A Comparative Analysis between FFT, EMD, and EEMD for Epilepsy Detection

Abstract

Context: Epilepsy is a neurological disease that affects more than 50 million people worldwide, causing recurrent seizures, with a significant impact on patients' quality of life due to abnormally synchronized neuronal activity. Method: This article discusses three methods used for signal analysis in patients diagnosed with epilepsy. Conventional signal decomposition methods, such as the fast Fourier transform, widely used in signal analysis based on time series techniques, have some issues when analyzing nonlinear and non-stationary signals, in addition to difficulties in detecting low-order frequencies. Results: To overcome these limitations, alternatives such as empirical mode decomposition and one of its variants, called ensemble empirical mode decomposition, have been developed. These techniques allow observing different oscillation modes through intrinsic mode functions and instantaneous frequencies. Conclusions: In this study, the results obtained through the aforementioned techniques were compared, revealing the impact of nonlinear methods on the reconstruction of brain activity.

Keywords

• electroencephalogram
• empirical mode decomposition
• epilepsy
• instantaneous frequency
• intrinsec mode functions
• methodology
• non-lineal
• non-stationary
• oscillation modes
• seizures