Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach

Abstract

Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical setting. In this paper, we present an electroencephalography (EEG) frequency bands (sub-bands) and montages selection (sub-zones) method for classifier training that exploits Natural Language Processing from individual patients’ clinical report. The proposed approach is targeting for individualized treatment. We integrated the prior knowledge from patient’s reports into the classifier-building process, mimicking the authentic thinking process of experienced neurologist’s when diagnosing seizure using EEG. The keywords from clinical documents are mapped to the EEG data in terms of frequency bands and scalp EEG electrodes. The data of experiments are from the Temple University Hospital EEG seizure corpus, and the dataset is divided based on each group of patients with same seizure type and same recording electrode references. The classifier includes Random Forest, Support Vector Machine and Multi-Layer Perceptron. The classification performance indicates that competitive results can be achieve with a small portion of EEG the data. Using the sub-zones selection for Generalized Seizures (GNSZ) on all three electrodes, data are reduced by nearly 50% while the performance metrics remain at the same level with the whole frequency and zones. Moreover, when selecting by sub-zones and sub-bands together for GNSZ with Linked Ears reference, the data range reduced to 0.3% of whole range, and the performance deviates less than 3% from the results with whole range of data. Results show that using proposed approach may lead to more efficient implementations of the seizure classifier to be executed on power-efficient devices for long lasting real-time seizures detection.

Role of EEG and MRI brain in the diagnosis of unprovoked seizures in children in a tertiary care centre

“…This might be because of the metamorphic pattern of focal seizures in EEG in which, as the seizure ends, rhythmic waves or sequential spikes change to a slow wave pattern that gradually decreases in frequency. [12] Also, this might be because most of the aware focal seizures may not be associated with discernible changes in routine scalp EEG [13], and the present study had 27.27 % focal aware seizures with normal EEG patterns. This might also be because focal and generalized seizure disorders show some overlap of both clinical and electrographic manifestations, and the entity of unihemispheric epilepsies blurs the boundaries further [14].…”

Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals—A Systematic Literature Review

“…Additionally, the procedure may be required in some applications such as wearable devices where using a large number of channels is impractical [ 26 ]. Channel selection can be performed using different approaches, whether they are statistical approaches [ 11 , 22 , 36 , 62 , 75 , 76 , 113 ], data-driven approaches [ 14 , 88 , 116 , 117 , 118 ], wrapper approaches [ 119 ], or from prior knowledge based on previous studies [ 120 , 121 ].…”

Machine learning‐based classifying of risk‐takers and risk‐aversive individuals using resting‐state EEG data: A pilot feasibility study

“…One of the most widespread algorithms that researchers have used for EEG classification is the SVM classifier algorithm. The SVM is a supervised ML model that can classify high‐dimensional feature space based on the hyperplane (Shanir et al., 2018 ; Wang et al., 2022 ). This classification algorithm reduces the time required for the learning phase by transforming the prediction problem into an optimization problem and has better accuracy and speed than the other algorithms.…”