Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning

dc.contributor.authorZhang, Yuntong
dc.contributor.authorXu, Jingye
dc.contributor.authorXie, Mimi
dc.contributor.authorZhu, Dakai
dc.contributor.authorSong, Houbing
dc.contributor.authorWang, Wei
dc.date.accessioned2024-05-09T15:24:09Z
dc.date.available2024-05-09T15:24:09Z
dc.date.issued2024-01-22
dc.description.abstractHeart Rate Variability (HRV) measures the variation of the time between consecutive heartbeats and is a major indicator of physical and mental health. Recent research has demonstrated that photoplethysmography (PPG) sensors can be used to infer HRV. However, many prior studies had high errors because they only employed signal processing or machine learning (ML), or because they indirectly inferred HRV, or because there lacks large training datasets. Many prior studies may also require large ML models. The low accuracy and large model sizes limit their applications to small embedded devices and potential future use in healthcare. To address the above issues, we first collected a large dataset of PPG signals and HRV ground truth. With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV. Evaluation results show that our method had errors between 3.5% to 25.7% and outperformed signal-processing-only and ML-only methods. We also explored different ML models, which showed that Decision Trees and Multi-level Perceptrons have 13.0% and 9.1% errors on average with models at most hundreds of KB and inference time less than 1ms. Hence, they are more suitable for small embedded devices and potentially enable the future use of PPG-based HRV monitoring in healthcare.
dc.description.departmentComputer Science
dc.description.sponsorshipThis research was in part supported by the National Science Foundation (NSF), under grants, 2155096, 2221843, 2215359, 2309760, and 2317117.
dc.identifier.citationZhang, Y., Xu, J., Xie, M., Zhu, D., Song, H., & Wang, W. (2024). Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning. Paper presented at the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, Orlando. https://doi.org/10.1145/3580252.3586971
dc.identifier.otherhttps://doi.org/10.1145/3580252.3586971
dc.identifier.urihttps://hdl.handle.net/20.500.12588/6419
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.rightsAttribution 3.0 United Statesen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/
dc.subjectHeart Rate Variability
dc.subjectmachine learning
dc.subjectphotoplethysmography
dc.subjectsignal processing
dc.titleEfficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning
dc.typeArticle

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