Adaptive Fourier series modeling of time-varying evoked potentials: study of human somatosensory evoked response to etomidate anesthetic

Thakor, Nitish V.; Vaz, Christopher A.; McPherson, Robert W.; Hanley, Daniel F.

doi:10.1016/0168-5597(91)90148-q

Cited by 32 publications

(5 citation statements)

References 25 publications

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“…Those findings have been applied in clinic practices and support our findings. It has been shown that power spectral changes occurring in the EP can also be important indicators of injury 29. However, its main disadvantage is the loss of time information, which is another important indicator of the integrity of the EP waveform.…”

Section: Discussionmentioning

confidence: 99%

Prevention of spinal cord injury with time-frequency analysis of evoked potentials: an experimental study

Lu²,

Holmes³

et al. 2001

Journal of Neurology, Neurosurgery &Amp; Psychiatry

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Results-The TFDs of EPs were found to concentrate in a certain location under normal conditions. When injury occurred, the energy decreased in peak power, and there was a greater dispersion of energy across the time-frequency range. Strong relations were found between latency and peak time, and amplitude and peak power. However, the change in peak power after injury was significantly larger than the corresponding change in amplitude (p<0.001 by ANOVA). Conclusions-It was found that TFA of EPs provided an earlier and more sensitive indication of injury than time domain monitoring alone. It is suggested that TFA of EP signals should therefore be useful in preventing spinal cord injury during surgery. (J Neurol Neurosurg Psychiatry 2001;71:732-740) Keywords: time frequency analysis; evoked potential; spinal cord injury Spinal surgery is a common form of treatment for various spinal deficits, but also entails the risk of spinal cord damage, which may lead to sensory or motor problems, and even paralysis. To reduce this risk, various evoked potential techniques have been used to monitor the integrity of spinal cord function during surgery. However, false outcomes from monitoring are still a concern to the monitoring and surgical teams. 1 Current intraoperative monitoring techniques measure only the latency and amplitude of the evoked potential (EP) 1 2 despite the fact that EP signals are usually polyphasic waveforms that reflect diVerent activation and conduction velocities within the spinal cord. 3 Present measurements cannot therefore represent the precise characteristics of EP signals. The power spectrum of EP signals can indicate the proportion of diVerent frequency components in the signal, and may be more representative of the physical nature of the signal than the features seen in the time spectrum alone. As such, combining time and frequency analysis into time-frequency analysis (TFA) could be a useful tool in spinal cord monitoring. 4 The main advantage of this is the integrity of information from the whole of the EP waveform, rather than arbitrarily selected features.Application of TFA to EP signals to monitor the integrity of spinal cord function during spinal surgery has not yet been reported. In the present study, the changes of various EP waveforms in time-frequency space were studied after mechanical insult to the spinal cord. The purpose of this was to assess the applicability of TFA methods to EP signals, and provide a basis for the clinical use of TFA in spinal EP monitoring. Materials and methods EXPERIMENTAL PROCEDURETwenty mature rats weighing between 260 and 280 g were used. All the surgical procedures were performed under intravenous pentobarbital (0.05 mg/g) anaesthesia augmented by local 1% xylocaine infiltration. Additional pentobarbital was given at intervals and in amounts established in non-curarised rats to assure adequate anaesthesia.Many investigators use physically transected spinal cords for studying spinal cord regeneration, but this is rarely encountered in injury to the human ...

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Section: Discussionmentioning

confidence: 99%

Prevention of spinal cord injury with time-frequency analysis of evoked potentials: an experimental study

Lu²,

Holmes³

et al. 2001

Journal of Neurology, Neurosurgery &Amp; Psychiatry

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“…These techniques include conventional digital filters (Aunon and McGillem, 1975), Wiener filters (Cerutti et al, 1987), Kalman filters (Georgiadis et al, 2005), adaptive filters (Thakor et al, 1991), neural networks (Thakor et al, 1991) and model-based estimation (Davila and Mobin, 1992). Other techniques aim to filter the representative potential after the averaging process: regularization methods (Karjalainen et al, 1999;Aydin, 2008) or model-based estimation (Furst and Blau, 1991).…”

Section: State Of the Artmentioning

confidence: 99%

Averaging methods for extracting representative waveforms from motor unit action potential trains

Malanda

Navallas

Rodriguez‐Falces

et al. 2015

Journal of Electromyography and Kinesiology

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“…Several papers have been presented in the area of biomedical signal processing where an adaptive solution based on the LMS algorithm is suggested [9]- [13]. The fundamental principles of adaptive filtering for noise cancelation were described by Widrow et al [1].…”

Section: Introductionmentioning

confidence: 99%

Adaptive noise removal in the ECG using the Block LMS algorithm

Rahman¹,

Ahamed

Reddy

2009

2009 2nd International Conference on Adaptive Science &Amp; Technology (ICAST)

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The electrocardiogram (ECG) is the most commonly used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG signals are corrupted by artifacts. So the noise removal is a classical problem in ECG records, that generally produces artifactual data when measuring the ECG parameters. The Block LMS (BLMS) algorithm, being the solution of the steepest descent strategy for minimizing the mean squared error in a complete signal occurrence, is shown to be steady-state unbiased and with a lower variance than the LMS algorithm. In this paper, we present a BLMS algorithm for removing artifacts preserving the low frequency components and tiny features of the ECG. Finally, we have applied this algorithm on ECG signals from the MIT-BIH data base and compared its performance with the conventional LMS algorithm. The results show that the performance of the BLMS algorithm is superior than the LMS algorithm.

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Adaptive Fourier series modeling of time-varying evoked potentials: study of human somatosensory evoked response to etomidate anesthetic

Cited by 32 publications

References 25 publications

Prevention of spinal cord injury with time-frequency analysis of evoked potentials: an experimental study

Prevention of spinal cord injury with time-frequency analysis of evoked potentials: an experimental study

Averaging methods for extracting representative waveforms from motor unit action potential trains

Adaptive noise removal in the ECG using the Block LMS algorithm

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