Automated inter-patient seizure detection using multichannel Convolutional and Recurrent Neural Networks

Jeff Craley, Emily Johnson, Christophe Jouny, Archana Venkataraman

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We present an end-to-end deep learning model that can automatically detect epileptic seizures in multichannel electroencephalography (EEG) recordings. Our model combines a Convolutional Neural Network (CNN) and a Bidirectional Long Short-Term Memory (BLSTM) network to efficiently mine information from the EEG data using a small number of trainable parameters. Specifically, the CNN learns a latent encoding for each one second window of raw multichannel EEG data. In conjunction, the BLSTM learns the temporal evolution of seizure presentations given the CNN encodings. The combination of these architectures allows our model to capture both the short time scale EEG features indicative of seizure activity as well as the long term correlations in seizure presentations. Unlike most prior work in seizure detection, we mimic an in-patient monitoring setting through a leave-one-patient-out cross validation procedure, attaining an average seizure detection sensitivity of 0.91 across all patients. This strategy verifies that our model can generalize to new patients. We demonstrate that our CNN–BLSTM outperforms both conventional feature extraction methods and state-of-the-art deep learning approaches that rely on larger and more complex network architectures.

Original languageEnglish (US)
Article number102360
JournalBiomedical Signal Processing and Control
StatePublished - Feb 2021


  • Convolutional Neural Networks
  • Epilepsy
  • Long Short-Term Memory networks
  • Seizure detection

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics


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