Biomedical subjects

Benny S C Lam

Publications and source records attributed to Benny S C Lam.

3 recordsLinked to original sources

PubMed2005

Adaptive signal enhancement of somatosensory evoked potential for spinal cord compression detection: an experimental study.

The objective of this study was to assess the efficacy of adaptive signal enhancement (ASE) as a means of indicating intraoperative spinal cord impingement. ASE technique was used to determine the changes in the somatosensory evoked potential (SEP) elicited from eighteen rats with varying levels of spinal cord compression. ASE technique was found to be able to effectively extract SEP signals for the detection of spinal cord injury. Furthermore, while the traditional ensemble averaging (EA) technique requires more than 500 trials for meaningful signal processing in severe noisy SEP recordings, the ASE method required only 50 trials to provide similar information. Because of its fast and reliable SEP detection, the ASE method is ideal for spinal cord monitoring in the clinical setting.

Algorithms↗

PubMed2005

Multi-adaptive filtering technique for surface somatosensory evoked potentials processing.

Somatosensory evoked potential (SEP) testing has been widely applied to diagnosis of various neurological disorders. However, SEP recorded using surface electrodes is buried in noises, which makes the signal-to-noise ratio (SNR) very poor. Conventional averaging method usually requires up to thousands of raw SEP input trials to increase the SNR so that an identifiable waveform can be produced for latency and amplitude measurement. In this study, a multi-adaptive filtering (MAF) technique, emerging from the combination of well-developed adaptive noise canceller and adaptive signal enhancer, is introduced for fast and accurate surface SEP extraction. The MAF technique first processes the raw surface recorded SEP by the Canceller with a reference noise channel of background noise for adaptive subtraction before entering the Enhancer. The MAF was verified by filtering simulated SEP signals in which electroencephalography and Gaussian noise of different SNRs were added. It was found that the MAF could effectively suppress the noise and enhance the SEP components such that the SNR of the SEP is improved. Results showed that MAF with 50 input trials could provide similar performance in SEP detection to those extracted by the conventional averaging method with 1000 trials even at an SNR of -20 dB.

Algorithms↗

PubMedDate not supplied

Validation of an adaptive signal enhancer in intraoperative somatosensory evoked potentials monitoring.

The conventional approach of ensemble averaging in intraoperative somatosensory evoked potentials (SEP) monitoring requires more than 500 trials to extract a reliable waveform for neurologic diagnosis. Previous studies showed that an adaptive signal enhancer (ASE) could increase the signal-to-noise ratio of input signals. This study assessed the accuracy and efficiency of the ASE in the extraction of neurologic normal human and abnormal rat SEP. Cortical and subcortical SEP were taken from 16 subjects undergoing scoliosis surgery. SEP extracted by ASE were compared with those obtained with 500-trial averaging in terms of peak latency, amplitude, and waveforms using correlation coefficients. An animal study composed of 18 rats was used to test the ASE in detecting abnormal SEP changes due to spinal cord compression. The results demonstrate the accuracy of ASE by showing very high correlations between ASE-processed SEP and ensemble averaging-processed SEP in waveforms, peak latencies, and amplitudes. The results also show the efficiency of the ASE in extracting SEP waveforms from 50 input trials, which provided waveforms of sufficiently high quality and latency/amplitude measurements equivalent to those obtained in 500 trials of conventional ensemble averaging. Because of its fast extraction ability, adaptive signal enhancement could be an appropriate alternative to conventional ensemble averaging in intraoperative spinal cord monitoring.

Adolescent↗