# Topological Data Analysis for Arrhythmia Detection through Modular Neural Networks

arXiv: https://arxiv.org/abs/1906.05795 · HTML: https://mlsanity.com/paper/1906.05795/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [Activation Landscapes as a Topological Summary of Neural Network Performance](https://mlsanity.com/paper/2110.10136/) (2021)
- [PHG-Net: Persistent Homology Guided Medical Image Classification](https://mlsanity.com/paper/2311.17243/) (2023)
- [Diffeomorphic interpolation for efficient persistence-based topological optimization](https://mlsanity.com/paper/2405.18820/) (2024)
- [Geometric feature performance under downsampling for EEG classification tasks](https://mlsanity.com/paper/2102.07669/) (2021)
- [Importance attribution in neural networks by means of persistence landscapes of time series](https://mlsanity.com/paper/2302.03132/) (2023)
- [Differentially Private Topological Data Analysis](https://mlsanity.com/paper/2305.03609/) (2023)
- [Topological Analysis for Detecting Anomalies (TADA) in Time Series](https://mlsanity.com/paper/2406.06168/) (2024)
- [Topological data analysis using persistent discrete homology](https://mlsanity.com/paper/2506.15020/) (2025)
- [Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques](https://mlsanity.com/paper/2605.02947/) (2026)

## Abstract (arXiv)

This paper presents an innovative and generic deep learning approach to monitor heart conditions from ECG signals.We focus our attention on both the detection and classification of abnormal heartbeats, known as arrhythmia. We strongly insist on generalization throughout the construction of a deep-learning model that turns out to be effective for new unseen patient. The novelty of our approach relies on the use of topological data analysis as basis of our multichannel architecture, to diminish the bias due to individual differences. We show that our structure reaches the performances of the state-of-the-art methods regarding arrhythmia detection and classification.