On wavelet analysis of auditory evoked potentials

Bradley, Andrew P.; Wilson, Wayne J.

doi:10.1016/j.clinph.2003.11.016

Cited by 67 publications

(43 citation statements)

References 52 publications

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“…Improper selection of center frequency and bandwidth related with the mother wavelet would cause in a decrease of the resolution in both time and frequency therefore detailed analysis for the proper selection of mother wavelet which is given for auditory evoked potentials in [23]. The time domain evoked potentials of averaged 27 sweeps for non-target and target are given with their continuous wavelet transforms in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The signals acquired by AEP were analyzed by continuous time wavelet transform where the mother wavelet was chosen as Bspline basis function as recommended in [21][22][23]. Improper selection of center frequency and bandwidth related with the mother wavelet would cause in a decrease of the resolution in both time and frequency therefore detailed analysis for the proper selection of mother wavelet which is given for auditory evoked potentials in [23].…”

Section: Resultsmentioning

confidence: 99%

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Continuous time wavelet entropy of auditory evoked potentials

Cek

Özgören

Savacı

2010

Computers in Biology and Medicine

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a b s t r a c tIn this paper, the continuous time wavelet entropy (CTWE) of auditory evoked potentials (AEP) has been characterized by evaluating the relative wavelet energies (RWE) in specified EEG frequency bands. Thus, the rapid variations of CTWE due to the auditory stimulation could be detected in post-stimulus time interval. This approach removes the probability of missing the information hidden in short time intervals. The discrete time and continuous time wavelet based wavelet entropy variations were compared on non-target and target AEP data. It was observed that CTWE can also be an alternative method to analyze entropy as a function of time.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Continuous time wavelet entropy of auditory evoked potentials

Cek

Özgören

Savacı

2010

Computers in Biology and Medicine

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“…Each averaged ABR waveform was then baseline shifted to a starting baseline of 0 lV, and decomposed using a six level OCDWT with dyadic scaling at each new scale. A biorthogonal 5.5 mother wavelet was used for the wavelet decomposition [as recommended by Bradley and Wilson (2004)]. The resulting wavelet coefficients were used to reconstruct the ABR waveform at approximation level A6 (0-391 Hz), and detail levels D6 (391-781 Hz) and D5 (781-1563 Hz).…”

Section: Signal Processingmentioning

confidence: 99%

“…The discrete wavelet transform (DWT), and its use as a multiresolution analysis (MRA) tool, has been widely described in the literature (Jawerth and Sweldens, 1994;Hess-Nielsen and Wickerhauser, 1996;Unser and Aldroubi, 1996;Blinowska and Durka, 1997;Samar et al, 1999;Wilson, 2002;Bradley and Wilson, 2004;Wilson, 2004;Zhang et al, 2004;Bradley and Wilson, 2005;Zhang et al, 2005). In summary, the DWT is a form of digital filtering capable of deconstructing a signal into its component scales (frequency ranges), and then detailing how each scale evolves over time.…”

Section: The Over-complete Discrete Wavelet Transformmentioning

confidence: 99%

On the dual structure of the auditory brainstem response in dogs

Wilson

Bailey

Balke

et al. 2006

Clinical Neurophysiology

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Objective: To use the over-complete discrete wavelet transform (OCDWT) to further examine the dual structure of auditory brainstem response (ABR) in the dog. Methods: ABR waveforms recorded from 20 adult dogs at supra-threshold (90 and 70 dBnHL) and threshold (0-15 dBSL) levels were decomposed using a six level OCDWT and reconstructed at individual scales (frequency ranges) A6 (0-391 Hz), D6 (391-781 Hz), and D5 (781-1563 Hz). Results: At supra-threshold stimulus levels, the A6 scale (0-391 Hz) showed a large amplitude waveform with its prominent wave corresponding in latency with ABR waves II/III; the D6 scale (391-781 Hz) showed a small amplitude waveform with its first four waves corresponding in latency to ABR waves I, II/III, V, and VI; and the D5 scale (781-1563 Hz) showed a large amplitude, multiple peaked waveform with its first six waves corresponding in latency to ABR waves I, II, III, IV, V, and VI. At threshold stimulus levels (0-15 dBSL), the A6 scale (0-391 Hz) continued to show a relatively large amplitude waveform, but both the D6 and D5 scales (391-781 and 781-1563 Hz, respectively) now showed relatively small amplitude waveforms. Conclusions: A dual structure exists within the ABR of the dog, but its relative structure changes with stimulus level. Significance: The ABR in the dog differs from that in the human both in the relative contributions made by its different frequency components, and the way these components change with stimulus level.

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“…Several approaches have been developed to analyze and classify biomedicine signals: electroencephalography signals [7], electrocardiogram signals [8], and particularly signals based on Auditory Brainstem Response (ABR) test, which is a test for hearing and brain (neurological) functioning, [6,[14][15][16]. Traditionally, biomedicine signals are processed using signal processing approaches, mainly based on peak and wave identification from pattern recognition approaches, such as in [6][7][8][14][15][16]. The main problem is then to identify pertinent parameters.…”

Section: Introductionmentioning

confidence: 99%

Hybrid intelligent diagnosis approach based on soft computing from signal and image knowledge representations for a biomedical application

Chohra

Kanaoui

Amarger

et al. 2014

Vietnam J Comput Sci

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Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods as neural networks and fuzzy logic, have shown great potential in the development of decision support systems. Dealing with expert (human) knowledge consideration, Computer-Aided Diagnosis (CAD) dilemma is one of the most interesting, but also one of the most difficult problems. Among difficulties contributing to challenging nature of this problem, one can mention the need of fine classification and decision-making. In this paper, a brief survey on fault diagnosis systems is given first. Then, from a fault diagnosis system analysis of the classification and decision-making problem, a global diagnosis synopsis is deduced. Afterwards, a hybrid intelligent diagnosis approach, based on soft computing implying modular neural networks for classification and fuzzy logic for decision-making, is suggested from signal and image representations. The suggested approach is developed in biomedicine for a CAD, from Auditory Brainstem Response test, and the prototype design and experimental results are presented. In fact, a double classification is exploited in a primary fuzzy diagnosis, to ensure a satisfactory reliability. Then, this reliability is reinforced using a confidence parameter with the primary diagnosis result, exploited in a final fuzzy diagnosis giving the appropriate diagnosis with a Indeed, experimental results demonstrate the efficiency and reliability of CAD for three classes: two auditory pathologies Retro-cochlear Class (RC) and Endocochlear Class (EC), and Normal auditory Class (NC). The generalization rate of NC is clearly higher for primary fuzzy diagnosis and final fuzzy diagnosis than that of the two classifications. The obtained rates for RC and EC are higher than obtained by image classification but quite similar than those obtained by signal classification. An important contribution of the final fuzzy diagnosis is the fact that a confidence index is associated with each fault diagnosis. Finally, a discussion is given with regard to the reliability and large application field of the suggested approach.

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On wavelet analysis of auditory evoked potentials

Abstract: 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.

Cited by 67 publications

References 52 publications

Continuous time wavelet entropy of auditory evoked potentials

Continuous time wavelet entropy of auditory evoked potentials

On the dual structure of the auditory brainstem response in dogs

Hybrid intelligent diagnosis approach based on soft computing from signal and image knowledge representations for a biomedical application

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