Epileptic Patient Activity Recognition System Using Extreme Learning Machine Method

Abstract

The Human Activity Recognition (HAR) system is the hottest research area in clinical research. The HAR plays a vital role in learning about a patient’s abnormal activities; based upon this information, the patient’s psychological state can be estimated. An epileptic seizure is a neurological disorder of the human brain and affects millions of people worldwide. If epilepsy is diagnosed correctly and in an early stage, then up to 70% of people can be seizure-free. There is a need for intelligent automatic HAR systems that help clinicians diagnose neurological disorders accurately. In this research, we proposed a Deep Learning (DL) model that enables the detection of epileptic seizures in an automated way, addressing a need in clinical research. To recognize epileptic seizures from brain activities, EEG is a raw but good source of information. In previous studies, many techniques used raw data from EEG to help recognize epileptic patient activities; however, the applied method of extracting features required much intensive expertise from clinical aspects such as radiology and clinical methods. The image data are also used to diagnose epileptic seizures, but applying Machine Learning (ML) methods could address the overfitting problem. In this research, we mainly focused on classifying epilepsy through physical epileptic activities instead of feature engineering and performed the detection of epileptic seizures in three steps. In the first step, we used the open-source numerical dataset of epilepsy of Bonn university from the UCI Machine Learning repository. In the second step, data were fed to the proposed ELM model for training in different training and testing ratios with a little bit of rescaling because the dataset was already pre-processed, normalized, and restructured. In the third step, epileptic and non-epileptic activity was recognized, and in this step, EEG signal feature extraction was automatically performed by a DL model named ELM; features were selected by a Feature Selection (FS) algorithm based on ELM and the final classification was performed using the ELM classifier. In our presented research, seven different ML algorithms were applied for the binary classification of epileptic activities, including K-Nearest Neighbor (KNN), Naïve Bayes (NB), Logistic Regression (LR), Stochastic Gradient Boosting Classifier (SGDC), Gradient Boosting Classifier (GB), Decision Trees (DT), and three deep learning models named Extreme Learning Machine (ELM), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN). After deep analysis, it is observed that the best results were obtained by our proposed DL model, Extreme Learning Machine (ELM), with an accuracy of 100% accuracy and a 0.99 AUC. Such high performance has not attained in previous research. The proposed model’s performance was checked with other models in terms of performance parameters, namely confusion matrix, accuracy, precision, recall, F1-score, specificity, sensitivity, and the ROC curve.

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach

“…As previously recommended 50 , we also strongly believe that a collaboration to create standardized datasets, selection of appropriate predictor variables for modelling, sharing of models and code, are essential for advancing this research field. It is important to note that in our study we used only scalp EEG signals, which are known to have lower spatial resolution and high level of signal noise 53 , 54 , if compared with to Stereo-EEG signals. Moreover, the performance of the ANN model may be affected by the dataset size and the monocentric recruitment.…”

Effective Detection of Epileptic Seizures through EEG Signals Using Deep Learning Approaches

“…Guided by prior analyses [5], we selected leading ML classifiers for comparative benchmarking, including k-nearest neighbors (KNN), naive Bayes (NB), logistic regression (LR), random forest (RF), decision trees (DT), stochastic gradient boosting (SGDC), and gradient boosting (GB). Recent studies confirm the utility of these algorithms paired with neurologists in accurately detecting seizures and characterizing epileptiform EEG dynamics [41,42].…”

“…To detect anomalous behaviors and enable early detection of epileptic seizures before they escalate into more severe states, human activity recognition (HAR) can be effectively applied [5]. Recent scholarly investigations have explored a range of techniques for identifying anomalous behaviors [6], including wearable technologies, sensor-based approaches, and ambient instrument methodologies.…”

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

“…As previously recommended [50], we also strongly believe that a collaboration to create standardized datasets, selection of appropriate predictor variables for modelling, sharing of models and code, are essential for advancing this research eld. It is important to note that in our study we used only scalp EEG signals, which are known to have lower spatial resolution and high level of signal noise [53,54], if compared with to Stero-EEG signals. Moreover, the performance of the ANN model may be affected by the dataset size and the monocentric recruitment.…”