Deriving reference values for nerve conduction studies from existing data using mixture model clustering

Reference values for nerve conduction studies of the peroneal, tibial, and sural nerve derived from a large population-based cohort: Associations with demographic and anthropometric characteristics-The Maastricht study

INTRODUCTION/AIMS

Nerve conduction studies (NCSs) are widely used to support the clinical diagnosis of neuromuscular disorders. The aims of this study were to obtain reference values for peroneal, tibial, and sural NCSs and to examine the associations with demographic and anthropometric factors.

METHODS

In 5099 participants (aged 40-79 years) without type 2 diabetes of The Maastricht Study, NCSs of peroneal, tibial, and sural nerves were performed. Values for compound muscle action potential (CMAP) and sensory nerve action potential amplitude, nerve conduction velocity (NCV), and distal latency were acquired. The association of age, sex, body mass index (BMI), and height with NCS values was determined using uni- and multivariate linear regression analyses.

RESULTS

Detailed reference values are reported per decade for men and women. Significantly lower NCVs and longer distal latencies were observed in all nerves in older and taller individuals as well as in men. In these groups, amplitudes of the tibial and sural nerves were significantly lower, whereas a lower peroneal nerve CMAP was only significantly associated with age. BMI showed a multidirectional association. After correction for anthropometric factors in the multivariate analysis, the association between sex and NCS values was less straightforward.

DISCUSSION

These values from a population-based dataset could be used as a reference for generating normative values. Our findings show the association of NCS values with anthropometric factors. In clinical practice, these factors can be considered when interpreting NCS values.

Harnessing historical data to derive reference limits - A comparison of e-norms to traditionally derived reference limits

Objective

Nerve conduction studies (NCS) require valid reference limits for meaningful interpretation. We aimed to further develop the extrapolated norms (e-norms) method for obtaining NCS reference limits from historical laboratory datasets for children and adults, and to validate it against traditionally derived reference limits.

Methods

We compared reference limits obtained by applying a further developed e-norms with reference limits from healthy controls for the age strata's 9-18, 20-44 and 45-60 years old. The control data consisted of 65 healthy children and 578 healthy adults, matched with 1294 and 5628 patients respectively. Five commonly investigated nerves were chosen: The tibial and peroneal motor nerves (amplitudes, conduction velocities, F-waves), and the sural, superficial peroneal and medial plantar sensory nerves (amplitudes, conduction velocities). The datasets were matched by hospital to ensure identical equipment and protocols. The e-norms method was adapted, and reference limit calculation using both ±2 SD (original method) and ±2.5 SD (to compensate for predicted underestimation of population SD by the e-norms method) was compared to control data using ±2 SD. Percentage agreement between e-norms and the traditional method was calculated.

Results

On average, the e-norms method (mean ±2 SD) produced slightly stricter reference limits compared to the traditional method. Increasing the e-norms range to mean ±2.5 SD improved the results in children while slightly overcorrecting in the adult group. The average agreement between the two methods was 95 % (±2 SD) and 96 % (±2.5 SD).

Conclusions

The e-norms method yielded slightly stricter reference limits overall than ones obtained through traditional methods; However, much of the difference can be attributed to a few outlying plots where the raters found it difficult to apply e-norms correctly. The two methods disagreed on classification of 4-5% of cases. Our e-norms software is suited to analyze large amounts of raw NCS data; it should further reduce bias and facilitate more accurate ratings.

Significance

With small adaptations, the e-norms method adequately replicates traditionally derived reference limits, and is a viable method to produce reference limits from historical datasets.

Test-Retest Reliability of Repetitive Ocular Vestibular Evoked Myogenic Potentials in Myasthenia Gravis Patients and Healthy Control Subjects

PURPOSE

Repetitive ocular vestibular evoked myogenic potentials (ROVEMP) are a novel diagnostic test to quantify neuromuscular transmission deficits in extraocular muscles in myasthenia gravis. We aimed to investigate the test-retest reliability of the ROVEMP and the effect of amplitude and age.

METHODS

We performed the ROVEMP test twice in 19 patients with myasthenia gravis (52.7 ± 19.8 years) and in 15 healthy control subjects (46.5 ± 16 years). The Bland-Altman level of agreement was determined. The relationship between test-retest reliability and signal quality, participant age and signal amplitude was studied.

RESULTS

Limits of agreement were from -179.9 to 139.3 in myasthenia gravis patients and from -56.9 to 89.5 in healthy control subjects. Difference between measurements correlated with signal amplitude ( r = -0.50, P < 0.001). Combining the primary cohort with previously published data from 114 subjects, we found a significant negative correlation between age and reference amplitude ( r = -0.163, P = 0.045).

CONCLUSIONS

This study shows that in our hands, the test-retest reliability of the ROVEMP is not optimal. Measurements with higher reference amplitude had a better quality, higher reproducibility, and increased diagnostic yield. We caution against the use of ROVEMP measurements of lower amplitude in clinical practice. In addition, given the correlation between age and amplitude, age matching of healthy control subjects and patients is essential in future studies.

EEG-based driver states discrimination by noise fraction analysis and novel clustering algorithm

Driver states are reported as one of the principal factors in driving safety. Distinguishing the driving driver state based on the artifact-free electroencephalogram (EEG) signal is an effective means, but redundant information and noise will inevitably reduce the signal-to-noise ratio of the EEG signal. This study proposes a method to automatically remove electrooculography (EOG) artifacts by noise fraction analysis. Specifically, multi-channel EEG recordings are collected after the driver experiences a long time driving and after a certain period of rest respectively. Noise fraction analysis is then applied to remove EOG artifacts by separating the multichannel EEG into components by optimizing the signal-to-noise quotient. The representation of data characteristics of the EEG after denoising is found in the Fisher ratio space. Additionally, a novel clustering algorithm is designed to identify denoising EEG by combining cluster ensemble and probability mixture model (CEPM). The EEG mapping plot is used to illustrate the effectiveness and efficiency of noise fraction analysis on the denoising of EEG signals. Adjusted rand index (ARI) and accuracy (ACC) are used to demonstrate clustering performance and precision. The results showed that the noise artifacts in the EEG were removed and the clustering accuracy of all participants was above 90%, resulting in a high driver fatigue recognition rate.