Real-time analysis of EEG using Walsh transforms

Weide, Bruce W.; Andrews, L.T.; Iannone, Anthony M.

doi:10.1016/0010-4825(78)90027-6

Cited by 10 publications

(4 citation statements)

References 6 publications

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“…In contrast to the results of Weide et al (3), the respective spectral peaks in the Walsh and Fourier spectral estimates were of similar amplitudes, but the Walsh spectral estimates containing machining peaks often exhibited odd-sequency harmonics of the peaks which were not present in the other spectral estimates, for example Fig. le.…”

Section: Introductioncontrasting

confidence: 87%

A Comparison of Orthogonal Transforms in Their Application to Surface Texture Analysis

Mulvaney

Newland

Gill

1986

Proceedings of the Institution of Mechanical Engineers, Part C:

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The orthogonal transforms most commonly used in engineering applications are compared in their ability to characterize surface texture data. The results suggest that, for this application, the Fourier and cosine transforms are to be preferred because of their ability to model the data more precisely and because of their relatively rapid rates of convergence. THE SUBJECTIVE PERFORMANCE OF THE ORTHOGONAL TRANSFORMSAlthough all of the machined specimens listed in Tables 1 and 2 were assessed, two specimens are selected to 116186

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Section: Introductioncontrasting

confidence: 87%

A Comparison of Orthogonal Transforms in Their Application to Surface Texture Analysis

Mulvaney

Newland

Gill

1986

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…6. Detection of the peak that coincides with the occurrence of the SSEP was automated using the Walsh transformation method (Adjouadi et al, 2004(Adjouadi et al, , 2005Smith, 1981;Weide et al, 1978) to indicate the evoked potential response.…”

Section: Structure Of the Algorithmmentioning

confidence: 99%

Peak Detection of Somatosensory Evoked Potentials Using an Integrated Principal Component Analysis–Walsh Method

Vedala

Yaylali

Cabrerizo

et al. 2012

Journal of Clinical Neurophysiology

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Clinical application of somatosensory evoked potentials (SSEP) in intraoperative neurophysiological monitoring still requires anywhere between 200 to 500 trials, which is excessive and introduces a delay during surgery. In this study, the analysis was performed on the data recorded in 20 patients undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. The first 10 trials were analyzed using an eigen decomposition technique, and a signal extraction algorithm eliminated the common components of the signals not contributing to the SSEP. A unique Walsh transform operation was then used to identify the position of the SSEP event within the clinical requirements of 10% time in latency deviation and 50% peak-to-peak amplitude deviation using only 10 trials. The algorithm also shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials.

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“…Detection of the peak that coincides with the occurrence of the SSEP was automated using the Walsh transformation method (Weide et al, 1978;Smith, 1981;Adjouadi et al, 2004Adjouadi et al, , 2005 to indicate the evoked potential response.…”

Section: Amuse Algorithmmentioning

confidence: 99%

“…A difference operator can be used but they have a serious drawback of being highly susceptible to noise, and thus a smoothing operator need to be included to improve the differentiator's signal to noise ratio (SNR). The Walsh differentiation method (Adjouadi et al, 2004(Adjouadi et al, , 2005Weide et al, 1978) was utilized to overcome the problem, as described in the following section.…”

Section: Amuse Algorithm -Noise Components Filteringmentioning

confidence: 99%

A Novel Signal Processing Method for Intraoperative Neurophysiological Monitoring in Spinal Surgeries

Vedala¹

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My mentor Dr. Malek Adjouadi has taught me a great deal in professional, personal and intellectual development. He has always had my best interest at heart and has provided me with every opportunity to grow. I am grateful for all the kindness and patience that he has shown me over the years. I would like to thank my gurus, Dr.Armando Barreto, Dr. Jean Andrian and Dr. Naphtalie Rishe, naming a few, whose trials to obtain an SSEP signal, which is excessive and introduces a significant delay to prevent potential neurological risks during surgery. The main objective of this dissertation is to develop a means to obtain the SSEP signal using a much reduced number of trials (20 trials or less) while still optimizing the effectiveness of the monitoring system. The preliminary research steps were to determine those characteristics that distinguish the SSEP with the ongoing brain activity. We first established that the brain activity is indeed quasi-stationary whereas an SSEP is expected to be identical every time a trial is recorded.A novel algorithm is subsequently developed using Chebyshev time windowing for preconditioning of SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasistationarity of EEG on 12 preconditioned trials. A unique Walsh transform operation was then used to identify the position of the SSEP event. An alarm is raised vii when there is a 10% time in latency deviation and/or 50% peak-to-peak amplitude deviation, as per the clinical requirements. The algorithm shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials.In this study, the analysis was performed on the data recorded in 29 patients who underwent surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp EEG. This method is shown empirically to be more clinically viable than present day approaches. In all 29 cases, the algorithm took on an average 4sec to extract an SSEP signal, as compared to conventional methods, which take up to several minutes.The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provided for a much improved and effective neurophysiological monitoring process.

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Real-time analysis of EEG using Walsh transforms

Cited by 10 publications

References 6 publications

A Comparison of Orthogonal Transforms in Their Application to Surface Texture Analysis

A Comparison of Orthogonal Transforms in Their Application to Surface Texture Analysis

Peak Detection of Somatosensory Evoked Potentials Using an Integrated Principal Component Analysis–Walsh Method

A Novel Signal Processing Method for Intraoperative Neurophysiological Monitoring in Spinal Surgeries

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