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

arXiv: https://arxiv.org/abs/2211.04628 · HTML: https://mlsanity.com/paper/2211.04628/

## 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 (most cited first, 15 of 15)

- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [The Temple University Hospital Seizure Detection Corpus](https://mlsanity.com/paper/1801.08085/) (2018)
- [Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis](https://mlsanity.com/paper/2104.08336/) (2021)
- [Machine-Learning-Based Diagnostics of EEG Pathology](https://mlsanity.com/paper/2002.05115/) (2020)
- [ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identification](https://mlsanity.com/paper/1802.00308/) (2018)
- [Neural Memory Networks for Seizure Type Classification](https://mlsanity.com/paper/1912.04968/) (2019)
- [Deep learning with convolutional neural networks for decoding and visualization of EEG pathology](https://mlsanity.com/paper/1708.08012/) (2017)
- [Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network](https://mlsanity.com/paper/1908.02252/) (2019)
- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree](https://mlsanity.com/paper/2012.10034/) (2020)
- [Epileptic Seizure Classification with Symmetric and Hybrid Bilinear Models](https://mlsanity.com/paper/2001.06282/) (2020)
- [Deep Learning with ConvNET Predicts Imagery Tasks Through EEG](https://mlsanity.com/paper/1907.05674/) (2019)
- [Wavelet-Based Multi-Class Seizure Type Classification System](https://mlsanity.com/paper/2203.00511/) (2022)
- [Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis](https://mlsanity.com/paper/2101.10263/) (2021)
- [Seizure Classification of EEG based on Wavelet Signal Denoising Using a Novel Channel Selection Algorithm](https://mlsanity.com/paper/2109.00852/) (2021)

## Built on by (most cited first, 2 of 2)

- [The role of data partitioning on the performance of EEG-based deep learning models in supervised cross-subject analysis: a preliminary study](https://mlsanity.com/paper/2505.13021/) (2025)
- [Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models](https://mlsanity.com/paper/2505.21507/) (2025)

## 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.