Brain Age Prediction/Classification through Recurrent Deep Learning with Electroencephalogram Recordings of Seizure Subjects

Abstract

With modern population growth and an increase in the average lifespan, more patients are becoming afflicted with neurodegenerative diseases such as dementia and Alzheimer’s. Patients with a history of epilepsy, drug abuse, and mental health disorders such as depression have a larger risk of developing Alzheimer’s and other neurodegenerative diseases later in life. Utilizing recordings of patients’ brain waves obtained from the Temple University abnormal electroencephalogram (EEG) corpus, deep leaning long short-term memory neural networks are utilized to classify and predict patients’ brain ages. The proposed deep learning neural network model structure and brain wave-processing methodology leads to an accuracy of 90% in patients’ brain age classification across six age groups, with a mean absolute error value of 7 years for the brain age regression analysis. The achieved results demonstrate that the use of raw patient-sourced brain wave information leads to higher performance metrics than methods utilizing other brain wave-preprocessing methods and outperforms other deep learning models such as convolutional neural networks.

SGAAE-AC: A Semi-Supervised Graph Attention Autoencoder for Electroencephalography (EEG) Age Clustering

“…For instance, Kaur and colleagues applied the random forest algorithm to predict age and gender [11], achieving an age classification accuracy of 88.33% and a gender classification accuracy of 96.66%. Around the 2020s, deep learning technology made breakthrough progress in this area [12][13][14]. Among these, Jusseaume et al utilized Long Short-Term Memory (LSTM) networks to analyze EEG records of epilepsy patients [13], successfully predicting the patients' brain age with an accuracy of up to 90%.…”

“…Around the 2020s, deep learning technology made breakthrough progress in this area [12][13][14]. Among these, Jusseaume et al utilized Long Short-Term Memory (LSTM) networks to analyze EEG records of epilepsy patients [13], successfully predicting the patients' brain age with an accuracy of up to 90%. However, despite these advances in EEG-based age prediction and classification, none of the studies addressed the patterns of how aging affects EEG background wave activity, nor the characteristics of EEG background waves at different ages.…”

Modeling EEG Signals for Mental Confusion Using DNN and LSTM With Custom Attention Layer

“…As learning techniques have advanced, there has been growing interest and active research into applying learning techniques to enhance the accuracy of analyzing vast amounts of EEG data. EEG signals are commonly used to monitor brain activity in patients with neurological disorders [25]. As people age, their ability to multitask decreases, and the risk of neurodegenerative diseases increases.…”

Classifying Routine Clinical Electroencephalograms With Multivariate Iterative Filtering and Convolutional Neural Networks

“…Time series are high dimensional data that are more suited for deep learning approaches compared to the classical (non-neural) machine learning methods. For instance, the work in [21] aims to predict the brain age of the participants using EEGs as a direct input to a bi-long short term memory (Bi-LSTM) and gated recurrent unit (GRU) models. The age of participants is categorized into six age groups, and classification accuracy is obtained as the metric for analyzing the performance of the models.…”