Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals Article Swipe
Xuhao Dong
,
Ziyi Wang
,
Liangli Cao
,
Zhencheng Chen
,
Yongbo Liang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics13050913
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics13050913
Due to the simplicity and convenience of PPG signal acquisition, the detection of the respiration rate based on the PPG signal is more suitable for dynamic monitoring than the impedance spirometry method, but it is challenging to achieve accurate predictions from low-signal-quality PPG signals, especially in intensive-care patients with weak PPG signals. The goal of this study was to construct a simple model for respiration rate estimation based on PPG signals using a machine-learning approach fusing signal quality metrics to improve the accuracy of estimation despite the low-signal-quality PPG signals. In this study, we propose a method based on the whale optimization algorithm (WOA) with a hybrid relation vector machine (HRVM) to construct a highly robust model considering signal quality factors to estimate RR from PPG signals in real time. To detect the performance of the proposed model, we simultaneously recorded PPG signals and impedance respiratory rates obtained from the BIDMC dataset. The results of the respiration rate prediction model proposed in this study showed that the MAE and RMSE were 0.71 and 0.99 breaths/min, respectively, in the training set, and 1.24 and 1.79 breaths/min, respectively, in the test set. Compared without taking signal quality factors into account, MAE and RMSE are reduced by 1.28 and 1.67 breaths/min, respectively, in the training set, and reduced by 0.62 and 0.65 breaths/min in the test set. Even in the nonnormal breathing range below 12 bpm and above 24 bpm, the MAE reached 2.68 and 4.28 breaths/min, respectively, and the RMSE reached 3.52 and 5.01 breaths/min, respectively. The results show that the model that considers the PPG signal quality and respiratory quality proposed in this study has obvious advantages and application potential in predicting the respiration rate to cope with the problem of low signal quality.
Related Topics
Concepts
Photoplethysmogram
Respiratory rate
Computer science
SIGNAL (programming language)
Test set
Pattern recognition (psychology)
Artificial intelligence
Respiration rate
Mean squared error
Mathematics
Respiration
Heart rate
Statistics
Medicine
Computer vision
Filter (signal processing)
Programming language
Anatomy
Blood pressure
Radiology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics13050913
- https://www.mdpi.com/2075-4418/13/5/913/pdf?version=1677585304
- OA Status
- gold
- Cited By
- 6
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4322741392
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4322741392Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics13050913Digital Object Identifier
- Title
-
Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography SignalsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-02-28Full publication date if available
- Authors
-
Xuhao Dong, Ziyi Wang, Liangli Cao, Zhencheng Chen, Yongbo LiangList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics13050913Publisher landing page
- PDF URL
-
https://www.mdpi.com/2075-4418/13/5/913/pdf?version=1677585304Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2075-4418/13/5/913/pdf?version=1677585304Direct OA link when available
- Concepts
-
Photoplethysmogram, Respiratory rate, Computer science, SIGNAL (programming language), Test set, Pattern recognition (psychology), Artificial intelligence, Respiration rate, Mean squared error, Mathematics, Respiration, Heart rate, Statistics, Medicine, Computer vision, Filter (signal processing), Programming language, Anatomy, Blood pressure, RadiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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