Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes

Abstract

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

“…Since EEG is very prone to noise and different type of artifacts, filtering of EEG signals is widely studied in EEG recognition context. The study conducted in [75] compares three types of smoothing filters (smooth filter, median filter and Savitzky-Golay) on EEG data for the medical diagnostic purposes. The authors concluded that the most useful filter is the classical Savitzky-Golay since it smooths the data without distorting the shape of the waves.…”

“…However, in very short time intervals (in the range of few seconds), the emotions show lesser variance in healthy individuals with good emotion regulation. Different from the studies [75,76], the filtering is applied on the output in our method. It is assumed that in a defined small-time interval T the emotion state does not change.…”

Vital Sign Monitoring in Car Seats Based on Electrocardiography, Ballistocardiography and Seismocardiography: A Review

“…Monitoring of vital functions in a car faces many problems with signal distortion due to interference caused by the vehicle and driver himself—for example, movement artifacts [ 75 ]. These artifacts are non-stationary and more prevalent in discrete sensory techniques [ 76 ]. Thus, the current research deals with their elimination using sensor fusion, which means combining multiple sensors at different locations, even different types of sensors [ 77 ].…”

“…Analysis of EEG signals is a very complex task which is even more difficult when the main system has to communicate with other various subsystems to source data or maintain proper [ 76 , 105 , 106 , 107 ]. That is due to the non-stationary character of these signals, their susceptibility to various artifacts (external and internal) and other disturbances [ 76 , 106 , 107 ].…”

“…Analysis of EEG signals is a very complex task which is even more difficult when the main system has to communicate with other various subsystems to source data or maintain proper [ 76 , 105 , 106 , 107 ]. That is due to the non-stationary character of these signals, their susceptibility to various artifacts (external and internal) and other disturbances [ 76 , 106 , 107 ]. The EEG signal is usually analyzed and evaluated in various frequency spectra: Delta rhythm (0.5 ≤ f < 4 Hz) is a symptom of deeper stages of sleep; Theta rhythm (4 Hz ≤ f < 8) is typically present in the initial stages of sleep; Alpha rhythm (8 Hz ≤ f ≤ 13) is the main manifestation of the resting brain activity, while Beta rhythm (13 < f < 30 Hz) is present in nervous or anxious subjects.…”