Beyond accuracy
Assessment of statistical imputation techniques for heart rate data
Publication date
2025-11-14
Document type
Konferenzbeitrag
Author
Organisational unit
Conference
9th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2025) ; Prague, Czech Republic ; September 19-21, 2025
Publisher
EDP Sciences
Series or journal
BIO Web of Conferences
ISSN
Periodical volume
195
Article ID
03002
Part of the university bibliography
✅
Language
English
Abstract
Recent advances in wearable technology have enabled the continuous monitoring of vital physiological signals, essential for predictive modeling and early detection of extreme physiological events. Among these physiological signals, heart rate (HR) plays a central role, as it is widely used in monitoring and managing cardiovascular conditions and detecting extreme physiological events such as hypoglycemia. However, data from wearable devices often suffer from missing values. To address this issue, recent studies have employed various imputation techniques. Traditionally, the effectiveness of these methods has been evaluated using predictive accuracy metrics such as RMSE, MAPE, and MAE, which assess numerical proximity to the original data. While informative, these metrics might fail to capture the complex statistical structure inherent in physiological signals. This study bridges this gap by presenting a comprehensive evaluation of four statistical imputation methods, Linear Interpolation, K-Nearest Neighbors (KNN), Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and B-splines, for short-term HR data gaps. We assess their performance using both predictive accuracy metrics and statistical distance measures, including the Cohen’s Distance Test (CDT) and Jensen-Shannon Distance (JSD), applied to HR data from the D1NAMO dataset and the BIG IDEAs Lab Glycemic Variability and Wearable Device dataset. The analysis reveals limitations in existing imputation approaches and the absence of a robust framework for evaluating imputation quality in physiological signals. Finally, this study proposes a foundational framework to develop a composite evaluation metric to assess imputation performance.
Description
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
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Published version
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