# Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithm

arXiv: https://arxiv.org/abs/2512.24253 · HTML: https://mlsanity.com/paper/2512.24253/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 79 references that are in the map.
- First posted on arXiv 30 Dec 2025.

## Builds on (most cited first, 4 of 4)

- [Time Series Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2002.12478/) (2020)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [Early detection of sepsis utilizing deep learning on electronic health record event sequences](https://mlsanity.com/paper/1906.02956/) (2019)
- [Meta-learning in healthcare: A survey](https://mlsanity.com/paper/2308.02877/) (2023)

## Abstract (arXiv)

Sepsis, characterized by a dysregulated immune response to infection, results in significant mortality, morbidity, and healthcare costs. The timely prediction of sepsis progression is crucial for reducing adverse outcomes through early intervention. Despite the development of numerous models for Intensive Care Unit (ICU) patients, there remains a notable gap in approaches for the early detection of sepsis in non-ward settings. This research introduces and evaluates four novel machine learning algorithms designed for predicting the onset of sepsis on wearable devices by analyzing heart rate data. The architecture of these models was refined through a genetic algorithm, optimizing for performance, computational complexity, and memory requirements. Performance metrics were subsequently extracted for each model to evaluate their feasibility for implementation on wearable devices capable of accurate heart rate monitoring. The models were initially tailored for a prediction window of one hour, later extended to four hours through transfer learning. The encouraging outcomes of this study suggest the potential for wearable technology to facilitate early sepsis detection outside ICU and ward environments.