Multichannel wavelet-type decomposition of evoked potentials: model-based recognition of generator activity

Time-frequency analysis of somatosensory evoked potentials for intraoperative spinal cord monitoring

PURPOSE

To evaluate the potential use of time-frequency analysis and its reliability in intraoperative somatosensory evoked potential (SEP) monitoring.

METHODS

One hundred ninety-one patients undergoing thoracic and/or lumbar spinal surgery were studied retrospectively. The SEP signals were recorded during different stages of surgery. Averaged SEP was analyzed by short-time Fourier transform. The main peak in the time-frequency interpretation of SEP was measured in peak power, peak time, and peak frequency. The variability of these parameters was compared with that of amplitude and latency during different stages of surgery. The reliability of these parameters was also compared in true-positive and false-positive cases.

RESULTS

During different surgical stages for the posterior tibial nerve SEP, the intrasubject variability of peak power was found to be more stable than that of amplitude, while the intrasubject variability of peak time did not show any difference compared with that of latency. The peak frequency presented stable during surgery. Moreover, the true-positive SEP case showed that peak power may detect the potential injury earlier than amplitude does. The false-positive outcomes could be reduced by the proposed method.

CONCLUSIONS

The SEP peak component was found stable and reliable during the different stages of surgery. For clinical application purpose, time-frequency analysis was suggested to be an additional monitoring method besides the conventional amplitude/latency measurement since it provided a more reproducible and prompt response to the potential injury in intraoperative SEP monitoring.

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.

A novel shape analysis technique for somatosensory evoked potentials

Somatosensory evoked potentials (SEP) have been shown to be an important electrophysiological measure to assess the integrity of the spinal cord. However the peaks in the SEP waveform are often undetectable due to low signal-to-noise (SNR) ratio. Sometimes they also become indistinct during injury when the SEP flattens. Hence time-domain analysis methods are often subject to errors, and need human-expert intervention. In this paper, we propose a new technique for analyzing the shape of the evoked potentials, in which slope information is obtained for the entire signal in specific time bins. Apart from solving the problems associated with present methods, this technique has an added advantage of analyzing the SEP signal as a whole rather than simply a few peaks. The efficacy of this technique was investigated on SEP signals recorded from 12 rats before and after contusion spinal cord injury at thoracic vertebra T8. The statistical analysis results revealed significant effect of injury to hindlimbs, whereas almost none to forelimbs. Thus, the results show high potential of this technique to differentiate between normal and injured spinal cord.

Time-frequency feature of intraoperative somatosensory evoked potential signals

The purpose of this paper is to investigate the time-frequency analysis of somatosensory evoked potentials (SEP) and its potential application to intraoperative spinal cord monitoring. In this study, SEP signals were recorded from 97 adolescent patients undergoing surgical correction of idiopathic scoliosis. The time-frequency distributions of SEP were observed during surgery by analysing averaged SEP signals with Short Time Fourier Transform (STFT). Main peak in time-frequency interpretation of SEP was measured in peak time, peak frequency and peak power. The changes of these parameters were compared with the changes of latency and amplitude. The results showed that changes in peak times and peak powers were found to correlate to the changes of latency and amplitude, respectively. However, the peak time showed more variability than the latency (p<0.01), while the peak power showed significantly lower variability than the amplitude (p<0.01). The peak frequency of SEP seems to be unchanged during surgery. Time-frequency analysis of SEP waveform gives stable and easily identifiable characteristics, and also shows higher specificity than amplitude and latency. Applying time-frequency analysis to SEP may improve the reliability of intraoperative spinal cord monitoring.

Application of time-frequency analysis to somatosensory evoked potential for intraoperative spinal cord monitoring

OBJECTIVE

To investigate the improvement in the reliability of intraoperative spinal cord monitoring by applying time-frequency analysis to somatosensory evoked potentials (SEP).

METHODS

34 patients undergoing scoliosis surgery were studied. SEP were recorded during different stages of scoliosis surgery. Averaged SEP signals were analysed intraoperatively by short time Fourier transform (STFT). The time-frequency characteristics of SEP were observed during surgery. The main peak in the time-frequency interpretation of SEP was measured in peak time, peak frequency, and peak power. The changes in these variables were compared with the changes in latency and amplitude during different surgical stages.

RESULTS

During different surgical stages, changes in peak times and peak powers were found to correlate with the changes in latency and amplitude, respectively. Peak time showed more variability than latency (p < 0.01), while peak power showed less variability than amplitude (p < 0.01). The peak frequency of SEP appeared to be unchanged during surgery. SEP signals were found to have specific time-frequency characteristics, with the time-frequency distribution of the signals being located in a particular time-frequency space.

CONCLUSIONS

Time-frequency analysis of SEP waveforms reveals stable and easily identifiable characteristics. Peak power is recommended as a more reliable monitoring parameter than amplitude, while peak time monitoring was not superior to latency measurement. Applying time-frequency analysis to SEP can improve the reliability of intraoperative spinal cord monitoring.

Latency change estimation for evoked potentials: a comparison of algorithms

Evoked potentials (EPs) have been widely used to quantify neurological system properties. Changes in EP latency may indicate impending neurological dysfunctions. This paper provides a review and a performance comparison of three classes of latency change estimation algorithms: correlation based, adaptive least mean square (LMS) based, and p-norm based algorithms. Data analysis based on computer simulated data and data from impact acceleration and hypoxia experiments were conducted. This concluded that correlation-based algorithms should only be used when the expected latency change is fixed and the noises are not correlated. While all adaptive LMS type algorithms were capable of tracking and estimating latency changes (fixed or variable) under Gaussian noise conditions, the direct LMS-based algorithm reduced the estimation error power given by traditional filter type LMS algorithms as much as 93%. When periodic interference was present, the frequency selective LMS algorithms outperformed other LMS-based algorithms and reduced of the estimation error power by 89%. Alpha-stable noise processes are better approximations of noises found in various EP analysis applications and adaptive p-norm based algorithms are found to be very robust under such noise conditions, eliminate erroneous abrupt latency changes other algorithms would have produced.

Feature extraction and state identification in biomedical signals using hierarchical fuzzy clustering

Many problems in the field of biomedical signal processing can be reduced to a task of state recognition and event prediction. Examples can be found in tachycardia detection from ECG signals, epileptic seizure or psychotic attack prediction from an EEG signal, and prediction of vehicle drivers falling asleep from both signals. The problem generally treats a set of ordered measurements and asks for the recognition of some patterns of observed elements that will forecast an event or a transition between two different states of the biological system. It is proposed to apply clustering methods to grouping discontinuous related temporal patterns of a continuously sampled measurement. The vague switches from one stationary state to another are naturally treated by means of fuzzy clustering. In such cases, an adaptive selection of the number of clusters (the number of underlying semi-stationary processes) can overcome the general non-stationary nature of biomedical signals and enable the formation of a warning cluster. The algorithm suggested for the clustering is a new recursive algorithm for hierarchical fuzzy partitioning. Each pattern can have a non-zero membership in more than one data subset in the hierarchy. A 'natural' and feasible solution to the cluster validity problem is suggested by combining hierarchical and fuzzy concepts. The algorithm is shown to be effective for a variety of data sets with a wide dynamic range of both covariance matrices and number of members in each class. The new method is applied to state recognition during recovery from exercise using the heart rate signal and to the forecasting of generalised epileptic seizures from the EEG signal.