Online Imputation Analysis for Anomaly Heart Rate Detection in Wearable Data Streams
Year: 2026
Type: conference-paper
Abstract: Wearable data streams are prone to missing values, which may lead to biased findings and inappropriate treatment decisions in remote health monitoring applications. This challenge underscores the importance of real-time imputation for streaming data. Our study evaluates the effectiveness of online imputation methods for enhancing anomaly detection in patients' wearable data streams. Experiments were conducted under various missing mechanisms and rates, utilizing four online imputation methods: Linear Regression, Hoeffding Tree regressor, Hoeffding Adaptive Tree regressor, and Multi-layer Perceptron for regression, in addition to a baseline mean imputation. This poster results demonstrates that online imputation methods, particularly the Hoeffding Adaptive Tree, achieved superior imputation performance and improved anomaly detection in most scenarios. Nevertheless, each missing mechanism produced distinct patterns of results that varied in both performance and stability, highlighting the need for further research to advance online imputation approaches for critical healthcare scenarios.
Institution: Universidade de São Paulo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7221128752