Electroglottography (EGG) is a technology developed for measuring the vocal fold contact area during human voice production. Although considered subjective and unreliable as a sole diagnostic method, with the correct application of relevant computational methods, it can constitute a most promising non-invasive voice disorder diagnostic tools in a form of a digital vocal tract pathology classifier. The aim of the following study is to gather and evaluate currently existing digital voice quality assessment systems and vocal tract abnormality classification systems that rely on the use of electroglottographic bio-impedance signals. To fully comprehend the findings of this review, first the subject of EGG is introduced. For that, we summarise most relevant existing research on EGG with a particular focus on its application in diagnostics. Then, we move on to the focal point of this work, which is describing and comparing the existing EGG-based digital voice pathology classification systems. With the application of PRISMA model, 13 articles were chosen and analysed in detail. Direct comparison between chosen studies brought us to pivotal conclusions, which have been described in Section 5 of this report. Meanwhile, certain limitations arising from the literature were identified, such as questionable understanding of the nature of EGG bio-impedance signals. The appropriate recommendations for future work were made, including the application of different methods for EGG feature extraction, as well as the need for continuous EGG datasets development containing signals gathered in various conditions and with different equipments.
Keywords: Bio-impedance; CQ; Closed quotient; Deep learning; EGG signal classification; Electroglottography; Statistical classifier; Voice pathology classification; Voice pathology detection.
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