mlsanityData to 14 Oct 2026

MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG

arXiv 2211.04628 ↗First posted 9 Nov 2022eess.SP cs.LGcited by 2

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Abstract (arXiv)

Seizure type identification is essential for the treatment and management of epileptic patients. However, it is a difficult process known to be time consuming and labor intensive. Automated diagnosis systems, with the advancement of machine learning algorithms, have the potential to accelerate the classification process, alert patients, and support physicians in making quick and accurate decisions. In this paper, we present a novel multi-path seizure-type classification deep learning network (MP-SeizNet), consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory neural network (Bi-LSTM) with an attention mechanism. The objective of this study was to classify specific types of seizures, including complex partial, simple partial, absence, tonic, and tonic-clonic seizures, using only electroencephalogram (EEG) data. The EEG data is fed to our proposed model in two different representations. The CNN was fed with wavelet-based features extracted from the EEG signals, while the Bi-LSTM was fed with raw EEG signals to let our MP-SeizNet jointly learns from different representations of seizure data for more accurate information learning. The proposed MP-SeizNet was evaluated using the largest available EEG epilepsy database, the Temple University Hospital EEG Seizure Corpus, TUSZ v1.5.2. We evaluated our proposed model across different patient data using three-fold cross-validation and across seizure data using five-fold cross-validation, achieving F1 scores of 87.6% and 98.1%, respectively.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 69 references that are in the map. First posted on arXiv 9 Nov 2022.

Builds on

15 of 69 references.

References · 15

  1. 2016A Survey of Multi-View Representation Learningcited by 107
  2. 2018The Temple University Hospital Seizure Detection Corpuscited by 83
  3. 2021Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysiscited by 76
  4. 2020Machine-Learning-Based Diagnostics of EEG Pathologycited by 35
  5. 2018ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identificationcited by 27
  6. 2019Neural Memory Networks for Seizure Type Classificationcited by 19
  7. 2017Deep learning with convolutional neural networks for decoding and visualization of EEG pathologycited by 13
  8. 2019Classification of Hand Movements from EEG using a Deep Attention-based LSTM Networkcited by 12
  9. 2021Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropiescited by 9
  10. 2020Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision treecited by 5
  11. 2020Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Modelscited by 3
  12. 2019Deep Learning with ConvNET Predicts Imagery Tasks Through EEGcited by 2
3 more
  1. 2022Wavelet-Based Multi-Class Seizure Type Classification Systemcited by 2
  2. 2021Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysiscited by 1
  3. 2021Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithmcited by 1

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary studycited by 4
  2. 2025Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning modelscited by 0