Vocal cord movement: can it be accurately graded?

INTRODUCTION

Flexible nasendoscopy (FNE) is the principal assessment method for vocal cord movement. Because the procedure is inherently subjective it may not be possible for clinicians to grade the degree of vocal cord movement reliably. The aim of this study was to assess the accuracy and consistency of grading vocal cord movement as viewed via FNE.

METHODS

Thirty FNE videos, without sound or clinical information, were assessed by six consultant head and neck surgeons. The surgeons were asked to assess and grade right and left vocal cord movement independently, based on a five-category scale. This process was repeated three times on separate occasions. Agreement and reliability were assessed.

RESULTS

Mean overall observed inter-rater agreement was 67.7% (sd 1.9) with the five-category scale, increasing to 91.4% (sd 1.9) when a three-category scale was derived. Mean overall observed intra-rater agreement was 78.3% (sd 9.7) for five categories, increasing to 93.1% (sd 3.3) for three categories. Discriminating vocal cord motion was less reliable using the five-category scale (k = 0.52) than with the three-category scale (k = 0.68).

CONCLUSIONS

This study demonstrates quantitatively that it is challenging to accurately and consistently grade subtle differences in vocal cord movement, as proven by the reduced agreement and reliability when using a five-point scale instead of a three-point scale. The study highlights the need for an objective measure to help in the assessment of vocal cord movement.

Evaluation of Different Signal Processing Methods in Time and Frequency Domain for Brain-Computer Interface Applications

Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one's movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such real-time BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.