Design of a dual-route acoustic-feature and EfficientNet-B0 spectrogram-based voice-screening device for Parkinson's disease assessment.

Authors

Wenxiang Zhou, Ziwei Yu, Shujuan Luo

Published in

Biomedizinische Technik. Biomedical engineering. Jul 27, 2026. Epub Jul 27, 2026.

Abstract

Parkinson's disease (PD) can affect vocal production before severe motor symptoms become apparent. This study presents a device-oriented dual-route voice-screening framework that combines interpretable acoustic descriptors with spectrogram-based transfer learning for PD assessment support.
Publicly available sustained-vowel /a/ recordings from the Parkinson Speech Dataset with Multiple Types of Sound Recordings were used for algorithmic evaluation. The proposed handheld device, dashboard, and connectivity workflow were treated as a conceptual deployment scenario. The acoustic route generated an integrated acoustic-feature vector and grouped representation-level summaries of selected descriptor families. The spectrogram route generated RGB color-mapped and grayscale time-frequency representations, which were processed using an EfficientNet-B0 transfer-learning convolutional neural network.
Among conventional acoustic-feature classifiers, the Ensemble Boosting Classifier achieved the highest AUC point estimate for the integrated acoustic-feature route. Grouped representation-level evidence showed weak-to-moderate discrimination for acoustic descriptor families. RGB and grayscale spectrograms processed through the EfficientNet-B0 route produced reported AUC point estimates of 0.93 and 0.89, respectively, within the available public-dataset evaluation.
The proposed framework combines interpretable acoustic descriptors and spectrogram-based transfer learning within a device-oriented screening-support workflow. Integrated acoustic features support interpretable reporting, while spectrogram-based transfer learning provides higher representation-level discrimination. Future validation with physical prototypes, standardized acquisition protocols, and independent clinical cohorts is required before clinical deployment.

PMID:
42497021
Bibliographic data and abstract were imported from PubMed on 25 Jul 2026.

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