# Fixation Sequences as Time Series: A Topological Approach to Dyslexia Detection

arXiv: https://arxiv.org/abs/2604.21698 · HTML: https://mlsanity.com/paper/2604.21698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 23 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [Persistence Images: A Stable Vector Representation of Persistent Homology](https://mlsanity.com/paper/1507.06217/) (2015)
- [An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists](https://mlsanity.com/paper/1710.04019/) (2017)
- [Stochastic Convergence of Persistence Landscapes and Silhouettes](https://mlsanity.com/paper/1312.0308/) (2013)
- [Assessing Language Proficiency from Eye Movements in Reading](https://mlsanity.com/paper/1804.07329/) (2018)
- [Eye-tracking based classification of Mandarin Chinese readers with and without dyslexia using neural sequence models](https://mlsanity.com/paper/2210.09819/) (2022)
- [Comparing persistence diagrams through complex vectors](https://mlsanity.com/paper/1505.01335/) (2015)
- [The Copenhagen Corpus of Eye Tracking Recordings from Natural Reading of Danish Texts](https://mlsanity.com/paper/2204.13311/) (2022)
- [DysLexML: Screening Tool for Dyslexia Using Machine Learning](https://mlsanity.com/paper/1903.06274/) (2019)
- [Banana Trees for the Persistence in Time Series Experimentally](https://mlsanity.com/paper/2405.17920/) (2024)

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

- [Exploring Dowker Homology for Sentence Similarity](https://mlsanity.com/paper/2608.22909/) (2026)

## Abstract (arXiv)

Persistent homology, a method from topological data analysis, extracts robust, multi-scale features from data. It produces stable representations of time series by applying varying thresholds to their values (a process known as a textitfiltration). We develop novel filtrations for time series and introduce topological methods for the analysis of eye-tracking data, by interpreting fixation sequences as time series, and constructing ``hybrid models'' that combine topological features with traditional statistical features. We empirically evaluate our method by applying it to the task of dyslexia detection from eye-tracking-while-reading data using the Copenhagen Corpus, which contains scanpaths from dyslexic and non-dyslexic L1 and L2 readers. Our hybrid models outperform existing approaches that rely solely on traditional features, showing that persistent homology captures complementary information encoded in fixation sequences. The strength of these topological features is further underscored by their achieving performance comparable to established baseline methods. Importantly, our proposed filtrations outperform existing ones.