Diagnosis of New Onset Vocal Cord Paralysis Using Acoustic Analysis Yu Wei Hamid GholamHosseini Andrew Cameron Michael J Harrison Ahmed Al-Jumaily

Yu Wei

Hamid GholamHosseini

Andrew Cameron

Michael J Harrison

Ahmed Al-Jumaily

The clinical challenge

Clinical significance Hoarse, raspy voice Inability to swallow safely Inability to cough

Hoarse, raspy voice

Inability to swallow safely

Inability to cough

A variety of diagnostic solutions….

Can we apply acoustic analysis to diagnose new onset vocal cord paralysis??

Can we apply acoustic analysis to diagnose new onset vocal cord paralysis??

But hasn’t this been done before?

Hoarseness diagram A means of detecting and classifying pathological voices based on Glottal to Noise Excitation Ratio Period Perturbation Quotient Energy Perturbation Quotient Mean Waveform Matching Coefficient

A means of detecting and classifying pathological voices based on

Glottal to Noise Excitation Ratio

Period Perturbation Quotient

Energy Perturbation Quotient

Mean Waveform Matching Coefficient

So why did we think we could do any better? Not validated as a screening tool Non-specific Developed from databases of pathological voices

Not validated as a screening tool

Non-specific

Developed from databases of pathological voices

So why did we think we could do any better? Specific to one vocal cord pathology Data collected from patients before and after development of vocal cord pathology.

Specific to one vocal cord pathology

Data collected from patients before and after development of vocal cord pathology.

Methods Data collected from 33 patients. Vocal sampling pre and post-operatively. Postoperatively, patients were classified into two groups based on patient and physician perceptions hoarse Non-hoarse Data sets were interrogated to develop discriminatory algorithm

Data collected from 33 patients.

Vocal sampling pre and post-operatively.

Postoperatively, patients were classified into two groups based on patient and physician perceptions

hoarse

Non-hoarse

Data sets were interrogated to develop discriminatory algorithm

Analysis Following preliminary analysis, sustained ‘eee’ data point selected as most discriminatory. Wavelet packet analysis was performed to characterise potential discriminatory features A Support Vector Machine was used to identify characterising features of the hoarse group. Discriminatory efficacy was compared with the Hoarseness Diagram

Following preliminary analysis, sustained ‘eee’ data point selected as most discriminatory.

Wavelet packet analysis was performed to characterise potential discriminatory features

A Support Vector Machine was used to identify characterising features of the hoarse group.

Discriminatory efficacy was compared with the Hoarseness Diagram

Results – Hoarseness diagram

Results – experimental algorithm Classification accuracy Applying algorithm to post-operative signals only 82.5% Applying algorithm to pre and postoperative signals of patients who developed hoarseness 93.5%

Conclusion We have successfully identified features on acoustic analysis which are associated with recurrent laryngeal nerve palsy.

We have successfully identified features on acoustic analysis which are associated with recurrent laryngeal nerve palsy.

Limitations of this research American accents only! Algorithm tested against clinician perception – not the ‘gold standard’ test larger data sets will be required to comment on the sensitivity and specificity of our algorithm as a diagnostic test

American accents only!

Algorithm tested against clinician perception – not the ‘gold standard’ test

larger data sets will be required to comment on the sensitivity and specificity of our algorithm as a diagnostic test

Where to next?? Test our algorithm by adding further patients Test our algorithm against the ‘gold standard’ – direct visualisation of the vocal cords. Improve the quality of our data collection by having patients make highly discriminatory sounds.

Test our algorithm by adding further patients

Test our algorithm against the ‘gold standard’ – direct visualisation of the vocal cords.

Improve the quality of our data collection by having patients make highly discriminatory sounds.