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

1 code implementation • 23 Oct 2022 • Guy Revach, Timur Locher, Nir Shlezinger, Ruud J. G. van Sloun, Rik Vullings

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.

Denoising ECG Denoising

Unsupervised Learned Kalman Filtering

1 code implementation • 18 Oct 2021 • Guy Revach, Nir Shlezinger, Timur Locher, Xiaoyong Ni, Ruud J. G. van Sloun, Yonina C. Eldar

In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF), to learn its mapping in an unsupervised manner, i. e., without requiring ground-truth states.