Electrocardiographic signals (ECG) are used in many healthcare applications, including at-home monitoring of vital signs. These applications often rely on wearable technology and provide low quality ECG signals. Although many methods have been proposed for denoising the ECG to boost its quality and enable clinical interpretation, these methods typically fall short for ECG data obtained with wearable technology, because of either their limited tolerance to noise or their limited flexibility to capture ECG dynamics. This paper presents HKF, a hierarchical Kalman filtering method, that leverages a patient-specific learned structured prior of the ECG signal, and integrates it into a state space model to yield filters that capture both intra- and inter-heartbeat dynamics. HKF is demonstrated to outperform previously proposed methods such as the model-based Kalman filter and data-driven autoencoders, in ECG denoising task in terms of mean-squared error, making it a suitable candidate for application in extramural healthcare settings.

pdf/hierarchical-filtering-with-online-learned-priors-for-ecg-2tb77ir3.pdf

Hierarchical Filtering With Online Learned Priors for ECG Denoising

Reliable real-time estimation of the interaural time delay of a sound source is difficult in the presence of noise and reverberation. However, the psychoacoustical precedence effect suggests that accurate estimation is possible by concentrating on the first-arriving sound. This paper introduces a novel real-time estimation method inspired by the precedence effect. First, the arrival of first-arriving sound is detected by performing a hypothesis test based on local approximation of the binaural signal with exponentially decaying sinusoids, which effectively model the shape of a sound onset. After detection, the interaural time delay is directly retrieved from the phase shift of the approximating sinusoids. The local model approximation is done with efficient recursions by parameterization of the model with autonomous linear state-space models, making the algorithm implementable in real-time.

Real-Time Interaural Time Delay Estimation via Onset Detection

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

Signal-to-peak-interference ratio (SPIR) optimal fil-ters are template matching filters with peak interference suppression properties. Such max-SPIR filters are used in multi-pattern recognition problems, such as neural spike sorting in micro-electrode array probes, where cellular action potentials need to be detected and clustered according to their firing neuron cells. In high-density probes with hundreds of channels, such max-SPIR filter banks can require unacceptable high computational resources, in particular for applications with real-time demands and/or on-probe spike sorting. In this paper, we present a computationally attractive substitute for max-SPIR filters by recursively computed Autonomous Linear State Space Model (ALSSM) filters. In our approach, we approximate the impulse response of max-SPIR filters by low order ALSSMs and perform the signal convolution in the new, low-dimensional ALSSM vector space. We demonstrate our method on real neural recordings from high-density probes and show only minimal loss in detection quality while the computational complexity drops by up to a factor 10.

Windowed State Space Filters for Peak Interference Suppression in Neural Spike Sorting

Local polynomial approximations represent a versatile feature space for time-domain signal analysis. The parameters of such polynomial approximations can be computed by efficient recursions using autonomous linear state space models and often allow analytical solutions for quantities of interest. The approach is illustrated by practical examples including the estimation of the delay difference between two acoustic signals and template matching in electrocardiogram signals with local variations in amplitude and time scale.

Signal Analysis Using Local Polynomial Approximations

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Signal Analysis Using Local Polynomial Approximations