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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/16/8355 |
| _version_ | 1797411302094667776 |
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| author | Soojeong Lee Hyeonjoon Moon Chang-Hwan Son Gangseong Lee |
| author_facet | Soojeong Lee Hyeonjoon Moon Chang-Hwan Son Gangseong Lee |
| author_sort | Soojeong Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:43:06Z |
| format | Article |
| id | doaj.art-885c2e7448ae4fb1879eb1859dbd24a5 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:43:06Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-885c2e7448ae4fb1879eb1859dbd24a52023-12-03T13:18:43ZengMDPI AGApplied Sciences2076-34172022-08-011216835510.3390/app12168355Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting AlgorithmSoojeong Lee0Hyeonjoon Moon1Chang-Hwan Son2Gangseong Lee3Department of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, KoreaDepartment of Computer Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, KoreaDepartment of Software Science & Engineering, Kunsan National University, 558 Daehak-ro, Gunsan-si 54150, KoreaIngenium College, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, KoreaVarious 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.https://www.mdpi.com/2076-3417/12/16/8355ensemble learningphotolethysmogramrespiration rate predictiongradient boosting techniqueautocorrelation function-based power spectral feature extraction |
| spellingShingle | Soojeong Lee Hyeonjoon Moon Chang-Hwan Son Gangseong Lee Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm Applied Sciences ensemble learning photolethysmogram respiration rate prediction gradient boosting technique autocorrelation function-based power spectral feature extraction |
| title | Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm |
| title_full | Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm |
| title_fullStr | Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm |
| title_full_unstemmed | Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm |
| title_short | Respiratory Rate Estimation Combining Autocorrelation Function-Based Power Spectral Feature Extraction with Gradient Boosting Algorithm |
| title_sort | respiratory rate estimation combining autocorrelation function based power spectral feature extraction with gradient boosting algorithm |
| topic | ensemble learning photolethysmogram respiration rate prediction gradient boosting technique autocorrelation function-based power spectral feature extraction |
| url | https://www.mdpi.com/2076-3417/12/16/8355 |
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