Lightweight Convolution Transformer for Cross-patient Seizure Detection in Multi-channel EEG Signals

05/07/2023
by   Salim Rukhsar, et al.
0

Background: Epilepsy is a neurological illness affecting the brain that makes people more likely to experience frequent, spontaneous seizures. There has to be an accurate automated method for measuring seizure frequency and severity in order to assess the efficacy of pharmacological therapy for epilepsy. The drug quantities are often derived from patient reports which may cause significant issues owing to inadequate or inaccurate descriptions of seizures and their frequencies. Methods and materials: This study proposes a novel deep learning architecture based lightweight convolution transformer (LCT). The transformer is able to learn spatial and temporal correlated information simultaneously from the multi-channel electroencephalogram (EEG) signal to detect seizures at smaller segment lengths. In the proposed model, the lack of translation equivariance and localization of ViT is reduced using convolution tokenization, and rich information from the transformer encoder is extracted by sequence pooling instead of the learnable class token. Results: Extensive experimental results demonstrate that the proposed model of cross-patient learning can effectively detect seizures from the raw EEG signals. The accuracy and F1-score of seizure detection in the cross-patient case on the CHB-MIT dataset are shown to be 96.31 the performance metrics show that the inclusion of inductive biases and attention-based pooling in the model enhances the performance and reduces the number of transformer encoder layers, which significantly reduces the computational complexity. In this research work, we provided a novel approach to enhance efficiency and simplify the architecture for multi-channel automated seizure detection.

READ FULL TEXT
research
09/18/2022

EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers

Epilepsy is one of the most common neurological diseases, characterized ...
research
06/23/2023

TACOformer:Token-channel compounded Cross Attention for Multimodal Emotion Recognition

Recently, emotion recognition based on physiological signals has emerged...
research
12/15/2021

EEG-Transformer: Self-attention from Transformer Architecture for Decoding EEG of Imagined Speech

Transformers are groundbreaking architectures that have changed a flow o...
research
07/31/2016

Learning Robust Features using Deep Learning for Automatic Seizure Detection

We present and evaluate the capacity of a deep neural network to learn r...
research
12/31/2019

Driver fatigue EEG signals detection by using robust univariate analysis

Driver fatigue is a major cause of traffic accidents and the electroence...
research
09/18/2019

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Objective: Epilepsy is a chronic neurological disorder characterized by ...
research
03/13/2023

Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals

Pain is a serious worldwide health problem that affects a vast proportio...

Please sign up or login with your details

Forgot password? Click here to reset