Summary of Localising the Seizure Onset Zone From Single-pulse Electrical Stimulation Responses with a Cnn Transformer, by Jamie Norris et al.
Localising the Seizure Onset Zone from Single-Pulse Electrical Stimulation Responses with a CNN Transformer
by Jamie Norris, Aswin Chari, Dorien van Blooijs, Gerald Cooray, Karl Friston, Martin Tisdall, Richard Rosch
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new study combines deep learning techniques with electrical stimulation methods to improve the localization of the epileptogenic focus, a crucial step in surgical treatment for epilepsy. The researchers compared two analysis paradigms: divergent and convergent, which evaluate outward and inward effective connections respectively. They found that the convergent approach outperformed the divergent one, achieving an AUROC of 0.666. Additionally, they demonstrated the effectiveness of CNN Transformers with cross-channel attention in handling heterogeneous electrode placements, resulting in an AUROC of 0.730. These findings have significant implications for modeling patient-specific intracranial EEG electrode placements and bridging the gap between deep learning research and practical healthcare applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Epilepsy is a common neurological disorder that can be challenging to treat. To help surgeons make better decisions, researchers are working on new ways to find the part of the brain causing seizures. They’re using computers to analyze special signals called electrical stimulation responses. The study used two different methods to look at these signals and found that one method was better than the other. It also showed that a special type of computer model can handle different types of electrodes, which is important for making accurate predictions. |
Keywords
» Artificial intelligence » Attention » Cnn » Deep learning
