Unmixing Noise from Hawkes Process to Model Learned Physiological Events
This work improves physiological signal analysis for researchers by reducing false detections, though it appears incremental as it builds on existing Hawkes process and dictionary learning methods.
The paper tackled the problem of identifying physiological events in signals by addressing spurious detections from data-driven methods, resulting in enhanced event distribution understanding and minimized false detection rates.
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.