Reconhecimento de padrões aplicados à identificação de patologias de laringe

Abstract

Diseases that affect the larynx have been considerably increased in recent years due to the condition of nowadays society where there have been unhealthy habits like smoking, alcohol and tobacco and an increased vocal abuse, perhaps due to the increase in noise pollution, especially in large urban cities. Currently the exam performed by per-oral endoscopy (aimed to identify laryngeal pathologies) have been videolaryngoscopy and videostroboscopy, both invasive and often uncomfortable to the patient. Seeking to improve the comfort of the patients who need to undergo through these procedures, this study aims to identify acoustic patterns that can be applied to the identification of laryngeal pathologies in order to creating a new non-invasive larynx assessment method. Here two different configurations of neural networks were used. The first one was generated from 524.287 combinations of 19 acoustic measurements to classify voices into normal or from a diseased larynx, and achieved an max accuracy of 99.5% (96.99±2.08%). Using 3 and 6 rotated measurements (obtained from the principal components analysis method), the accuracy was 93.98±0.24% and 94.07±0.29%, respectively. With 6 rotated measurements from a previouly standardization of the 19 acoustic measurements, the accuracy was 97.88±1.53%. The second one, to classify 23 different voice types (including normal voices), showed better accuracy in identifying hiperfunctioned larynxes and normal voices, with 58.23±18.98% and 52.15±18.31%, respectively. The worst accuracy was obtained from vocal fatigues, with 0.57±1.99%. Excluding normal voices of the analysis, hyperfunctioned voices remained the most easily identifiable (with an accuracy of 57.3±19.55%) followed by anterior-posterior constriction (with 18.14±11.45%), and the most difficult condition to be identified remained vocal fatigue (with 0.7±2.14%). Re-sampling the neural networks input vectors, it was obtained accuracies of 25.88±10.15%, 21.47±7.58%, and 18.44±6.57% from such networks with 20, 30, and 40 hidden layer neurons, respectively. For comparison, classification using support vector machine produced an accuracy of 67±6.2%. Thus, it was shown that the acoustic measurements need to be improved to achieve better results of classification among the studied laryngeal pathologies. Even so, it was found that is possible to discriminate normal from dysphonic speakers.