[Noise attenuation analysis on auditory evoked potential based on maximum length sequence].

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi

School of Biomedical Engineering, Southern Medical University, Guangzhou 510515,

Published: April 2018

AI Article Synopsis

  • The study investigates how the order of maximum length sequences (m-sequences) affects noise attenuation in auditory evoked potentials (AEPs) using EEG data.
  • Results indicate that the noise attenuation ratios align with theoretical predictions, showing effective noise reduction regardless of the m-sequence order used.
  • The findings suggest that AEPs remain consistent across different m-sequence orders, highlighting a more effective framework for applying m-sequence methods to analyze non-linear AEPs.

Article Abstract

The maximum length sequence (m-sequence) has been successfully used to study the linear/nonlinear components of auditory evoked potential (AEP) with rapid stimulation. However, more study is needed to evaluate the effect of the m-sequence order in terms of the noise attenuation performance. This study aimed to address this issue using response-free electroencephalogram (EEG) and EEGs with nonlinear AEPs. We examined the noise attenuation ratios to evaluate the noise variation for the calculations of superimposed averaging and cross-correlation, respectively, which constitutes the main process in the deconvolution method using the dataset of spontaneous EEGs to simulate the cases of different orders (order 5 to 12) of m-sequences. And an experiment using m-sequences of order 7 and 9 was performed in true cases with substantial linear and nonlinear AEPs. The results demonstrate that the noise attenuation ratio is well agreed with the theoretical value derived from the properties of m-sequences on the random noise condition. The comparison of waveforms for AEP components from two m-sequences showed high similarity suggesting the insensitivity of AEP to the m-sequence order. This study provides a more comprehensive solution to the selection of m-sequences which will facilitate the feasible application on the nonlinear AEP with m-sequence method.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9935107PMC
http://dx.doi.org/10.7507/1001-5515.201703065DOI Listing

Publication Analysis

Top Keywords

noise attenuation
12
auditory evoked
8
evoked potential
8
maximum length
8
m-sequence order
8
nonlinear aeps
8
aep m-sequence
8
noise
5
m-sequences
5
[noise attenuation
4

Similar Publications

Objective: To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD).

Patients And Methods: Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation.

View Article and Find Full Text PDF

During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy images results in a higher radiation dose being administered to the patient. Filtering techniques can be utilized to reduce radiation without losing diagnosis capabilities.

View Article and Find Full Text PDF

The use of self-adaptive principal components in PCA-based denoising.

J Magn Reson

December 2024

Department of Low-Temperature Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 747/2, 180 00 Prague 8, Czech Republic.

PCA-based denoising usually implies either discarding a number of high-index principal components (PCs) of a data matrix or their attenuation according to a regularization model. This work introduces an alternative, model-free, approach to high-index PC attenuation that seeks to average values of PC vectors as if they were expected from noise perturbation of data. According to the perturbation theory, the average PCs are attenuated versions of the clean PCs of noiseless data - the higher the noise-related content in a PC vector, the lower is its average's norm.

View Article and Find Full Text PDF

Associations of Traumatic Brain Injury and Hearing: Results From the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS).

J Head Trauma Rehabil

December 2024

Author Affiliations: Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (Dr Schneider); Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania (Dr Schneider); Department of Psychiatry and Behavioral Sciences, School of Medicine, Johns Hopkins University, Baltimore, Maryland (Dr Kamath); Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland (Drs Reed, Sharrett, Lin, and Deal); The MIND Center, University of Mississippi Medical Center, Jackson, Mississippi (Dr Mosley); National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, Maryland (Dr Gottesman); Department of Otolaryngology, School of Medicine, Johns Hopkins University, Baltimore, Maryland (Drs Lin and Deal); and Cochlear Center for Hearing and Public Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Drs Lin and Deal).

Objective: To examine associations of traumatic brain injury (TBI) with self-reported and clinical measures of hearing function.

Setting: Four US communities.

Participants: A total of 3176 Atherosclerosis Risk in Communities Study participants who attended the sixth study visit in 2016-2017, when hearing was assessed.

View Article and Find Full Text PDF

An MRI-guided stereotactic neurosurgical robotic system for semi-enclosed head coils.

J Robot Surg

December 2024

National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, 100084, China.

Magnetic resonance imaging (MRI) offers high-quality soft tissue imaging without radiation exposure, which allows stereotactic techniques to significantly improve outcomes in cranial surgeries, particularly in deep brain stimulation (DBS) procedures. However, conventional stereotactic neurosurgeries often rely on mechanical stereotactic head frames and preoperative imaging, leading to suboptimal results due to the invisibility and the contact with patient's head, which may cause additional harm. This paper presents a frameless, MRI-guided stereotactic neurosurgical robotic system.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!