Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques

Abstract

The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.

Method of automated epileptiform seizures and sleep spindles detection in the wavelet spectrogram of rats' EEG

“…A review of the works on automatic epileptic seizures detection is presented in [5,6]. In [5], the EEG signal is decomposed into empirical modes, for which the standard deviation, the asymmetry coefficients and the kurtosis are calculated.…”

“…In [6], the parameters for the seizure classification are skewness and kurtosis coefficients, the Fourier peak frequency of the spectrum, the median of the frequency, entropy, correlation dimension, and variance of the EEG signals. However, as in [5], epileptic discharge is distinguished from the background activity, but other high-amplitude events present in the signal were not considered.…”

Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals

“…Despite the use of new antiepileptic drugs, one-third still have seizures. The electrical discharges related to epilepsy are called epileptiform paroxysms, appearing in a single, rhythmic or periodic form, and classified into spikes (lasting from 20 to 70ms), sharp waves (70 to 200ms), and several types of epileptiform complexes like spike-wave ( Figure 1) (Arunkumar et al, 2012;Fergus et al, 2015;Stevanovic, 2012;Tatum IV et al, 2009).…”

“…However, EEG interpretation becomes tedious due to variability in amplitude, phase, frequency, and non-periodic features. Neurologists analyze the recordings by the review of large datasets, being a time-consuming, stressful and subjective diagnostic process (Arunkumar et al, 2012;Fergus et al, 2015;Peker et al, 2016;Ramgopal et al, 2014;Wang et al, 2014).…”

“…Several techniques were explored in order to improve the performance of Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals automated systems (Arunkumar et al, 2012;Fergus et al, 2015;Gotman and Gloor, 1976;Hunyadi et al, 2011;Olejarczyk et al, 2009;Peker et al, 2016;Petersen et al, 2013;Ramgopal et al, 2014;Wang et al, 2014). In the last decades, a very powerful tool called wavelet transform was applied to solve this problem (Adeli et al, 2007;Ayoubian et al, 2013;Haydari et al, 2011).…”