# The Temple University Hospital Seizure Detection Corpus

arXiv: https://arxiv.org/abs/1801.08085 · HTML: https://mlsanity.com/paper/1801.08085/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2018.

## Built on by (most cited first, 30 of 83)

- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [Hungry Hungry Hippos: Towards Language Modeling with State Space Models](https://mlsanity.com/paper/2212.14052/) (2022)
- [Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI](https://mlsanity.com/paper/2405.18765/) (2024)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models](https://mlsanity.com/paper/2211.11176/) (2022)
- [Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis](https://mlsanity.com/paper/2208.07448/) (2022)
- [MBrain: A Multi-channel Self-Supervised Learning Framework for Brain Signals](https://mlsanity.com/paper/2306.13102/) (2023)
- [Self-supervised Learning for Electroencephalogram: A Systematic Survey](https://mlsanity.com/paper/2401.05446/) (2024)
- [Graph Anomaly Detection in Time Series: A Survey](https://mlsanity.com/paper/2302.00058/) (2023)
- [SzCORE: A Seizure Community Open-source Research Evaluation framework for the validation of EEG-based automated seizure detection algorithms](https://mlsanity.com/paper/2402.13005/) (2024)
- [REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates](https://mlsanity.com/paper/2406.16906/) (2024)
- [MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning](https://mlsanity.com/paper/2406.06620/) (2024)
- [NeurIPT: Foundation Model for Neural Interfaces](https://mlsanity.com/paper/2510.16548/) (2025)
- [Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting](https://mlsanity.com/paper/2201.08780/) (2022)
- [Concept-based explainability for an EEG transformer model](https://mlsanity.com/paper/2307.12745/) (2023)
- [EvoBrain: Dynamic Multi-Channel EEG Graph Modeling for Time-Evolving Brain Networks](https://mlsanity.com/paper/2509.15857/) (2025)
- [THD-BAR: Topology Hierarchical Derived Brain Autoregressive Modeling for EEG Generic Representations](https://mlsanity.com/paper/2511.13733/) (2025)
- [LLM as Clinical Graph Structure Refiner: Enhancing Representation Learning in EEG Seizure Diagnosis](https://mlsanity.com/paper/2604.28178/) (2026)
- [NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces](https://mlsanity.com/paper/2605.14698/) (2026)
- [Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data](https://mlsanity.com/paper/2405.09568/) (2024)
- [EEG-Language Pretraining for Highly Label-Efficient Clinical Phenotyping](https://mlsanity.com/paper/2409.07480/) (2024)
- [NeuroRVQ: Multi-Scale Biosignal Tokenization for Generative Foundation Models](https://mlsanity.com/paper/2510.13068/) (2025)
- [Avoiding Post-Processing with Event-Based Detection in Biomedical Signals](https://mlsanity.com/paper/2209.11007/) (2022)
- [A Meta-GNN approach to personalized seizure detection and classification](https://mlsanity.com/paper/2211.02642/) (2022)
- [Large Language Models for EEG: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2506.06353/) (2025)
- [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://mlsanity.com/paper/2212.03176/) (2022)
- [Towards trustworthy seizure onset detection using workflow notes](https://mlsanity.com/paper/2306.08728/) (2023)
- [Open-Set Multivariate Time-Series Anomaly Detection](https://mlsanity.com/paper/2310.12294/) (2023)
- [BioSerenity-E1: a self-supervised EEG model for medical applications](https://mlsanity.com/paper/2503.10362/) (2025)
- [Low Latency Real-Time Seizure Detection Using Transfer Deep Learning](https://mlsanity.com/paper/2202.07796/) (2022)

## Abstract (arXiv)

We introduce the TUH EEG Seizure Corpus (TUSZ), which is the largest open source corpus of its type, and represents an accurate characterization of clinical conditions. In this paper, we describe the techniques used to develop TUSZ, evaluate their effectiveness, and present some descriptive statistics on the resulting corpus.