Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm
Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the gradient boostin...
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| Veröffentlicht in: | Applied sciences Jg. 12; H. 16; S. 8355 |
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| Abstract | Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the gradient boosting algorithm is based on effective iteration strategies. This gradient boosting algorithm is just beginning to be used for respiratory rate estimation. Based on this, we propose a novel methodology combining an autocorrelation function-based power spectral feature extraction process with the gradient boosting algorithm to estimate respiratory rate since we acquire the respiration frequency using the autocorrelation function-based power spectral feature extraction that finds the time domain’s periodicity. The proposed methodology solves overfitting for the training datasets because we obtain the data dimension by applying autocorrelation function-based power spectral feature extraction and then split the long-resampled wave signal to increase the number of input data samples. The proposed model provides accurate respiratory rate estimates and offers a solution for reliably managing the estimation uncertainty. In addition, the proposed method presents a more precise estimate than conventional respiratory rate measurement techniques. |
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| AbstractList | Various machine learning models have been used in the biomedical engineering field, but only a small number of studies have been conducted on respiratory rate estimation. Unlike ensemble models using simple averages of basic learners such as bagging, random forest, and boosting, the gradient boosting algorithm is based on effective iteration strategies. This gradient boosting algorithm is just beginning to be used for respiratory rate estimation. Based on this, we propose a novel methodology combining an autocorrelation function-based power spectral feature extraction process with the gradient boosting algorithm to estimate respiratory rate since we acquire the respiration frequency using the autocorrelation function-based power spectral feature extraction that finds the time domain’s periodicity. The proposed methodology solves overfitting for the training datasets because we obtain the data dimension by applying autocorrelation function-based power spectral feature extraction and then split the long-resampled wave signal to increase the number of input data samples. The proposed model provides accurate respiratory rate estimates and offers a solution for reliably managing the estimation uncertainty. In addition, the proposed method presents a more precise estimate than conventional respiratory rate measurement techniques. |
| Author | Lee, Soojeong Son, Chang-Hwan Moon, Hyeonjoon Lee, Gangseong |
| Author_xml | – sequence: 1 givenname: Soojeong surname: Lee fullname: Lee, Soojeong – sequence: 2 givenname: Hyeonjoon orcidid: 0000-0001-7668-3838 surname: Moon fullname: Moon, Hyeonjoon – sequence: 3 givenname: Chang-Hwan surname: Son fullname: Son, Chang-Hwan – sequence: 4 givenname: Gangseong orcidid: 0000-0001-7991-3862 surname: Lee fullname: Lee, Gangseong |
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| Cites_doi | 10.1109/TSP.2017.2664048 10.1023/A:1010933404324 10.1007/s10877-014-9621-3 10.1016/j.neucom.2006.03.016 10.1162/089976600300015015 10.1109/JBHI.2022.3144990 10.12968/bjon.2013.22.10.570 10.3390/s20072108 10.1007/BF02600071 10.1109/RBME.2017.2763681 10.1007/s10916-020-01551-4 10.3390/e18080285 10.1109/TBME.2013.2246160 10.1161/CIRCULATIONAHA.114.009024 10.1049/htl.2014.0077 10.1109/TBME.2016.2613124 10.1161/01.CIR.91.10.2504 10.1016/j.annemergmed.2004.06.016 10.1007/s10877-006-9059-3 10.1109/TBME.2019.2923448 10.1109/JSEN.2018.2828599 10.1007/BF02348427 10.1016/j.compbiomed.2022.105338 10.1016/j.enbuild.2017.11.039 10.1017/CBO9780511611483 10.1007/s10877-011-9332-y 10.1161/01.CIR.101.23.e215 10.1007/3-540-44668-0_93 10.1109/BIBM47256.2019.8983313 10.1109/ACCESS.2020.3007524 10.1007/BF00116037 10.1088/0967-3334/28/3/R01 10.1016/j.csda.2013.09.006 10.1007/BF00058655 10.1088/0967-3334/37/4/610 10.1214/aos/1013203451 |
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| SubjectTerms | Algorithms autocorrelation function-based power spectral feature extraction Datasets Electrocardiography ensemble learning gradient boosting technique Heart failure Intensive care Machine learning Methods Patients photolethysmogram Pneumonia Pulse oximetry respiration rate prediction Sensors Wavelet transforms |
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| Title | Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm |
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