[2407.17844] Innovative Speech-Based Deep Learning Approaches for Parkinson’s Disease Classification: A Systematic Review

View a PDF of the paper titled Progressive Speech-Based mostly Deep Studying Approaches for Parkinson’s Illness Classification: A Systematic Overview, by Lisanne van Gelderen and 1 different authors

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Summary:Parkinson’s illness (PD), the second most prevalent neurodegenerative dysfunction worldwide, incessantly presents with early-stage speech impairments. Current developments in Synthetic Intelligence (AI), significantly deep studying (DL), have considerably enhanced PD prognosis by means of the evaluation of speech information. However, the progress of analysis is restricted by the restricted availability of publicly accessible speech-based PD datasets, primarily as a consequence of privateness issues. The aim of this systematic evaluate is to discover the present panorama of speech-based DL approaches for PD classification, based mostly on 33 scientific works printed between January 2020 and March 2024. We talk about their accessible sources, capabilities, and potential limitations, and points associated to bias, explainability, and privateness. Moreover, this evaluate offers an summary of publicly accessible speech-based datasets and open-source materials for PD. The DL approaches recognized are categorized into end-to-end (E2E) studying, switch studying (TL), and deep acoustic function extraction (DAFE). Amongst E2E approaches, Convolutional Neural Networks (CNNs) are prevalent, although Transformers are more and more widespread. E2E approaches face challenges reminiscent of restricted information and computational sources, particularly with Transformers. TL addresses these points by offering extra sturdy PD prognosis and higher generalizability throughout languages. DAFE goals to enhance the explainability and interpretability of outcomes by analyzing the precise results of deep options on each different DL approaches and extra conventional machine studying (ML) strategies. Nonetheless, it typically underperforms in comparison with E2E and TL approaches.