Statistical features-based comparison of analysis and synthesis of normal and epileptic electroencephalograms for various wavelets

Shriram, Revati; Sundhararajan, M.; Shete, Supriya; Daimiwal, Nivedita

doi:10.3906/elk-1511-254

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…MSE is calculated by dividing the squared norm of the difference between the data and the approximation by the number of elements, n . PSNR is the peak signal-to-noise ratio, measured in decibels [ 28 , 46 , 47 ]. The SNR, MSE, and PSNR were computed based on the following formulas:

where a is the original signal, b is the cleaned signal, n is number of samples, P a is power of the original signal, P n is the power of noise, and MAX f is the maximum signal value that exists in the cleaned signal.…”

Section: Methodsmentioning

confidence: 99%

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

Shivaraja

Remli

Kamal

et al. 2023

Sensors

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Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG’s signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments.

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Section: Methodsmentioning

confidence: 99%

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

Shivaraja

Remli

Kamal

et al. 2023

Sensors

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“…The following performance parameters have been calculated to evaluate the performance of filters -(i) Mean absolute error (MAE) is the absolute error between the reconstructed signal & the original signal and is reflective of the performance of filter structure. MAE is used as a gauge to know how much divergence is there in the reconstructed signal from the original signal [20].…”

Section: B Identification Of Competent Digital Filter Structurementioning

confidence: 99%

Performance Analysis of IIR & FIR Windowing Techniques in Electroencephalography Signal Processing

Anshul¹,

Bansal²,

Mahajan³

2019

IJITEE

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A very small amplitude (μV) of the electroencephalography (EEG) signal is infected by diverse artifacts. These artifacts have an effect on the distinctiveness of the signal because of which medical psychoanalysis and data retrieval is difficult. Therefore, EEG signals are initially preprocessed to eliminate the artifacts to produce signals that can serve as a base for further processing and analysis. Different filters are implemented to eliminate the artifacts present in the EEG signal. Recent research shows that window technique Finite Impulse Response (FIR) filter is usually used. In this paper, digital Infinite Impulse Response (IIR) filter and different Finite Impulse Response (FIR) window filters (Hanning, Hamming, Kaiser, Blackman) of various orders are implemented to eradicate the random noise added to EEG signals. Their performance analysis has been done in Matlab (R2016a) by calculating the mean square error, mean absolute error, signal to noise ratio, peak signal to noise ratio and cross-correlation. The results show that Kaiser Window based finite impulse response filter outperforms in removing the noise from the electroencephalogram signal. This research focuses on eradicating random noise in electroencephalogram signals but this approach will be extended to a different source of electroencephalogram contamination.

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“…Sleep is defined as quiescent sleep or non-rapideye-movement (NREM) sleep or rapid-eye-movement (REM) sleep based on the subject's physiological behaviour and the EEG. 1,2 The study of normal sleep and sleep disorders is critical because these are the most frequently encountered difficulties in people. Sleep apnea (SA) is a common sleep disorder defined by intermittent breathing during sleep phases.…”

Section: Introductionmentioning

confidence: 99%

Detection of sleep apnea using polysomnographic signals

et al. 2022

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Introduction: Sleep is important in humans, and it is affected by lifestyle changes. Improper sleep leads to serious physiological problems and disorders that occurs in human brain/scalp. These physiological changes and electrical activity of the human brain are recorded as electroencephalogram (EEG) signals. This paper describes the detection of a major sleep disorder \textit{i.e.}, sleep apnea (SA). Methods: In this paper, sleep apnea is measured using various artifacts taken from the subjects. The discrete wavelet transform (DWT) is used to extract characteristics from an electroencephalogram (EEG) signal and to detect sleep. This is used to determine whether a person has obstructive sleep apnea (OSA) or central sleep apnea (CSA). The wavelet technique is used to split the EEG signal into five frequency bands: delta, theta, alpha, beta, and gamma. Results: For these five frequency bands, the mean, standard deviation, variance, maximum, minimum, and energy are computed. Discussion: A sleep problem is detected based on these characteristics.

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Statistical features-based comparison of analysis and synthesis of normal and epileptic electroencephalograms for various wavelets

Cited by 6 publications

References 17 publications

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

Performance Analysis of IIR & FIR Windowing Techniques in Electroencephalography Signal Processing

Detection of sleep apnea using polysomnographic signals

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