HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising

TL;DR

HKF is a hierarchical, adaptive Kalman filter that learns patient-specific ECG dynamics online, significantly improving denoising performance over existing methods for wearable ECG signals.

Contribution

Introduces HKF, a novel hierarchical Kalman filtering approach with online learned priors for adaptive ECG denoising tailored to individual patients.

Findings

HKF reduces mean-squared error in ECG denoising.

HKF outperforms traditional Kalman filters and autoencoders.

HKF effectively preserves ECG waveform properties.

Abstract

Electrocardiography (ECG) signals play a pivotal role in many healthcare applications, especially in at-home monitoring of vital signs. Wearable technologies, which these applications often depend upon, frequently produce low-quality ECG signals. While several methods exist for ECG denoising to enhance signal quality and aid clinical interpretation, they often underperform with ECG data from wearable technology due to limited noise tolerance or inadequate flexibility in capturing ECG dynamics. This paper introduces HKF, a hierarchical and adaptive Kalman filter, which uses a proprietary state space model to effectively capture both intra- and inter-heartbeat dynamics for ECG signal denoising. HKF learns a patient-specific structured prior for the ECG signal's intra-heartbeat dynamics in an online manner, resulting in a filter that adapts to the specific ECG signal characteristics of each patient. In an empirical study, HKF demonstrated superior denoising performance (reduced mean-squared error) while preserving the unique properties of the waveform. In a comparative analysis, HKF outperformed previously proposed methods for ECG denoising, such as the model-based Kalman filter and data-driven autoencoders. This makes it a suitable candidate for applications in extramural healthcare settings.