Comparison of time-frequency distribution techniques for analysis of spinal somatosensory evoked potential

Hu, Yong; Luk, Keith D. K.; Lu, William W.; Holmes, Arthur D.; Leong, Jo‐Ann C.

doi:10.1007/bf02345294

Cited by 14 publications

(12 citation statements)

References 17 publications

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“…The first type of abnormality gives rise to the dissociation of excitation, which lengthens the latency and lowers the amplitude of the wave. Relative characteristic changes in magnitude and latency (Kang et al, 2009; Madhok et al, 2010), conduction velocity (Salami et al, 2003) and time-frequency distributions (Hu et al, 2001; Hu et al, 2002), have all been studied in the past decades and are commonly used as SSEP measurement parameters in both laboratory experiments and clinical situations (De Giorgio et al, 1993; Robinson et al, 2003; Rossetti et al, 2010; Rothstein, 2000; Young et al, 2004). The other type of abnormality is the altered or disturbed connection induced by the alterations to the neural signals and sources that occur after ischemic injury.…”

Section: Introductionmentioning

confidence: 99%

Time jitter of somatosensory evoked potentials in recovery from hypoxic–ischemic brain injury

Valentin

et al. 2011

Journal of Neuroscience Methods

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Impaired neural conductivity shown by delayed latency and reduced amplitude of characteristic peaks in somatosensory evoked potentials (SSEPs), has been used to monitor hypoxic-ischemic brain injury after Cardiac Arrest (CA). However, rather than characteristic peak deferral and suppression, the time jitter of the peak in SSEP related with time-variant neurological abnormalities is diminished by the commonly used ensemble average method. This paper utilizes the second order blind identification (SOBI) technique to extract characteristic peak information from one trial of SSEPs. Sixteen male Wistar rats were subjected to 7 or 9 minutes of asphyxial CA (n=8 per group). The SSEPs from median nerve stimulation were recorded for 4 hours after CA and then for 15 minute periods at 24, 48 and 72 h. Neurological outcomes were evaluated by Neurologic Deficit Score (NDS) at 72h post-CA. The SSEP signal was analyzed offline with SOBI processing in Matlab. The N10 feature of SSEP was compared between good (NDS≥50) and bad (NDS<50) outcomes. After processed by SOBI, the N10 detection rate was significantly increased (p<0.001) from 90 min post-CA. Statistical difference of the latency variance of the N10 between good and bad outcome groups existed at 24, 48 and 72 h post-CA (p<=0.001). Our study is the first application using SOBI detecting variance in neural signals like SSEP. N10 latency variance, related with neurophysiological dysfunction, increased after hypoxic-ischemic injury. The SOBI technique is an efficient method in the identification of peak detection and offers a favorable alternative to reveal the neural transmission variation.

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Section: Introductionmentioning

confidence: 99%

Time jitter of somatosensory evoked potentials in recovery from hypoxic–ischemic brain injury

Valentin

et al. 2011

Journal of Neuroscience Methods

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“…2), which has been previously reported in several studies (Hu et al 2001a(Hu et al , 2002(Hu et al , 2003, while noise in other region (RONI) in the time- 5 The assessment of the minimum number of trials to detection SEP responses. All the indicative SEP parameters (CC, P37 amplitude, N45 amplitude, P37-N45 amplitude, and phase values), which were sensitive to the stimulus intensity, were used to assess the minimum number of trials that can significantly distinguish the resting EEG and SEP responses.…”

Section: Multiple Linear Regression In the Time-frequency Domainmentioning

confidence: 97%

“…The explored frequencies were ranged from 21 to 200 Hz in steps of 1 Hz in this study. Previous studies have shown that most meaningful time-frequency features of SEPs are distributed in the specified frequency range (Hu et al 2001a(Hu et al , 2002(Hu et al , 2003Zhang et al 2009). The squared magnitude of F(s, f) is called the scalogram.…”

Section: Time-frequency Domain Analysismentioning

confidence: 97%

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Single-trial detection for intraoperative somatosensory evoked potentials monitoring

Zhang

Liu

et al. 2015

Cogn Neurodyn

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Abnormalities of somatosensory evoked potentials (SEPs) provide effective evidence for impairment of the somatosensory system, so that SEPs have been widely used in both clinical diagnosis and intraoperative neurophysiological monitoring. However, due to their low signal-to-noise ratio (SNR), SEPs are generally measured using ensemble averaging across hundreds of trials, thus unavoidably producing a tardiness of SEPs to the potential damages caused by surgical maneuvers and a loss of dynamical information of cortical processing related to somatosensory inputs. Here, we aimed to enhance the SNR of single-trial SEPs using Kalman filtering and time-frequency multiple linear regression (TF-MLR) and measure their single-trial parameters, both in the time domain and in the time-frequency domain. We first showed that, Kalman filtering and TF-MLR can effectively capture the single-trial SEP responses and provide accurate estimates of single-trial SEP parameters in the time domain and time-frequency domain, respectively. Furthermore, we identified significant correlations between the stimulus intensity and a set of indicative single-trial SEP parameters, including the correlation coefficient (between each single-trial SEPs and their average), P37 amplitude, N45 amplitude, P37-N45 amplitude, and phase value (at the zero-crossing points between P37 and N45). Finally, based on each indicative single-trial SEP parameter, we investigated the minimum number of trials required on a single-trial basis to suggest the existence of SEP responses, thus providing important information for fast SEP extraction in intraoperative monitoring.

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“…To provide timevarying spectral information on SEP waveforms, various time-frequency analysis (TFA) methods have been adopted and compared [3][4][5][6][7]. The short time Fourier transform (STFT) was recommended in [5][6][7] as an appropriate method for TFA of SEP signals and it was capable of revealing stable and easily identifiable characteristics -the parameters associated with the main peak in the time-frequency distribution (TFD) obtained by STFT.…”

Section: Introductionmentioning

confidence: 99%

High-resolution time-frequency analysis of somatosensory evoked potential components by means of matching pursuit

Zhang

Chan³

et al. 2008

2008 9th International Conference on Signal Processing

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This paper proposes to apply a high-resolution time-frequency analysis (TFA) algorithm, the matching pursuit (MP), to extract and identify detail components of somatosensory evoked potential (SEP) signals. Conventional TFA methods showed limited timefrequency resolution in short-period nonstationary SEP signals so that they cannot reveal detail components in time-frequency domain. The MP algorithm can decompose a SEP signal into a number of elementary components and provide a time-frequency parameter description of decomposed components. The stable components can be revealed by statistical analysis and classification of the extracted parameters. Experimental results on cortical SEP signals of rats show that a series of stable SEP components can be identified using the MP decomposition algorithm.

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Comparison of time-frequency distribution techniques for analysis of spinal somatosensory evoked potential

Cited by 14 publications

References 17 publications

Time jitter of somatosensory evoked potentials in recovery from hypoxic–ischemic brain injury

Time jitter of somatosensory evoked potentials in recovery from hypoxic–ischemic brain injury

Single-trial detection for intraoperative somatosensory evoked potentials monitoring

High-resolution time-frequency analysis of somatosensory evoked potential components by means of matching pursuit

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