Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization

Abstract

In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.

Formulation of the Challenges in Brain-Computer Interfaces as Optimization Problems—A Review

“…The detection of epileptic seizures is attempted using grid search optimization as in Wang et al ( 2019 ). The usage of optimization in this study was to tune the parameters of the random forest algorithm as it mainly generates a large number of hyperparameters and it is difficult to empirically arrive at the optimal values of these parameters.…”

“…The objective function thus aims at minimizing the linear discriminant analysis-based coefficients of the frequency bin summations done over an assortment of bins and traversed using certain constants called the slack variables. For the feature selection process, the objective function aims at minimizing the following, The detection of epileptic seizures is attempted using grid search optimization as in Wang et al (2019). The usage of optimization in this study was to tune the parameters of the random forest algorithm as it mainly generates a large number of hyperparameters and it is difficult to empirically arrive at the optimal values of these parameters.…”

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

“…The final output is the fusion of all other outputs of all decision trees. The algorithm is less sensitive to the curse of dimensionality and sufficient for both fNIRS and EEG application, even with less training data (Steyrl et al, 2016 ; Liu D. et al, 2017 ; Liu et al, 2018 ; Wang et al, 2019 ).…”

A New Machine Learning Approach in Detecting the Oil Palm Plantations Using Remote Sensing Data

“…It is difficult to select the optimal parameters by relying on experience alone. Fortunately, the grid search optimization (GSO) method [51], which searches the grid area of a variable to find the optimal grid point that satisfies the constraint function, has been widely used in the optimization of classification algorithm hyper-parameters. However, searching all the hyper-parameters on the grid requires a considerable amount of time.…”

“…To speed up the search time, we used a long-distance step size for a rough search over a large range and used small-distance steps to further refine the grid near the optimal point. Additionally, based on error rate and information entropy of OOB data, we proposed the estimate function f OOB to estimate the generalization error of the objective function, which can evaluate the strength of a decision tree and the correlations between the decision trees [51]. Suppose that OOB N (x) is the OOB data of RF classification model, N is the number of OOB data, n is the number of correctly classified data in OOB data, e n and H(n) are error rate and information entropy of OOB data, respectively.…”