Evaluating Imputation Techniques for Short-Term Gaps in Heart Rate Data

TL;DR

This paper evaluates four statistical methods for filling short-term gaps in heart rate data from wearables, using both traditional accuracy metrics and statistical distance measures, and proposes a new framework for assessing imputation quality.

Contribution

It introduces a comprehensive evaluation combining accuracy and statistical distance metrics for imputation methods and proposes a new framework for assessing imputation quality in physiological signals.

Findings

Linear interpolation performs well in short-term HR gap filling.

Existing methods have limitations in capturing complex signal structures.

Proposed framework offers a more robust evaluation of imputation quality.

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 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 Distance Test (CDT) and Jensen Shannon Distance (JS Distance), 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.