NCBI PubMed Central
open access
Deep learning architectures for EEG-based classification of Dravet syndrome: A comparative study of pre-trained and non-pretrained hybrid CNN-LSTM models.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Deep learning architectures for EEG-based classification of Dravet syndrome: A comparative study of pre-trained and non-pretrained hybrid CNN-LSTM models#418990
- Ameliorating Seizures in Dravet Syndrome: A Review of Newly Approved and Investigational Drugs, RNA and Gene-Based Therapies#185381
- Identifying interactions across brain areas while accounting for individual-neuron dynamics with a Transformer-based variational autoencoder.#923439
- Beyond the reducing valve: towards a computational neurophenomenology of altered states via deep neural networks.#258341
- Molecular alterations prediction in gliomas via an interpretable deep learning model: a multicentre and retrospective study.#174810
Record · ID 420953
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive