. © 2013 IEEE. |
| spellingShingle | Pimentel, M Clifton, D Clifton, L Tarassenko, L IEEE Probabilistic estimation of respiratory rate using gaussian processes |
| title | Probabilistic estimation of respiratory rate using gaussian processes |
| title_full | Probabilistic estimation of respiratory rate using gaussian processes |
| title_fullStr | Probabilistic estimation of respiratory rate using gaussian processes |
| title_full_unstemmed | Probabilistic estimation of respiratory rate using gaussian processes |
| title_short | Probabilistic estimation of respiratory rate using gaussian processes |
| title_sort | probabilistic estimation of respiratory rate using gaussian processes |
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