# Fully-Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection

## TL;DR

JULES is a new model-free segmentation method that detects flickering ion channel events at small scales, providing precise dwell times and amplitudes, and can enhance hidden Markov analysis.

## Contribution

The paper introduces JULES, a novel multiresolution and deconvolution-based segmentation method for ion channel recordings, capable of detecting small-scale flickering events.

## Key findings

- JULES accurately detects flickering events below filter frequency.
- It provides precise dwell times and amplitude levels.
- It enhances hidden Markov analysis for ion channel data.

## Abstract

We propose a new model-free segmentation method, JULES, which combines recent statistical multiresolution techniques with local deconvolution for idealization of ion channel recordings. The multiresolution criterion takes into account scales down to the sampling rate enabling the detection of flickering events, i.e., events on small temporal scales, even below the filter frequency. For such small scales the deconvolution step allows for a precise determination of dwell times and, in particular, of amplitude levels, a task which is not possible with common thresholding methods. This is confirmed theoretically and in a comprehensive simulation study. In addition, JULES can be applied as a preprocessing method for a refined hidden Markov analysis. Our new methodolodgy allows us to show that gramicidin A flickering events have the same amplitude as the slow gating events. JULES is available as an R function jules in the package clampSeg.