Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means

Abstract

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

“…Unsupervised learning algorithms can learn from unlabeled data sets and visually represent them. Unsupervised learning is primarily employed in two domains: first, it enables the extraction of representative features from high-dimensional data to reduce data sparsity and complexity through dimensionality reduction techniques, such as principal component analysis (PCA) ,,,,, and t-distributed stochastic neighbor embedding (t-SNE); ,− second, it facilitates grouping of data points on the basis of pairwise similarity metrics using clustering methods, like K-Means clustering ,− and hierarchical clustering, − thereby revealing inherent relationships among the data.…”

“…Big data is mainly collected through experimental tests ,− or literature and databases. , Subsequently, matching appropriate unsupervised − or supervised − learning algorithms to train this collected data leads to achieving accurate disease diagnoses (Figure ). Through continuous optimization of workflow modules and processes, two types of machine-learning-powered big data disease diagnosis sensors have gradually evolved: (1) machine learning diagnosis sensors ,− and (2) molecular computing diagnosis sensors. − Despite existing introductions and summaries of these sensors, there is still limited understanding regarding the relationship between data collection, machine learning, and disease diagnosis modules.…”

Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features

“…The quality of the images in a dataset significantly impacts the accuracy of facial feature detection and recognition algorithms. Pre-processing helps to improve the image quality by removing noise, correcting distortions, and adjusting the brightness and contrast [30]. (2) To make the images more consistent.…”

CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique

“…Moreover, EEG signals can be affected by real-world scenarios such as noise and artifacts. Various artifacts, such as muscle activity, eye movement, or electrical interference (artifact contamination), can contaminate EEG recordings [5]. Specialist expertise and experience are required to make a diagnosis using EEG signals based on seizure signal visual examinations captured during EEG sessions.…”