Latency change estimation for evoked potentials: A comparison of algorithms

Kong, Xuan; Oiu, T

doi:10.1007/bf02344806

Cited by 15 publications

(9 citation statements)

References 34 publications

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“…NC-Stat is noninvasive and requires Ͻ6 min to set up and use (16 -21). Detailed neurological assessments using these techniques have been reported in other studies of peripheral neuropathy (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26).…”

Section: Methodsmentioning

confidence: 99%

Phase I Clinical and Pharmacokinetic Study of BMS-247550, a Novel Derivative of Epothilone B, in Solid Tumors

Mani

McDaid

Hamilton

et al. 2004

Clinical Cancer Research

146

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Purpose: The purpose of this study was to determine the maximum tolerated dose, toxicity, and pharmacokinetics of BMS-247550 administered as a 1-h i.v. infusion every 3 weeks.Experimental Design: Patients with advanced solid malignancies were premedicated and treated with escalating doses of BMS-247550. Blood sampling was performed to characterize the pharmacodynamics and pharmacokinetics of BMS-247550.Results: Twenty-five patients were treated at six dose levels ranging from 7.4 to 59.2 mg/m 2 . At 50 mg/m 2 , 4 of 9 patients (44.4%) had dose-limiting toxicity (neutropenia, abdominal pain/nausea). At 40 mg/m 2 (the recommended Phase II dose), 2 of 12 patients (16.7%) had dose-limiting neutropenia. Overall, the most common nonhematological toxicity was fatigue/generalized weakness (grade 3-4 seen in 9.0% of patients), followed by neurosensory deficits manifested as peripheral neuropathy and by gastrointestinal discomfort. At 40 mg/m 2 , the incidence of grade 3 fatigue, abdominal pain, diarrhea, and neuropathy was 7.7%. Grade 1-2 neuropathy was observed in all patients enrolled and treated at 40 mg/m 2 . Two patients with paclitaxel-refractory ovarian cancer, one patient with taxane-naïve breast cancer, and another patient with docetaxel-refractory breast cancer had objective partial responses (lasting 6.0, 5.3, 3.0, and 4.5 months, respectively)

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Section: Methodsmentioning

confidence: 99%

Phase I Clinical and Pharmacokinetic Study of BMS-247550, a Novel Derivative of Epothilone B, in Solid Tumors

Mani

McDaid

Hamilton

et al. 2004

Clinical Cancer Research

146

View full text Add to dashboard Cite

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“…Earlier workers have proposed methods like parametric modeling [6–8], adaptive filtering [9–11], and time frequency analysis to obtain SSEP with minimum trials [6, 10, 12, 13]. Nonetheless, these methods still pose different constraints and contend with different limitations in extracting the SSEP accurately, consistently, and with the appropriate morphology.…”

Section: Introductionmentioning

confidence: 99%

Quasi-Stationarity of EEG for Intraoperative Monitoring during Spinal Surgeries

Vedala¹,

Motahari²,

Goryawala³

et al. 2014

The Scientific World Journal

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We present a study and application of quasi-stationarity of electroencephalogram for intraoperative neurophysiological monitoring (IONM) and an application of Chebyshev time windowing for preconditioning 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 quasi-stationarity of EEG on 12 preconditioned trials. This method is shown empirically to be more clinically viable than present day approaches. In all twelve cases, the algorithm takes 4 sec to extract an SSEP signal, as compared to conventional methods, which take 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 provide a much improved and effective neurophysiological monitoring process.

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“…Another approach involved the use of amplitude modulated stimulus and performing steady-state analysis on the recorded signals (Noss et al, 1996). The latency, that is, the time difference between the successive trials, was also exploited for noise removal (Kong and Oiu, 2001). Recent advances in functional source separation of SSEP signals (Porcaro et al, 2009) provide better insight into the EEG signal characteristics that can be used for SSEP signal detection.…”

mentioning

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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Latency change estimation for evoked potentials: A comparison of algorithms

Cited by 15 publications

References 34 publications

Phase I Clinical and Pharmacokinetic Study of BMS-247550, a Novel Derivative of Epothilone B, in Solid Tumors

Phase I Clinical and Pharmacokinetic Study of BMS-247550, a Novel Derivative of Epothilone B, in Solid Tumors

Quasi-Stationarity of EEG for Intraoperative Monitoring during Spinal Surgeries

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

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