Unmixing Noise from Hawkes Process to Model Learned Physiological Events

TL;DR

This paper introduces UNHaP, a novel method that uses marked Hawkes processes to jointly learn physiological event structures and effectively remove spurious detections, improving event analysis accuracy.

Contribution

UNHaP is the first approach to unmix structured physiological events from noise using Hawkes processes, addressing limitations of previous methods.

Findings

Improved detection accuracy with reduced false positives.

Effective separation of true events from noise in physiological signals.

Enhanced modeling of event temporal dynamics.

Abstract

Physiological signal analysis often involves identifying events crucial to understanding biological dynamics. Traditional methods rely on handcrafted procedures or supervised learning, presenting challenges such as expert dependence, lack of robustness, and the need for extensive labeled data. Data-driven methods like Convolutional Dictionary Learning (CDL) offer an alternative but tend to produce spurious detections. This work introduces UNHaP (Unmix Noise from Hawkes Processes), a novel approach addressing the joint learning of temporal structures in events and the removal of spurious detections. Leveraging marked Hawkes processes, UNHaP distinguishes between events of interest and spurious ones. By treating the event detection output as a mixture of structured and unstructured events, UNHaP efficiently unmixes these processes and estimates their parameters. This approach significantly enhances the understanding of event distributions while minimizing false detection rates.