Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks

By Alexandros T. Tzallas, Markos G. Tsipouras, Dimitrios I. Fotiadis. Computational Intelligence and Neuroscience, 2007

485 citations recorded by the source corpus.

The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1287998ac32a33d805e83e1b20509077b7aa1433

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In Cardiology and Cardiovascular Medicine

In Cognitive Neuroscience

In Signal Processing

Citations in the corpus

A review of epileptic seizure detection using machine learning classifiers (2020, methodology): “[103] used time–frequency-domain features with ANN for the EEG dataset and obtained 100% accuracy for the ‘seizure’ and ‘non-seizure’ classification problem; with epochs’ datasets the accuracy is 97.”
A review of feature extraction and performance evaluation in epileptic seizure detection using EEG (2019, methodology): “Similarly, fractional energies, energies in speciﬁc frequency bands and time windows of a Smoothed pseudo WVD (SPWVD) were applied to principal component analysis (PCA) and ANN, resulting in a high average accuracy of 95.00% [TTF07].”
Automated EEG analysis of epilepsy: A review (2013, methodology): “[114] Time–frequency methods Artificial neural network 97.”