Ischemic Stroke Identification Based on EEG and EOG using 1D Convolutional Neural Network and Batch Normalization

TL;DR

This study develops a 1D CNN model with batch normalization to classify ischemic stroke using EEG and EOG signals, achieving high accuracy and outperforming traditional classifiers, offering a promising diagnostic tool for resource-limited settings.

Contribution

The paper introduces a novel 1D CNN approach with batch normalization for stroke classification using EEG and EOG data, demonstrating superior accuracy over traditional classifiers.

Findings

Achieved 86% accuracy with 1D CNN on EEG/EOG data.

Outperformed shallow classifiers with accuracy up to 69%.

Used only 24 handcrafted features for classification.

Abstract

In 2015, stroke was the number one cause of death in Indonesia. The majority type of stroke is ischemic. The standard tool for diagnosing stroke is CT-Scan. For developing countries like Indonesia, the availability of CT-Scan is very limited and still relatively expensive. Because of the availability, another device that potential to diagnose stroke in Indonesia is EEG. Ischemic stroke occurs because of obstruction that can make the cerebral blood flow (CBF) on a person with stroke has become lower than CBF on a normal person (control) so that the EEG signal have a deceleration. On this study, we perform the ability of 1D Convolutional Neural Network (1DCNN) to construct classification model that can distinguish the EEG and EOG stroke data from EEG and EOG control data. To accelerate training process our model we use Batch Normalization. Involving 62 person data object and from leave one out the scenario with five times repetition of measurement we obtain the average of accuracy 0.86 (F-Score 0.861) only at 200 epoch. This result is better than all over shallow and popular classifiers as the comparator (the best result of accuracy 0.69 and F-Score 0.72 ). The feature used in our study were only 24 handcrafted feature with simple feature extraction process.