Quantitative and Qualitative Analyses of Invasive EEG for Epileptic Seizure Prediction

Original Article

Austin J Clin Neurol 2021; 8(1): 1144.

Quantitative and Qualitative Analyses of Invasive EEG for Epileptic Seizure Prediction

Hussein R1,2* and Ward R1

¹University of British Columbia, Electrical and Computer Engineering, Canada

²Stanford University, Center for Artificial Intelligence in Medicine & Imaging, USA

*Corresponding author: Ramy Hussein, Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; Center for Artificial Intelligence in Medicine & Imaging, Stanford University, Stanford, CA 94305, USA

Received: February 09, 2021; Accepted: March 06, 2021; Published: March 13, 2021


This study aims at explaining why existing machine learning methods have achieved limited performance when applied to the problems of seizure prediction using human invasive EEG (iEEG) data. We provide quantitative and qualitative analyses of iEEG data, as this data has commonly been used for seizure prediction tasks. Analyzing and understanding the iEEG signals provide insights into the characteristics of the preictal and interictal brain signals (i.e., the signals preceding and between epileptic seizure attacks). Experimental results show that: (1) the iEEG data varies from one patient to another. Therefore, iEEG features collected from a patient over a specific time period may not achieve reliable seizure prediction performance for other patient neither for the same patient at some future times, (2) for iEEG dimensionality reduction, the exclusion of any individual iEEG channel may result in losing spatial information needed for accurate seizure prediction, as the different iEEG channels contain complementary information. Also, Principal Component Analysis is found to be efficient for iEEG dimensionality reduction in seizure prediction tasks, and (3) For a particular patient, the distribution of the preictal and of the interictal iEEG data vary over time, and thus negatively affect the predictive ability of pretrained seizure prediction methods.

Keywords: Seizure prediction; EEG; PCA; Channel selection; Data mismatch


Epilepsy is a neurological disorder that affects around 70 million people worldwide [1]. It is characterized by recurrent seizures that strike without warning. Symptoms may range from a brief suspension of awareness to violent convulsions and sometimes loss of consciousness [2]. Currently, anti-epileptic drugs are given to epileptic patients in sufficiently high dosages. These drugs could result in undesirable side effects such as tiredness, stomach discomfort, dizziness, and also blurred vision. Further, the patient’s quality of life is severely affected by the anxiety associated with the unpredictable nature of seizures and the consequences therefrom. This motivated researchers to develop automatic seizure prediction systems [3]. The ability to predict seizures with high accuracy could make individualized epilepsy treatment possible (e.g., tailored therapies with fewer side effects). By having warnings of impending seizures, the patients can take their precautions and avoid any probable injuries. This vision inspired the proposed research.

Even though epileptic seizures seem unpredictable and often occur without warning, recent investigations have demonstrated that seizures do not strike at random [4,5]. Most existing seizure prediction methods, however, achieve limited performance [6-11]. Only patient-specific solutions that are tailored to individual patients have given good results [12,13], however, these methods have poor ability to adapt to new unseen data gathered from other patients. Intracranial Electroencephalogram (iEEG) is a common tool used for seizure prediction. The iEEG data that directly precede seizures is analyzed to identify the biomarkers that indicate upcoming seizures. In 2013, a big dataset of long-term iEEG recordings has been recorded (for 374-559 days) from three patients with drug-resistant epilepsy [8]. A subset of this dataset was made publicly available and used in the Melbourne University Seizure Prediction Competition organized in November 2016 on Kaggle.com. In [12], Kuhlmann et al. describe the human iEEG dataset used in this contest and the results achieved by the top eight seizure prediction solutions. Most of these solutions, however, are patient-specific and have a lower chance of being able to generalize beyond the statistical patterns of the training examples.

Neuroscientists have found that the temporal dynamics of the brain activity of epileptic patients can often be categorized into four different states: preictal (directly prior to a seizure), ictal (during a seizure), postictal (directly after a seizure), and interictal (between two consecutive seizures). Accurate seizure prediction solutions necessitate that prediction methods are able to differentiate between the preictal and interictal brain activities with high levels of accuracy. In this work, we provide extensive analyses of the human iEEG data preceding seizures (i.e., preictal) and between consecutive seizures (i.e., interictal). These analyses present some insights into the brain data and help us to better understand the unpredictable nature of epileptic seizures that we have been attempting to quantify.

As the success of seizure prediction solutions rely on their ability to differentiate between the interictal and preictal brain states, we first investigate whether interictal and preictal iEEGs are statistically different. We also study dimensionality reduction of iEEG data, as the huge amount of iEEG data involved in seizure studies could hamper the practical applicability of feature engineering and classification procedures. We study whether the popular Principal Component Analysis (PCA) could be effectively used for reducing the iEEG dimensional space, and whether excluding some iEEG channels (i.e., sensors) could result in reliable dimensionality reduction. Finally, we examine the cross-correlations between the different iEEG sensor readings during the preictal and interictal brain states and how could this knowledge be used. Below we first describe the human iEEG data and the subjects employed in this paper.

Subjects and Data

The iEEG data used is from the 2016 Kaggle seizure prediction competition and is described in [12]. The data were recorded constantly from humans suffering from refractory (drug-resistant) focal epilepsy using the NeuroVista Seizure Advisory System (described in [8]). Sixteen electrodes (4×4 contact strips) were implanted in all patients, directed to the presumed seizure focus, and connected to a telemetry unit embedded in the subclavicular area. Data were sampled at 400Hz, digitized using a 16-bit analogto- digital converter, wirelessly transmitted to an external hand-held advisory device, and continuously stored in a removable flash drive. (Figure 1) shows an example of a Computerized Tomography (CT) scan of the head of one of the patients and reveals the locations of the 16 iEEG electrodes on the cortical surface of the brain.