On wavelet analysis of auditory evoked potentials. 2004

A P Bradley, and W J Wilson
Cooperative Research Centre for Sensor Signal and Information Processing (CSSIP), School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, QLD 4072, Australia.

OBJECTIVE To determine a preferred wavelet transform (WT) procedure for multi-resolution analysis (MRA) of auditory evoked potentials (AEP). METHODS A number of WT algorithms, mother wavelets, and pre-processing techniques were examined by way of critical theoretical discussion followed by experimental testing of key points using real and simulated auditory brain-stem response (ABR) waveforms. Conclusions from these examinations were then tested on a normative ABR dataset. RESULTS The results of the various experiments are reported in detail. CONCLUSIONS Optimal AEP WT MRA is most likely to occur when an over-sampled discrete wavelet transformation (DWT) is used, utilising a smooth (regularity >or=3) and symmetrical (linear phase) mother wavelet, and a reflection boundary extension policy. CONCLUSIONS This study demonstrates the practical importance of, and explains how to minimize potential artefacts due to, 4 inter-related issues relevant to AEP WT MRA, namely shift variance, phase distortion, reconstruction smoothness, and boundary artefacts.

UI MeSH Term Description Entries
D011930 Reaction Time The time from the onset of a stimulus until a response is observed. Response Latency,Response Speed,Response Time,Latency, Response,Reaction Times,Response Latencies,Response Times,Speed, Response,Speeds, Response
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000328 Adult A person having attained full growth or maturity. Adults are of 19 through 44 years of age. For a person between 19 and 24 years of age, YOUNG ADULT is available. Adults
D000465 Algorithms A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task. Algorithm
D012815 Signal Processing, Computer-Assisted Computer-assisted processing of electric, ultrasonic, or electronic signals to interpret function and activity. Digital Signal Processing,Signal Interpretation, Computer-Assisted,Signal Processing, Digital,Computer-Assisted Signal Interpretation,Computer-Assisted Signal Interpretations,Computer-Assisted Signal Processing,Interpretation, Computer-Assisted Signal,Interpretations, Computer-Assisted Signal,Signal Interpretation, Computer Assisted,Signal Interpretations, Computer-Assisted,Signal Processing, Computer Assisted
D016057 Evoked Potentials, Auditory, Brain Stem Electrical waves in the CEREBRAL CORTEX generated by BRAIN STEM structures in response to auditory click stimuli. These are found to be abnormal in many patients with CEREBELLOPONTINE ANGLE lesions, MULTIPLE SCLEROSIS, or other DEMYELINATING DISEASES. Acoustic Evoked Brain Stem Potentials,Auditory Brain Stem Evoked Responses,Brain Stem Auditory Evoked Potentials,Evoked Responses, Auditory, Brain Stem,Acoustic Evoked Brain Stem Potential,Acoustic Evoked Brainstem Potential,Acoustic Evoked Brainstem Potentials,Auditory Brain Stem Evoked Response,Auditory Brain Stem Response,Auditory Brain Stem Responses,Auditory Brainstem Evoked Response,Auditory Brainstem Evoked Responses,Auditory Brainstem Responses,Brain Stem Auditory Evoked Potential,Brainstem Auditory Evoked Potential,Brainstem Auditory Evoked Potentials,Evoked Potential, Auditory, Brainstem,Evoked Potentials, Auditory, Brainstem,Evoked Response, Auditory, Brain Stem,Evoked Response, Auditory, Brainstem,Evoked Responses, Auditory, Brainstem,Auditory Brainstem Response,Brainstem Response, Auditory,Brainstem Responses, Auditory,Response, Auditory Brainstem,Responses, Auditory Brainstem

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