Digital Signal Characterization for Seizure Detection Using Frequency Domain Analysis

University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

Author: Jing Li; [2021]

Keywords: Fourier Transform; Wavelet Transform; EEG and ECG Anomaly Detection; Approximate Entropy; Hellinger Distance; Long Short- Term Memory; Fourier Transform; Wavelet Transform; EEG och ECG Anomalidetektion; Approximativ Entropi; Hellinger Distans; Lång Korttidsminne;

Abstract: Nowadays, a significant proportion of the population in the world is affected by cerebral diseases like epilepsy. In this study, frequency domain features of electroencephalography (EEG) signals were studied and analyzed, with a view being able to detect epileptic seizures more easily. The power spectrum and spectrogram were determined by using fast fourier transform (FFT) and the scalogram was found by performing continuous wavelet transform (CWT) on the testing EEG signal. In addition, two schemes, i.e. method 1 and method 2, were implemented for detecting epileptic seizures and the applicability of the two methods to electrocardiogram (ECG) signals were tested. A third method for anomaly detection in ECG signals was tested. 

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