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

Novel characterization method of impedance cardiography signals using time-frequency distributions

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P = 0.780) and the extended modified beta distribution (P = 0.765) provided similar results, higher than the rest of analyzed kernels. Graphical abstract Flowchart for the optimization of time-frequency distribution kernels for impedance cardiography signals.

Single-trial detection for intraoperative somatosensory evoked potentials monitoring

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.

Novel aspects of spinal cord evoked potentials (SCEPs) in the evaluation of dorso-ventral and lateral mechanical impacts on the spinal cord

OBJECTIVES

The aim of the current study was to mimic mechanical impacts on the spinal cord by manifesting the effects of dorsoventral (DVMP) and lateral (LMP) mechanical pressure on neural activity to address points to be considered during surgery for different purposes, including spinal cord decompression.

APPROACHES

Spinal cords of anesthetized rats were compressed at T13. Different characteristics of axons, including vulnerability, excitability, and conduction velocity (CV), in response to promptness, severity, and duration of pressure were assessed by spinal cord evoked potentials (SCEPs). Real-time SCEPs recorded at L4-5 revealed N1, N2, and N3 peaks that were used to represent the activity of injured sensory afferents, interneurons, and MN fibers. The averaged SCEP recordings were fitted by trust-region algorithm to find the equivalent Gaussian and polynomial equations.

MAIN RESULTS

The pyramidal and extrapyramidal pathways possessed CVs of 3-11 and 16-80 m s(-1), respectively. DVMP decreased the excitability of myelinated neural fibers in antidromic and orthodromic pathways. The excitability of fibers in extrapyramidal and pyramidal pathways of lateral corticospinal (LCS) and anterior corticospinal (ACS) tracts decreased following LMP. A significant drop in the amplitude of N3 and its conduction velocity (CV) revealed higher susceptibility of less-myelinated fibers to both DVMP and LMP. The best parametric fitting model for triplet healthy spinal cord CAP was a six-term Gaussian equation (G6) that fell into a five-term equation (G5) at the complete compression stage.

SIGNIFICANCE

The spinal cord is more susceptible to dorsoventral than lateral mechanical pressures, and this should be considered in spinal cord operations. SCEPs have shown promising capabilities for evaluating the severity of SCI and thus can be applied for diagnostic or prognostic intraoperative monitoring (IOM).

The proximal thoracic curve in adolescent idiopathic scoliosis: surgical strategy and management outcomes

There is no consensus on the definition of a structural proximal thoracic curve (PTC) and the indications for fusion. As such, we assessed a single institute's experience in the management of large PTCs (>35 degrees) in patients with adolescent idiopathic scoliosis (AIS) who were either fused or not fused. A retrospective radiographic analyses of 30 consecutive AIS patients with double thoracic curves who underwent PSF with a minimum of 2 years' follow-up were included for review. The patients were divided into two groups: group 1 (n = 15 patients) with fusion extended up to T2 or T3 and group 2 (n = 15) with fusion limited to T5 or below. Shoulder balance was assessed according to clavicular angle, first-rib difference, and radiographic shoulder height difference (SHD). PTCs were defined based on a Cobb angle of >35, the presence of apical rotation, and a positive T1 tilt. The decision to fuse the PTC was based on curve magnitude only, with those between 35 and 45 degrees undergoing a selective fusion of the main thoracic curve (MTC), with both curves fused if the PTC was more than 45 degrees. In group 1, there were eight females and seven males. Their ages ranged between 12 and 33 years, with a mean of 16.2 ± 5.5 years. Postoperatively, the mean PTC correction was 45.6%, which statistically differed from preoperative status (p = 0.001). No statistical difference was noted in T1 tilt and the first-rib difference from preoperative to postoperative follow-up (p > 0.05). However, the clavicular angle and SHD were increased significantly at the immediate postoperative interval (p < 0.05) but demonstrated no significant changes between the initial and the last follow-up values (p > 0.05). Group 2 consisted of one male and 14 females. The mean age was 16.4 ± 4 years (range: 11 to 28 years). The mean spontaneous PTC correction was 28.3% and remained essentially unchanged at the end of the follow-up. The improvement in the curve from preoperative status was highly statistically significant (p = 0.001). All radiographic shoulder parameters exhibited a significant increase in the immediate postoperative period and at last follow-up, and shoulder balance improvement was not noted on follow-up. Although both groups were not statistically similar with regards to the preoperative PTC, AVR, apical vertebral translation, and shoulder parameters, no significant difference could be found in PTC or shoulder parameters between both groups at last follow-up (p > 0.05). Our study illustrates important observations that should be considered in defining the PTC for fusion consideration. Spontaneous correction of the PTC occurs in structural curves greater than 35 degrees and less than 45 degrees, and this correction is maintained over time. Despite that correction, radiographic shoulder parameters are expected to slightly increase. Nonfusion strategy may be appropriate for PTCs between 35 and 45 degrees. After fusion of both the MTC and the PTC, the radiographic shoulder parameters did not significantly differ. Preoperative radiographic shoulder parameters are not predictive of postoperative shoulder imbalance.

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

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 min of asphyxial CA (n=8 per group). The SSEPs from median nerve stimulation were recorded for 4h after CA and then for 15 min periods at 24, 48 and 72 h. Neurological outcomes were evaluated by neurologic deficit score (NDS) at 72 h 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.

Identification of detailed time-frequency components in somatosensory evoked potentials

Somatosensory evoked potential (SEP) usually contains a set of detailed temporal components measured and identified in time domain, providing meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to reveal complex and fine time-frequency features of SEP in time-frequency domain using advanced time-frequency analysis (TFA) and pattern classification methods. A high-resolution TFA algorithm, matching pursuit (MP), was proposed to decompose a SEP signal into a string of elementary waves and to provide a time-frequency feature description of the waves. After a dimension reduction by principle component analysis (PCA), a density-guided K-means clustering was followed to identify typical waves existed in SEP. Experimental results on posterior tibial nerve SEP signals of 50 normal adults showed that a series of typical waves were discovered in SEP using the proposed MP decomposition and clustering methods. The statistical properties of these SEP waves were examined and their representative waveforms were synthesized. The identified SEP waves provided a comprehensive and detailed description of time-frequency features of SEP.

Time-frequency component analysis of somatosensory evoked potentials in rats

Background

Somatosensory evoked potential (SEP) signal usually contains a set of detailed temporal components measured and identified in a time domain, giving meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to measure and identify detailed time-frequency components in normal SEP using time-frequency analysis (TFA) methods and to obtain their distribution pattern in the time-frequency domain.

Methods

This paper proposes to apply a high-resolution time-frequency analysis algorithm, the matching pursuit (MP), to extract detailed time-frequency components of SEP signals. The MP algorithm decomposes a SEP signal into a number of elementary time-frequency components and provides a time-frequency parameter description of the components. A clustering by estimation of the probability density function in parameter space is followed to identify stable SEP time-frequency components.

Results

Experimental results on cortical SEP signals of 28 mature rats show that a series of stable SEP time-frequency components can be identified using the MP decomposition algorithm. Based on the statistical properties of the component parameters, an approximated distribution of these components in time-frequency domain is suggested to describe the complex SEP response.

Conclusion

This study shows that there is a set of stable and minute time-frequency components in SEP signals, which are revealed by the MP decomposition and clustering. These stable SEP components have specific localizations in the time-frequency domain.