Beyond Accuracy: Assessment of Statistical Imputation Techniques for Heart Rate Data

Open Access

Issue		BIO Web Conf. Volume 195, 2025 2025 9^th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2025)


Article Number		03002
Number of page(s)		10
Section		Biomedical Data Analysis and Epidemiological Studies
DOI		https://doi.org/10.1051/bioconf/202519503002
Published online		14 November 2025

American Diabetes Association Professional Practice Committee, 2. Diagnosis and Clas-sification of Diabetes: Standards of Care in Diabetes—2024. Diabetes Care 47(Suppl 1), S20–S42 (2024). https://doi.org/10.2337/dc24-S002 [Google Scholar]
L. Mochurad and Y. Mochurad, Parallel algorithms for interpolation with Bezier curves and B-splines for medical data recovery. In: International Workshop on Informatics and Data-Driven Medicine (2023). https://api.semanticscholar.org/CorpusID:266874158 [Google Scholar]
S. Lin, X. Wu, G. Martinez, and N. Chawla, Filling Missing Values on Wearable-Sensory Time Series Data. In: Proceedings, pp. 46–54 (2020). https://doi.org/10.1137/1.9781611976236.6 [Google Scholar]
O. Boursalie, R. Samavi, and T. Doyle, Evaluation Metrics for Deep Learning Imputation Models. In: Proceedings, pp. 309–322 (2022). https://doi.org/10.1007/978-3-030-93080-6_22 [Google Scholar]
F. Dubosson, J.-E. Ranvier, S. Bromuri, J.-P. Calbimonte, J. Ruiz, and M. Schumacher, The open d1namo dataset: A multi-modal dataset for research on non-invasive type 1 diabetes management. Informatics in Medicine Unlocked 13, 92–100 (2018). https://doi.org/10.1016/j.imu.2018.09.003 [Google Scholar]
J. Ahlert, T. Klein, F. Wichmann, and R. Geirhos, How Aligned are Different Alignment Metrics? arXiv preprint arXiv:2407.07530 (2024). https://doi.org/10.48550/arXiv.2407.07530 [Google Scholar]
H. Leutheuser, M. Bartholet, A. Marx, M. Pfister, M.-A. Burckhardt, S. Bachmann, and J. E. Vogt, Predicting risk for nocturnal hypoglycemia after physical activity in children with type 1 diabetes. Frontiers in Medicine 11, (Oct. 2024). https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2024.1439218 [Google Scholar]
M. Benchekroun, B. Chevallier, V. Zalc, D. Istrate, D. Lenne, and N. Vera, The impact of missing data on heart rate variability features: A comparative study of interpolation methods for ambulatory health monitoring. IRBM 44, 100776 (Aug. 2023). https://doi.org/10.1016/j.irbm.2023.100776 [Google Scholar]
S. Mantena, A. Arévalo, J. Maley, S. da Silva Vieira, R. Mateo-Collado, J. da Costa Sousa, and L. Celi, Predicting hypoglycemia in critically ill patients using machine learn-ing and electronic health records. Journal of Clinical Monitoring and Computing 36, 1297–1303 (2022). https://doi.org/10.1007/s10877-021-00760-7 [Google Scholar]
M.R. Vahedi, K.B. MacBride, W. Wunsik, Y. Kim, C. Fong, A.J. Padilla, M. Pourhomayoun, A. Zhong, S. Kulkarni, S. Arunachalam, and B. Jiang, Predicting Glu-cose Levels in Patients with Type1 Diabetes Based on Physiological and Activity Data. In: Proceedings of the 8th ACM MobiHoc 2018 Workshop on Pervasive Wireless Healthcare Workshop, pp. 4–9. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3220127.3220133 [Google Scholar]
A. Bertachi, C. Viñals, L. Biagi, I. Contreras, J. Vehí, I. Conget, and M. Giménez, Prediction of nocturnal hypoglycemia in adults with type 1 diabetes under multiple daily injections using continuous glucose monitoring and physical activity monitor. Sensors 20, 1705 (2020). https://doi.org/10.3390/s20061705 [Google Scholar]
N. U. Rehman, I. Contreras, A. Beneyto, and J. Vehi, The impact of missing con-tinuous blood glucose samples on machine learning models for predicting postprandial hypoglycemia: An experimental analysis. Journal of Clinical Medicine 9(11), (2020). https://doi.org/10.3390/math12101567 [Google Scholar]
D. Dave, K. Vyas, K. Branan, S. McKay, D. J. DeSalvo, R. Gutierrez-Osuna, G. L. Cote, and M. Erraguntla, Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry. Journal of Diabetes Science and Technology 18(2), 351–362 (2024). https://doi.org/10.1177/19322968221116393 [Google Scholar]
B. Cinar, Diadata: Daily life data of type-1 diabetes patients. Kaggle Dataset (2022). Accessed: 2025-05-09. https://www.kaggle.com/datasets/beyzacinar22/diadata [Google Scholar]
P. Cho, J. Kim, B. Bent, and J. Dunn, BIG IDEAs Lab Glycemic Variability and Wear-able Device Data (version 1.1.1). PhysioNet (2023). https://doi.org/10.13026/73s9-cw03 [Google Scholar]
P. Schreiber, B. Cinar, L. Mackert, and M. Maleshkova, VitaStress – Multimodal Vital Signs for Stress Detection. In: C. Stephanidis, M. Antona, S. Ntoa, and G. Salvendy (eds), HCI International 2025 Posters, pp. 205–215. Springer Nature Switzerland, Cham (2025). https://doi.org/10.1007/978-3-031-94162-7_20 [Google Scholar]
B. Cinar and M. Maleshkova, “Benchmarking Hypoglycemia Classification Using Quality-Enhanced DiaData”, Journal of Biomedical and Health Informatics, under review. [Google Scholar]
S. Chakrabarti, N. Biswas, K. Karnani, V. Padul, L. D. Jones, S. Kesari, and S. Ashili, “Binned Data Provide Better Imputation of Missing Time Series Data from Wearables,” Sensors, vol. 23, no. 3, p. 1454, 2023. https://doi.org/10.3390/s23031454 [Google Scholar]
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587 [Google Scholar]
Briët, J., Harremoës, P. (2009). Properties of classical and quantum Jensen–Shannon divergence. Physical Review A, 79(5), 052311. https://doi.org/10.1103/PhysRevA.79.052311 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.