A new feature for the classification of non-stationary signals based on the direction of signal energy in the time–frequency domain. (1st September 2018)

• Record Type: Journal Article
• Authors: Khan, Nabeel Ali; Ali, Sadiq
• Is Part Of: Computers in biology and medicine. Volume 100(2018)
• Page Start: 10
• Page End: 16
• Publication Date: 2018-09-01
• Subjects: EEG -- Adaptive time-frequency analysis -- Seizure detection -- Epilepsy; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2018.06.018
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The detection of seizure activity in electroencephalogram (EEG) segments is very important for the classification and localization of epileptic seizures. The evolution of a seizure in an EEG usually appears as a train of non-uniformly spaced spikes and/or as piecewise linear frequency modulated signals. If a seizure is present, then the energy of the EEG is concentrated along the time axis and the frequency axis in the time–frequency plane. However, in the absence of a seizure, the energy of the EEG signal is uniformly distributed along all directions in the time–frequency plane. Based on this observation, we propose a new approach for the detection of a seizure. In this paper, we develop a new feature that exploits the direction of the energy of the signal in the time–frequency domain to distinguish between seizures and non-seizures in an EEG. Our experimental results indicate the superiority of the proposed approach over other conventional time–frequency approaches; for example, the proposed feature set achieves a classification accuracy of 98.25% by only using five features. Highlights: A new feature based on the direction of signal energy in time-frequency domain has been defined. The proposed feature outperforms all other features in terms of area under receiver operator characteristics curve. The proposed classification methodology outperforms state of the art.