# Heterogeneous Idealization of Ion Channel Recordings -- Open Channel Noise

arXiv: https://arxiv.org/abs/2008.02658 · HTML: https://mlsanity.com/paper/2008.02658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models](https://mlsanity.com/paper/2305.18578/) (2023)

## Abstract (arXiv)

We propose a new model-free segmentation method for idealizing ion channel recordings. This method is designed to deal with heterogeneity of measurement errors. This in particular applies to open channel noise which, in general, is particularly difficult to cope with for model-free approaches. Our methodology is able to deal with lowpass filtered data which provides a further computational challenge. To this end we propose a multiresolution testing approach, combined with local deconvolution to resolve the lowpass filter. Simulations and statistical theory confirm that the proposed idealization recovers the underlying signal very accurately at presence of heterogeneous noise, even when events are shorter than the filter length. The method is compared to existing approaches in computer experiments and on real data. We find that it is the only one which allows to identify openings of the PorB porine at two different temporal scales. An implementation is available as an R package.