Power Spectral Analysis of EEG as a Potential Marker in the Diagnosis of Spastic Cerebralpalsycases

Abstract

The detection and diagnosis of various neurological disorders are performed using different medical devices among which electroencephalogram (EEG) is one of the most cost effective technique. Though significant progress had been made in the analysis of EEG for diagnosis of different neurological disorders, yet detection of cerebral palsy (CP) is not quite clear. This study was performed to analyze the EEG power spectrum density (PSD) of spastic CP and normal children to find if any significant EEG patterns could be used for early detection of CP. Twenty children participated in this study out of which ten were spastic CP and other ten were normal healthy children. EEG of all the participants was recorded from C3 C4 and F3 F4 regions following montage 10-20 system. The artifact-free EEG signals of 15 minutes duration was extracted for spectral analysis using Fast Fourier Transformation (FFT) algorithm in MATLAB and power density spectrum (PSD) was plotted. The PSD revealed high intensity power peak at frequency of 50Hz and smaller at 100 Hz, which was consistent for all healthy subjects. In case of spastic CP children, high intensity peak at 100Hz were prominent and smaller peak was observed at 50Hz. The high intensity 100Hz peak observed in the PSD of spastic CP patients demonstrated that this tool can be used for early detection of spastic CP.

Effect of correlated color temperature and illuminance on sustained attention collected via electroencephalography signals

“…This transformation is based on fast Fourier Transform (FFT) to measure the discrete transform and inverse transform of the Fourier series. Using this mathematical method of frequency analysis of complex waveforms, EEG signals can be quantitatively analyzed (Dressler et al, 2004; Rajak et al, 2016). Studies have shown that when people concentrate their attention, the amplitude of α waves was smaller than that of normal state, the signal power was smaller, and the corresponding PSD was smaller (Li et al, 2009).…”

“…Fourier Transform (FFT) to measure the discrete transform and inverse transform of the Fourier series. Using this mathematical method of frequency analysis of complex waveforms, EEG signals can be quantitatively analyzed (Dressler et al, 2004;Rajak et al, 2016).…”

EEG-Based Demarcation of Yogic and Non-Yogic Sleep Patterns Using Power Spectral Analysis

“…It is a well-established method of signal processing which is commonly used. Power spectral density is defined as the signal power distribution over frequency (Dressler et al, 2014;Rajak et al, 2016). It shows the strength of the energy as a frequency function (Sukhada A. Unde, 2014).…”

“…It shows the strength of the energy as a frequency function (Sukhada A. Unde, 2014). Power spectral density analysis is often used for quantification of EEG signals (Dressler et al, 2014;Rajak et al, 2016). A sensible way to identify periodicity within the waveforms and determination of the relative energy content of the periodicities are given by this mathematical method of frequency understanding of various waveforms which is power spectral density analysis (Rajak et al, 2017).…”

Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

“…Moreover, instead of the raw EEG signal, its Welch power spectral density (PSD) is calculated and then used as input to the ResNet based classifier. The Welch PSD determines the distribution of signal power over frequency [34], which reduces the estimated power spectra at the cost of reduced frequency resolution [35]. Human EEG studies have revealed that aging reduces the power of lower frequency bands, i.e., delta, theta, and sigma, while increasing that of the higher frequency bands, i.e., beta [36,37].…”